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Development of Markerless Motion Capture Methods to Measure Risk Factors for ACL Injury in Female Athletes

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

By Evan Robert Kohler, B.S. Graduate Program in Mechanical Engineering

The Ohio State University 2012

Master's Examination Committee: Ajit M. W. Chaudhari, Advisor Alison L. Sheets

Copyright by Evan Robert Kohler 2012

Abstract

Marker-based motion capture is currently the most commonly used method in biomechanics research, and more specifically, ACL injury research. Use of commercial marker-based systems has led to the recent development of pre-screening programs aimed at determining which athletes have landing kinematics that predispose them to ACL injury. These marker-based systems, however, are strongly affected by soft tissue movement, can cause unnatural movement of the subject, and require additional time and an expert to place the markers. As a result, markerless motion capture methods have recently been developed that only require the use of multiple video cameras. These systems have the potential to capture data during game and practice situations, which would provide researchers the opportunity to examine how subjects move during sportspecific situations. To determine if markerless motion capture has the potential to be used in ACL injury research, the purpose of this study was to measure knee movements during dynamic activities that have been shown to predict ACL injury risk in high schoolaged females. To complete this analysis, we collected data from nine healthy females as they completed multiple drop-vertical jumps and drop-to-cut movements using both markerless and marker-based motion capture systems. Based on the data collected by each system, subject-specific models were created to determine the lower body joint ii

center positions and knee angles for the entire stance phase of each trial. The root mean squared (rms) differences of the markerless and marker-based lower body joint center position results were calculated for each trial to determine the relative accuracy of the markerless motion capture system. The joint center position average rms differences for all subjects ranged from 16 mm to 28 mm and the average knee flexion/extension and abduction/adduction angle differences were between 2˚ and 4˚ throughout the drop-vertical jump and drop-to-cut trials. The two main causes for the differences between the two systems were soft tissue movement affecting the marker-based results, and model tracking errors that affected the markerless measurements. Other sources of error that likely affected the marker-based results included marker reconstruction and marker placement. Additionally, sources of error that likely affected the markerless results included errors in visual hull creation and model generation. Despite recognizing these sources of error, we could not quantify each source’s contribution to the overall difference in this study. This study is the first to compare markerless and marker-based results by having the subjects perform dynamic movements other than walking. These results suggest that markerless motion capture can be used in ACL injury risk studies, although researchers would be advised to perform further testing to ensure the accuracy of the markerless system is appropriate for their application. This further testing could include comparisons to a more accurate gold standard for measuring the motion of the bones under the skin, such as inserting bone pins through the skin or using medical imaging techniques like dual plane fluoroscopy. iii

Dedication

To my parents, Dave and Lisa. Thank you.

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Acknowledgments

I would first like to thank Dr. Alison Sheets for bringing me on as a member of your laboratory. I really appreciate all of the countless hours that you have spent to help me learn and to guide me through my project. I am very grateful for your help from start to finish. Next, I would like to thank Dr. Ajit Chaudhari for his assistance throughout the project. Your ideas and expertise were very helpful every step of the way. Thank you to Tim Scalley and Becky Lathrop for your help with the data collection. Thank you Joe Ewing, Ryan Lai, and the members of the OSU Sports Biomechanics Lab (Greg Freisinger, Steve Jamison, and Mike McNally) for your help while I was learning how to use the markerless and marker-based systems. Finally, thank you to my family and friends for your support and patience as I worked on this project. Specifically, thank you to my parents, Dave and Lisa, who helped me get to this point in my life and have always been very supportive of my goals.

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Vita

June 2006 .......................................................Perrysburg High School June 2011 .......................................................B.S. Mechanical Engineering, The Ohio State University September 2011 to March 2012 .....................Graduate Teaching Assistant, Department of Mechanical Engineering, The Ohio State University March 2012 to present ...................................Graduate Research Assistant, Department of Mechanical Engineering, The Ohio State University

Fields of Study

Major Field: Mechanical Engineering

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Table of Contents Abstract ............................................................................................................................... ii Dedication .......................................................................................................................... iv Acknowledgments............................................................................................................... v Vita..................................................................................................................................... vi List of Tables ................................................................................................................... viii List of Figures .................................................................................................................... ix Chapter 1: Introduction ....................................................................................................... 1 Chapter 2: Methods ........................................................................................................... 16 Chapter 3: Results ............................................................................................................. 36 Chapter 4: Discussion ....................................................................................................... 44 Chapter 5: Conclusion....................................................................................................... 65 References ......................................................................................................................... 71

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List of Tables

Table 3.1: Joint center difference for all trials. ................................................................. 36 Table 3.2: Knee angle differences for all trials. ................................................................ 41

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List of Figures

Figure 2.1: Markerless motion capture camera. Ten of these cameras were placed around the capture volume. ........................................................................................................... 17 Figure 2.2: Marker-based motion capture camera. Eight of these were placed around the capture volume. ................................................................................................................. 17 Figure 2.3: Registration object used to set a global reference frame for both systems. Reflective markers that both systems could record were placed on top of the orange balls. The orange balls were not used in the calibration procedure............................................ 18 Figure 2.4: Force plate set-up. The plates were placed side by side so one foot could land on each force plate during the jumps. ............................................................................... 19 Figure 2.5: Reflective markers (81 total) were placed on each subject to record their motion with the marker-based cameras. ........................................................................... 21 Figure 2.6: Drop-vertical jump movement that the subjects performed. .......................... 22 Figure 2.7: Drop-to-cut movement that the subjects performed. ..................................... 23 Figure 2.8: Synchronized images from multiple cameras are used to create the visual hulls. .................................................................................................................................. 25 Figure 2.9: Positions of the ten cameras surrounding the capture volume. ..................... 26 Figure 2.10: Background subtraction: the background image (left) is subtracted from the image with the subject (middle) to obtain a silhouette of the subject (right). .................. 26 Figure 2.11: Visual hulls created using 8 cameras (left) and 10 cameras (right). ............ 27 ix

Figure 2.12: Final three-dimensional visual hull created from visual hulls in Figure 2.11 (left – front view, right – side view). ................................................................................ 27 Figure 2.13: Example of segmentation of the visual hull. Note that these are not the actual joint centers used in tracking; instead, joint center locations were defined by the marker-based system using bony landmarks identified by palpation. .............................. 29 Figure 2.14: Example of the tracking results. Each visual hull (left) was tracked by a subject-specific model (right) to obtain kinematic data. ................................................... 30 Figure 2.15: The tracking results (gray) overlaid on the original visual hull (green) for the example in Figure 2.14. .................................................................................................... 31 Figure 2.16: Vicon Nexus working environment. Each marker is reconstructed in the 3D environment, and the different colors represent different body segments. ....................... 32 Figure 2.17: Reference pose in each system (left – MB results, middle – original pose, right – MMC results). ....................................................................................................... 33 Figure 3.1: Time normalized hip joint center differences for all DVJ trials, with the shaded portion representing standard deviation. ............................................................... 37 Figure 3.2: Time normalized knee joint center differences for all DVJ trials, with the shaded portion representing standard deviation. ............................................................... 38 Figure 3.3: Time normalized ankle joint center differences for all DVJ trials, with the shaded portion representing standard deviation. ............................................................... 38 Figure 3.4: Time normalized hip joint center difference for all DC trials, with the shaded portion representing standard deviation. ........................................................................... 39

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Figure 3.5: Time normalized knee joint center difference for all DC trials, with the shaded portion representing standard deviation. ............................................................... 40 Figure 3.6: Time normalized ankle joint center difference for all DC trials, with the shaded portion representing standard deviation. ............................................................... 40 Figure 3.7: Time normalized knee flexion/extension angle differences for all DVJ trials, with the shaded portion representing standard deviation. ................................................. 42 Figure 3.8: Time normalized knee abduction/adduction angle differences for all DVJ trials, with the shaded portion representing standard deviation. ....................................... 42 Figure 3.9: Time normalized knee flexion/extension angle differences for all DC trials, with the shaded portion representing standard deviation. ................................................. 43 Figure 3.10: Time normalized knee abduction/adduction angle differences for all DC trials, with the shaded portion representing standard deviation. ....................................... 43 Figure 4.1: Possible causes of joint center position differences grouped by system along with the likelihood of contribution to difference. ............................................................. 45 Figure 4.2: Time normalized eigenvalue percent differences for all DVJ trials. .............. 46 Figure 4.3: Time normalized eigenvalue percent differences for all DC trials. ............... 47 Figure 4.4: Change in distance between knee marker and FM2 marker for DVJ trials. .. 48 Figure 4.5: Change in distance between knee marker and FM2 marker for DC trials. .... 48 Figure 4.6: Eigenvalue vs. joint center differences during DVJ trials for the right leg .... 51 Figure 4.7: Eigenvalue vs. joint center differences during DVJ trials for the left leg ...... 52 Figure 4.8: Change in distance between knee marker and FM2 marker vs. eigenvalue percent difference during DVJ trials for the right leg. ...................................................... 53 xi

Figure 4.9: Change in distance between knee marker and FM2 marker vs. eigenvalue percent difference during DVJ trials for the left leg. ........................................................ 53

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Chapter 1: Introduction

ACL Injuries As sports become more and more popular in the United States and around the world, the incidence of anterior cruciate ligament (ACL) injuries among athletes continues to be a major problem in sports medicine (Renstrom et al., 2008). In a European country with a national ACL surgical registry, the incidence of ACL reconstruction surgeries was nearly 1 in 1,000 citizens among the active portion of the population (ages 16 to 39), and this rate did not include the number of ACL injuries where surgery was either not required or not chosen (Granan et al., 2008). Along with the extensive rehabilitation required for reconstructive ACL surgery, an ACL reconstruction costs tens of thousands of dollars (Hewett et al., 1999). Therefore, it is likely that over $1 billion total is being spent annually in the United States alone to reconstruct and rehabilitate injured ACLs. ACL Location and Function The ACL is one of the four major ligaments located in the knee, along with the posterior cruciate ligament (PCL), the lateral collateral ligament (LCL), and the medial collateral ligament (MCL). One end of the ACL is connected to the posterior part of the lateral femoral condyle while the other end of the ACL is attached to a fossa on the tibia 1

that is located anterior and lateral to the medial tibial spine (Duthon et al., 2006). The ACL is very important to knee joint stability, and its major role is to prevent excessive translation of the tibia relative to the femur (Butler et al., 1980). Occurrence in Females While ACL injuries can affect any athlete in any sport, research has shown that females are three to four times more likely to injure their ACL than males in common sports such as basketball and soccer (Agel et al., 2005; Hootman et al., 2007; Messina et al., 1999). The exact reason for this dramatic difference is unknown, but vigorous research is currently being undertaken to determine what causes females to experience a greater frequency of ACL injuries. So far, the increase in frequency of ACL injury in females has been attributed to a combination of neuromuscular (i.e. trunk and core mechanics, knee loading), anatomical/structural (i.e. ACL length and cross-sectional area), and hormonal factors (i.e. phase of menstrual cycle, estrogen levels) (Shultz et al., 2010). Noncontact Just as there is a dramatic difference between male and female ACL injury risk, there is a significant difference between contact and noncontact ACL injuries. Current statistics suggest that around 70% of ACL injuries are noncontact in nature, with the remaining 30% percent being a contact injury (Agel et al., 2005). A noncontact injury is commonly defined as the absence of direct contact with another player, the ball, or the floor (Agel et al., 2005). Most noncontact injuries that occur can be attributed to activities involving deceleration, pivoting, cutting, or landing. 2

Consequences of injury ACL injury occurrence is reported to begin around age 12 for females (Shea et al., 2004). Besides the obvious consequences that include the large costs of reconstructive surgery and rehabilitation, many other factors can lead to an ACL injury being a traumatic period in an adolescent female’s life. If the injury occurs during the school year, the athlete will likely have to miss class time which could also result in lower grades. Sometimes an ACL injury patient does not return to their previous athletic form before the injury, so the patient may not be eligible for college athletic scholarships they would have otherwise received if they had not been injured. More importantly, the patient may experience emotional difficulties and increased stress levels (Carson and Polman, 2008). After rupturing the ACL, it is commonly believed that greater than 50% of patients eventually develop osteoarthritis (OA) in the injured knee (Lohmander et al., 2007). While this number has been accepted in the past, more studies are challenging this statistic and claiming it is too large. A systematic review of 31 studies was recently undertaken, and the highest rated studies found that subjects with an isolated ACL injury developed knee OA 0% to 13% of the time ten years after their injury (Oiestad et al., 2009). Subjects with an injury to their meniscus along with their ACL developed knee OA 21% to 48% of the time ten years after their injury (Oiestad et al., 2009). While the exact percentage of patients who develop knee OA after rupturing their ACL remains unknown, even the likelihood for an increased risk of developing OA after an ACL injury is a significant consequence of the injury. Because of these consequences and the large 3

financial costs associated with ACL reconstruction, many researchers are attempting to determine what factors cause ACL injuries, if they can be predicted, and how they can be prevented. Consensus on future ACL research With the large quantity of research being undertaken in the field of ACL injuries, multiple groups of ACL injury experts are constantly attempting to organize what is already known about ACL injuries, the direction the field should be heading in order to prevent unnecessary research, and ways to produce more valuable results that progress the field forward. The consensus, as it relates to this study, is that ACL injury research should move away from sex-comparison studies and more towards determining the underlying causes of the differences between male and female ACL injury risk (Griffin et al., 2006; Renstrom et al., 2008; Shultz et al., 2010). However, since ACL injuries are not caused by only one factor, experts believe future studies should focus on integrating as many factors as possible (i.e. anatomical as well as biomechanical) (Renstrom et al., 2008; Shultz et al., 2010). To collect more realistic data, researchers would like to integrate more functional testing conditions since current tests usually take place in labs where the controlled conditions are different from game situations (Shultz et al., 2010). For example, jumping and completing a simple cut on a rubber mat inside a lab is very different from the same athlete reacting and cutting on an outdoor grass soccer field in response to another athlete that they are attempting to avoid. Similarly, the effects of playing surfaces, protective

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equipment, and meteorological conditions on ACL injury risk are relatively unknown and should be investigated (Griffin et al., 2006; Renstrom et al., 2008). Pre-screening and Retraining The best method of treating ACL injuries would be to avoid them in the first place, and researchers have attempted a wide variety of injury prevention methods to decrease the rate of injury. These methods include pre-screening athletes for higher risks of injuries, introducing injury prevention strategies, training programs, and injury awareness education (Griffin et al., 2006; Renstrom et al., 2008; Shultz et al., 2010). These methods would be most effective in adolescent athletes because injury prevention strategies can be introduced that could help the athlete remain ACL injury-free for a lifetime. The overwhelming consensus is that development of these programs should continue (Griffin et al., 2006; Renstrom et al., 2008; Shultz et al., 2010). One method of preventing injuries is to pre-screen athletes before their respective athletic season to determine which athletes’ anatomical and biomechanical measurements put them at an increased risk for an ACL injury (Hootman et al., 2007; Landry et al., 2007; Lathrop et al., 2011). These athletes that are at a higher risk for injury could then be trained using specific programs meant to lower ACL injury risk. Intervention programs that include balance training, plyometric training, strength training, and feedback have been shown to reduce the occurrence of ACL injuries in small studies (Gilchrist et al., 2008; Hewett et al., 1999; Mandelbaum et al., 2005; Myklebust et al., 2003). However, neither an ideal pre-screening program nor an ideal training intervention program currently exists on a widespread scale, and ACL injury rates in 5

females have yet to decrease (Shultz et al., 2010). Work to create practical pre-screening and intervention programs continues with the goal of creating an ideal injury prevention method as soon as possible. Recent Methods Many recent studies have attempted to determine which anatomical and biomechanical factors cause an increased risk of ACL injury because these factors could be tested during a pre-screening program and improved during an intervention program. In one study, researchers compared the knee and ankle kinematics between middle school and high school-aged males and females during a jump-stop unanticipated cut (JSUC) maneuver. The study found that females had greater knee abduction (valgus) angles at initial contact than males while there was no gender difference in either knee flexion at initial contact or maximum knee flexion throughout the JSUC (Ford et al., 2005). These results were similar to those found by McLean et al., who determined females experienced significantly larger peak knee valgus motion than males during jumplanding, sidestepping, and shuttle running tasks (McLean et al., 2005). They also found gender differences at initial contact in knee valgus motion for all three tasks (McLean et al., 2005). Larger knee valgus angles in females have also been measured in running, side-cutting, and cross-cutting tasks, but were not measured in a vertical stop-jump task (Chappell et al., 2007; Malinzak et al., 2001). Since females have been found to have an ACL risk of three to four times that of males, these studies were significant because they suggested a major biomechanical difference between sexes could be measured using simple jumping tasks. 6

In another relevant study, researchers recorded drop-vertical jump data of high school female athletes before their athletic season. After a 13-month surveillance period, the researchers further examined the data of the athletes that subsequently injured their ACL during the season and compared it to the data of the uninjured athletes to determine if any variables predicted ACL injuries. The study found that the injured females had higher valgus angles and knee abduction moments at the knee during the impact phase of the drop-vertical jump task (Hewett et al., 2005). This study was relevant to prevent ACL injuries because researchers discovered specific kinematics that they could screen athletes for to predict ACL injuries and found joint movements that should be decreased during training intervention programs. However, increased knee valgus motion alone does not cause ACL injury. Instead, knee valgus motion along with other ACL loading mechanisms and poor movement patterns likely contribute to the increased risk of ACL injury (Padua et al., 2009). Another variable that has been found to vary between males and females, and therefore believed to increase the risk of ACL injury, is decreased knee flexion angle during dynamic tasks (Chappell et al., 2007; Hewett et al., 2005; Malinzak et al., 2001; McLean et al., 2005). Additionally, decreased hip flexion has been found in females compared to males during certain dynamic activities (Chappell et al., 2007; Landry et al., 2007; McLean et al., 2005). These results were found by having subjects complete cutting tasks and drop-vertical jumps, among other movements. Because many studies have determined that knee valgus motion is a strong predictor of ACL injury in females, a group of researchers attempted to determine 7

measures that predicted increased knee abduction moment (KAM) in females. A study that examined the knee kinematics of female athletes during a drop-vertical jump procedure determined that increased knee abduction angle, increased knee extensor moments, decreased knee flexion angle, increased tibia length, and increased mass normalized to body height that accompanies growth contributed to 80% of the variance in KAM during the drop-vertical jump landing task (Myer et al., 2011a). By identifying these specific variables that increased KAM, better screening and intervention programs can be created to prevent ACL injuries. Although this paper reviews only a small sampling of the vast amount of ACL injury studies, many different variables have been shown to vary between males and females depending on the movement that subjects were asked to complete for each study. When choosing a movement for a pre-screening program, researchers must be careful to choose movements that have already been proven to show differences between male and female athletes. For example, if a pre-screening program is going to analyze knee valgus motion, asking subjects to complete a drop-vertical jump task would be an appropriate option because many studies have already found that differences in knee valgus motion can be detected using a drop-vertical jump task. New Methods Because an ideal ACL screening program would likely be used all over the world to screen female athletes, using an expensive and complex method like a marker-based motion capture system is not realistic. Ideally, a high school trainer or a clinician could quickly complete the ACL screening tests rather than a specialized lab researcher, who is 8

currently required to place markers and operate the marker-based motion capture system. Also, multiple markers cannot be applied to each athlete being screened because this would take too much time for the trainer and for the athletes. Currently, at least two methods are being developed to pre-screen female athletes for increased ACL injury risk. The first method requires the measurement of five parameters: tibia length, knee valgus motion, knee flexion range of motion (ROM), body mass, and quadriceps-to-hamstrings ratio. Each parameter is measured by the clinician, and then using a nomogram the clinician determines how many points each parameter receives. By adding the number of points for all five parameters, the total number of points correlates to the probability of a large knee load and an increased ACL injury risk (Myer et al., 2011c). With this method, knee valgus motion and knee flexion ROM are measured by having a subject perform a drop-vertical jump while being recorded using two standard video cameras. One camera is set to record the subject’s frontal plane while the second camera is set to record the subject’s sagittal plane. Knee valgus motion is determined by calculating the maximum displacement of the knee joint in the frontal plane as the subject lands during the drop-vertical jump with respect to the position of the knee just before initial contact. Knee flexion ROM is determined by calculating the difference between maximum knee flexion angle during landing and the knee flexion angle just before initial contact. Knee valgus motion and flexion ROM can be calculated with the help of a free image processing program such as ImageJ (Myer et al., 2011c). This method was validated by the researchers as a reliable method to predict large KAM, and is a positive 9

step towards a robust screening method that could be used to determine which females have a higher risk for ACL injuries (Myer et al., 2011b). A second method that is being developed to determine poor landing biomechanics is known as the Landing Error Scoring System (LESS). This method is similar to the nomogram method in that data is collected by having a subject perform a drop-vertical jump with one video camera positioned to record the frontal plane of the subject during landing and a second camera to record the sagittal plane. Each subject is required to perform three trials and a clinician uses the video of these trials at a later time to score the number of landing technique errors that the subject made during the drop-vertical jump (Padua et al., 2009). Clinicians score each subject’s set of three trials based on 17 different scoring items, with higher LESS scores indicating worse landing biomechanics. The subjects are then placed into one of four groups based on their score: excellent, good, moderate, and poor landing kinematics (Padua et al., 2009). However, during a study of 5,047 high school and college athletes, the LESS system was not able to predict ACL injury (Smith et al., 2012). Therefore, the LESS method and other pre-screening methods must continue to be developed before they are used on a widespread scale. Marker-based motion capture Marker-based motion capture methods have been very useful to ACL injury research because many of the variables measured during ACL research have good reliability from session to session (Ford et al., 2007). Additionally, marker-based motion capture is less invasive than other motion capture methods such as bone pins and less dangerous than methods such as using x-ray machines. Multiple marker-based systems 10

are commercially available, so the majority of biomechanical motion capture studies use these systems. As a result, marker-based systems have become an important biomechanics research tool that is realistic for use in a research environment. However, along with the advantages of the marker-based motion capture method come the disadvantages of the system. The marker-based system requires the researcher to place usually at least 20 retro-reflective markers on anatomical landmarks of each subject before collecting data. These markers may interfere with the subject’s normal movements, which would give less accurate results. Another disadvantage is that the placement of the markers on the subject requires additional time and an expert to place the markers before data can be collected. Since markers are required to be placed on the subject, athletes cannot be captured in their normal sport setting and researchers cannot collect data from actual games or even practices. Finally, the equipment required for marker-based systems tends to be expensive. Markerless Motion Capture As new methods for collecting data that can be used to predict ACL injuries in athletes are emerging, so too are new methods for collecting biomechanical data. One of these newer methods is known as markerless motion capture, a non-invasive data collection method that entails recording videos of a subject’s movements using multiple synchronized video cameras and several computer vision techniques to process the videos (Mündermann et al., 2006). By subtracting a background image from an image with the subject performing a movement, a silhouette of the subject is obtained. The silhouettes from each camera view are combined one frame at a time to create a three-dimensional 11

model of the subject, also known as a visual hull. The visual hulls are then combined in sequence to create a dynamic model of the moving subject. To track the movements of these visual hulls, a subject-specific model is created by using linear regression techniques to fit a human body shape with known joint centers to a visual hull of the subject standing in a common pose (Corazza et al., 2010b). Using the subject-specific model, the movements of the visual hulls are tracked by fitting the surface points of the subject-specific model to the surface points of the visual hull (Corazza et al., 2010b). This tracked model can help the researcher determine certain parameters such as the angle between two segments of the subject and the location of the subject’s joint centers. If force plate data is collected simultaneously, inverse dynamics can be used to find joint torques and forces. Markerless motion capture developed mainly out of the need for a non-invasive motion capture system, as a result of advancements in the computer vision field, and the potential to have a more accurate solution than existing motion capture methods (Mündermann et al., 2006). Markerless motion capture has the potential to minimize the effects of soft tissue artifact (STA), or the movement of the skin and fatty tissue on which the markers are placed. Currently, STA is the most significant issue with current markerbased motion capture techniques. Attempts at quantifying error caused by STA have produced varying results, although experts tend to agree that it is the most significant error in marker-based motion capture (Leardini et al., 2005). More practically, by eliminating the need to place markers on a subject before collecting data, the subject’s preparation time is decreased. The subject’s movements 12

will be more natural because they do not have multiple markers placed all over their body and they do not have to worry about displacing one of their markers (Mündermann et al., 2006). There are many possibilities for applications of markerless motion capture in the biomechanics field. The markerless method has already been used in an ACL injury research study to determine the effect that the shoe-surface interaction had on a subject’s movements during a cutting task (Dowling et al., 2010). The markerless method has also been used in an outdoor study, a location that is normally difficult for marker-based motion capture (Sheets et al., 2011). In the future, MMC can also be used to record and model athletes in game and practice settings. By collecting data from game or practice situations, researchers will have more realistic data about the motions athletes experience during competition or how athletes move differently during games. Training programs, such as the programs meant to reduce ACL injury risk, can be more suited towards reducing loads athletes experience during games. Future markerless methods will likely allow researchers to record multiple athletes at a time. This data collection method would be very helpful because it would potentially help researchers understand how athletes react to each other during certain situations. Purpose of Thesis The purpose of this research is to determine if markerless motion capture methods can be used to accurately measure knee movements during dynamic activities that have been shown to predict ACL injury risk in high-school aged females. We will collect data using both a markerless and a marker-based motion capture system to compare the hip, 13

knee, and ankle joint center positions and knee flexion/extension and abduction/adduction angles calculated by each system. Significance of Research This research is significant because females are currently three to four times more likely to injure their ACL than males, with the majority of these injuries being noncontact in nature (Agel et al., 2005; Hootman et al., 2007; Messina et al., 1999). Markerless motion capture can become an important tool in the development of pre-screening programs along with intervention training programs because markerless methods would be faster for researchers or clinicians to perform detailed measurements on each subject. In addition, data collected by markerless methods would be more applicable to game and practice situations because data could be collected outdoors while athletes participate in either a game or a practice. Additionally, movements captured with the markerless system will likely be more natural because the subject does not have markers attached to their body. This research will help expand markerless motion capture technology toward the sports biomechanics field, a field that it is rarely used in, and this research will provide a comparison for markerless technology to determine the direction this method must go towards to be an important tool in ACL injury research studies. If markerless motion capture could be used to efficiently and effectively screen for ACL injury risk, more athletes could be screened and those identified as “at-risk” could then participate in intervention programs designed to decrease the athlete’s injury risk. As a result, the ACL injury rate in females could decrease, and thousands of females would be positively 14

affected. These uninjured females would not miss class time, lose athletic scholarships, or have a higher risk of developing osteoarthritis due to an ACL injury. By enabling wide-spread screening of athletes for ACL injury risk, millions of dollars, and potentially billions, would be saved each year by significantly lowering the ACL injury rate in females.

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Chapter 2: Methods

Data was collected in Scott Laboratory at The Ohio State University using both a markerless motion capture system and marker-based motion capture system simultaneously. The markerless system consisted of ten Allied Vision Technologies Prosilica GX1050C digital cameras (Stadtroda, Germany) that were synchronized in time using software and hardware manufactured by Spica Technology Corp. (Maui, HI) (Figure 2.1). The resolution of the digital cameras was 1024 x 1024 pixels and the cameras recorded at 100 Hz. Eight of the cameras were focused on capturing the entire subject’s movement, while two of the cameras were aimed only to capture the movement of the subject’s legs and feet. Calibration for the markerless system was completed using internal and external camera parameters obtained before the subject arrived each day. The marker-based system consisted of eight Vicon T20 motion capture cameras (Vicon Motion Systems, Los Angeles, CA) recording data at 300 Hz (Figure 2.2). Calibration parameters for this system were also obtained before the subject arrived each day.

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Figure 2.1: Markerless motion capture camera. Ten of these cameras were placed around the capture volume.

Figure 2.2: Marker-based motion capture camera. Eight of these were placed around the capture volume.

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A common global reference frame was obtained for the two systems using a registration object outfitted with reflective markers that were visible to both systems (Figure 2.3). Additionally, the two systems were synchronized in time using a square voltage signal sent from the markerless system to the marker-based system at every instance that a frame was collected with the markerless system. Ground reaction force data was collected using two force plates (Bertec Corporation, Columbus, OH) recording at 900 Hz and synchronized in time with the marker-based data (Figure 2.4).

Figure 2.3: Registration object used to set a global reference frame for both systems. Reflective markers that both systems could record were placed on top of the orange balls. The orange balls were not used in the calibration procedure.

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Figure 2.4: Force plate set-up. The plates were placed side by side so one foot could land on each force plate during the jumps.

After obtaining IRB approval from The Ohio State University’s Office of Reasonable Research Practices, nine healthy females with no previous ACL injuries were recruited to participate in this study. Eight of the participants were previous college or high school athletes, participating in a variety of sports including soccer, basketball, volleyball, lacrosse, flag football, gymnastics, track, swimming, water polo, and cross-fit training. Female subjects were chosen because of the previously mentioned history of ACL research that was focused on female athletes and the potential for screening programs specifically targeted towards female athletes. Our subjects had an average age of 24.3 ± 4.6 years, an average height of 1.67 ± 0.06 meters, and an average mass of 62.1 ± 5.6 kg.

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After obtaining informed consent for each subject, 81 reflective markers were placed on their skin to collect marker-based motion capture data (Figure 2.5). Because soft tissue movement is a major source of error in marker-based motion capture, we attempted to minimize each limb’s nonrigid motion by collecting data using the pointcluster technique (PCT) (Andriacchi et al., 1998). Of the 81 total markers, 51 markers were placed on anatomical landmarks and the remaining 30 markers were arranged into clusters. Nine of these cluster markers were placed on each of the thighs, and six cluster markers were placed on each tibia for our study. Since this study focused on calculating movements of the lower body, it was important to minimize the effect of soft tissue movement in the thighs and shanks, and the point cluster technique has been shown to reduce this error significantly (Alexander and Andriacchi, 2001).

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Figure 2.5: Reflective markers (81 total) were placed on each subject to record their motion with the marker-based cameras.

After all of the markers were placed, the subject was asked to stand in two separate “reference” poses. The first reference pose was used as a static calibration for the marker-based system and the second reference pose was used to obtain a subjectspecific model for the markerless processing, as described earlier. For this study, subjects were asked to perform two movements that are commonly used in ACL injury research: the drop-vertical jump and the drop-to-cut. Before performing both movements, each subject performed as many practice trials as they needed until they felt comfortable completing the movement. Once the subject was ready, they completed the movement until four successful trials were recorded with short rests between each trial. 21

For the first movement, the drop-vertical jump (DVJ), the subject began the trial on a box 31 cm in height. The subject was instructed to drop vertically from the box and then immediately jump as high as they could while stretching both of their hands in the air as if they were reaching for a basketball rebound (Figure 2.6). A trial was considered successful if the subject dropped from the box, landed with one entire foot on each force plate, and jumped vertically upward after initial contact. This movement was chosen because it is a common movement used in ACL injury studies, and the majority of the measured variables have been proven to have good to excellent reliability (Ford et al., 2007). Also, the International Olympics Committee suggests using the drop-vertical jump task in any future programs used to identify ACL injury risk in individuals (Renstrom et al., 2008).

Figure 2.6: Drop-vertical jump movement that the subjects performed.

The second movement subjects were asked to complete was a drop-to-cut (DC). The subject was instructed to drop vertically from a box 31 cm in height and push off with their right foot to cut 45° to their left immediately after landing (Figure 2.7). This 22

movement is similar to a cut an athlete would use in soccer or basketball. A trial was considered successful if the subject dropped from the box, landed with one entire foot on each force plate, and cut 45° to their left after initial contact. This movement was chosen because similar cutting movements are commonly used in ACL injury studies to simulate a quick movement that is used in a number of different sports.

Figure 2.7: Drop-to-cut movement that the subjects performed.

Data Processing Overview After the collection of data was complete, the markerless data and marker-based data were processed separately and then compared to each other. Using the results of a trial with the registration object (Figure 2.3), a common global reference frame was established so the results of each system could be directly compared. This registration object had 12 reflective markers placed at varying heights that were measured by the Vicon Nexus software program, and these coordinates were used in the external calibration of the markerless motion capture system. For both the markerless and marker-based data processing, the goal of the processing step was to calculate the 23

coordinates of the joint centers for the right and left hips, knees, and ankles. The data processing focused on lower body joint centers to determine if markerless motion capture methods were an acceptable tool for use in ACL injury research. Markerless Data Processing After the internal and external calibration of the ten markerless motion capture cameras, three-dimensional models, known as visual hulls, were created of the subject performing each movement frame by frame. Since the markerless motion capture system’s cameras were synchronized in time and recorded data at 100 Hz, images from ten different camera angles that were recorded simultaneously were available at increments of 0.01 seconds in each trial (Figure 2.8 and Figure 2.9). For each image of the subject during their trial, a background image was subtracted using color and intensity thresholds so that only the subject’s silhouette remained (Figure 2.10). For each frame, these ten images were combined to create a three-dimensional visual hull using a technique developed by Laurentini (Laurentini, 1994). To create visual hulls of the subject’s whole body throughout the entire trial, it was necessary for the subject’s whole body to be visible to all ten cameras during the entire trial. Since the subject’s whole body was only visible to eight cameras during the entire trial because two cameras were focused on only the feet, the final visual hull was created in two steps. First, separate visual hulls were created of the upper body and lower body using eight cameras and ten cameras, respectively (Figure 2.11). The height at which the seam of the upper and lower body occurred was chosen based on the height that all ten cameras were still able to view the subject’s whole body. These two visual hulls were then combined using MATLAB 24

(MathWorks, Natick, MA) to create the final visual hull (Figure 2.12). These visual hulls were created with a voxel size of 5 mm and a triangular surface mesh.

Figure 2.8: Synchronized images from multiple cameras are used to create the visual hulls.

25

camera 1

camera 3 camera 2 camera 4 camera 10

camera 6 camera 9

camera 5

camera 7

camera 8

Figure 2.9: Positions of the ten cameras surrounding the capture volume.

Figure 2.10: Background subtraction: the background image (left) is subtracted from the image with the subject (middle) to obtain a silhouette of the subject (right).

26

Figure 2.11: Visual hulls created using 8 cameras (left) and 10 cameras (right).

Figure 2.12: Final three-dimensional visual hull created from visual hulls in Figure 2.11 (left – front view, right – side view).

27

To obtain kinematic data from these visual hulls (“track”), the first step was to generate a subject-specific model consisting of the surface shape and joint center locations for each subject (Figure 2.13). This model was generated using the method developed by Corazza et al. (2010). In this method, a visual hull of the subject standing in the reference pose was input into an algorithm that assigned each point in the visual hull to only one of the body’s 15 segments. To complete the segmentation, a template mesh was run through an iterative algorithm that stretched, compressed, rotated, and/or translated each segment of the template mesh until it matched our reference pose visual hull (Corazza et al., 2010a). The template mesh and corresponding deformations were based on a database of known human body shapes along with some learned body shapes (SCAPE) (Anguelov et al., 2005; Corazza et al., 2010a). The points of the original visual hull were assigned to segments of the template based on a closest point algorithm that matched points on the original mesh and the template mesh (Corazza et al., 2010a). This process led to reasonable segmentation of the body but joint center locations that were clearly inappropriate (Figure 2.13). To remove this potential source of error and compare the differences between the marker-based and markerless results based only on the markerless tracking algorithm, the joint center positions in the markerless reference poses were assigned the values of the joint center positions from the marker-based reference pose.

28

Figure 2.13: Example of segmentation of the visual hull. Note that these are not the actual joint centers used in tracking; instead, joint center locations were defined by the marker-based system using bony landmarks identified by palpation.

After generating a subject-specific model, an articulated iterative closest point (ICP) algorithm developed by Corazza et al. was used to track the visual hulls (2010). For each frame in a series of visual hulls, the articulated ICP algorithm applied a series of rigid transformations and rotations to each segment of the subject-specific model until the distance between its points and the visual hull mesh was minimized (Corazza et al., 2010b). Additionally, each segment of the subject-specific model was connected using a six degree-of-freedom joint so that each rigid transformation of one body segment was propagated to any distal body segments (Corazza et al., 2010b). At each iteration, the optimized tracking solution was calculated by minimizing an ICP cost function before 29

converging on what was assumed to be the optimized tracking solution (Corazza et al., 2010b). To track our data set, the ICP algorithm used a maximum of 80 iterations for each frame. The end result of this step was kinematic data that could be compared to the marker-based calculations (Figure 2.14 and Figure 2.15).

Figure 2.14: Example of the tracking results. Each visual hull (left) was tracked by a subject-specific model (right) to obtain kinematic data.

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Figure 2.15: The tracking results (gray) overlaid on the original visual hull (green) for the example in Figure 2.14.

Marker-based Data Processing To process the marker-based data, the Vicon Nexus software program was used to reconstruct each reflective marker in a three-dimensional graphical environment (Figure 2.16). Every marker on the subject was labeled according to the body landmark that it was attached to or according to the cluster to which it belonged. All drop-to-cut (DC) and drop-vertical jump (DVJ) trials were trimmed to only include the subject’s initial contact with the force plates through the subject’s final push off from the force plates (stance phase). This step was taken to ensure our data was similar to data that would be 31

recorded during ACL injury research, since researchers are most interested in a subject’s movement during landing.

Figure 2.16: Vicon Nexus working environment. Each marker is reconstructed in the 3D environment, and the different colors represent different body segments.

After labeling all markers and trimming the trials to only include the stance phase, all marker trajectories were low-pass filtered using a Woltring filter with generalized cross-validation (Woltring, 1986). Then, the data was processed using MATLAB code designed to work with Vicon Nexus data to calculate joint centers based on the anatomical landmark markers and the clusters of markers. This code was developed by Dr. Ajit Chaudhari of The Ohio State University Sports Biomechanics Lab, and was designed to minimize the effect of nonrigid motion. Therefore, each marker in the cluster 32

is assigned an equal mass and the center of mass is calculated based on the position of all markers at an instance in time. By calculating the percent difference of the eigenvalue from frame to frame, the amount of nonrigid motion and quality of the cluster can be quantitatively examined. Data Comparison Before any of the movement trials were processed, the reference pose joint center positions that were calculated using the markerless model generator were changed to match the joint center positions calculated by the marker-based system (Figure 2.17).

Figure 2.17: Reference pose in each system (left – MB results, middle – original pose, right – MMC results).

To determine the difference in the joint center coordinates calculated by each system, the synchronization data was used to align the results of the markerless (collected at 100 Hz) and marker-based data (collected at 300 Hz) frame by frame. To complete the

33

synchronization, every third marker-based frame was matched to its corresponding markerless frame. The root mean squared differences for each joint and each frame were calculated using (Eq. 1)

(1)

where the subscript MMC represents the markerless joint center coordinates and MB represents the marker-based joint center coordinates. The difference was averaged throughout the entire stance phase of the trial. For each subject, three DVJ trials and three DC trials were chosen for analysis. The results from the DVJ trials were averaged together for all nine subjects, and the same was done for the DC trials. Therefore, 27 trials were averaged together to obtain separate results for the DVJ trials and the DC trials. For all calculations, the time was normalized to a percent of the stance phase. This normalization permitted easier comparison between trials and subjects. Using the coordinates of the joint centers, joint angles that are commonly investigated in ACL injury research were also calculated. These angles included knee flexion/extension and knee abduction/adduction, and they were determined by calculating the angles between the thigh and shank segments. The thigh segment was defined as the vector between the hip and knee joint centers and the shank segment was defined as the vector between the knee and ankle joint centers. These vectors were then projected onto the global reference planes. The knee flexion/extension angle was defined as the angle 34

between the thigh and shank segments that were projected onto the global sagittal plane and knee abduction/adduction angle was defined as the angle between the thigh and shank segments that were projected onto the global coronal plane.

35

Chapter 3: Results

During the stance phase of all 27 DVJ trials, the joint center RMS differences between the markerless and marker-based measurements for the lower body varied from 16.7 mm to 27.6 mm with standard deviations ranging from 6.5 mm to 9.6 mm (Table 3.1). The joint center differences for the lower body joints varied from 15.9 mm to 24.5 mm during all 27 DC trials, with standard deviations ranging from 5.1 mm to 7.3 mm. Ankle joint centers had the lowest mean difference between the two systems, while the knee joint centers had the highest mean difference between systems.

Table 3.1: Joint center difference for all trials. DVJ Mean (mm) Right Hip Left Hip Right Knee Left Knee Right Ankle Left Ankle

21.7 19.5 25.2 27.6 16.7 18.9

DC

Standard Deviation (mm) 7.6 6.8 9.6 8.0 6.5 6.5

Mean (mm) 19.6 24.5 15.9 -

Standard Deviation (mm) 6.4 7.3 5.1 -

To determine if there were any trends in the joint center differences throughout the trials, the joint center differences during each of the 27 DVJ trials were calculated 36

frame by frame and time normalized (Figure 3.1, Figure 3.2, and Figure 3.3). For each joint center, the plots for the left and right joints followed similar patterns because the legs were performing similar movements. The knee joint center difference plots had large differences in the first and last frames, while having global maximums at around 50% of the stance phase. This point in the trial was where the subject was performing their maximum squat and their center of gravity was lowest. The hip joint center difference plots also had global maximums at around 50% of the stance phase. The ankle joint center difference plots, however, had large differences at the beginning and end of the stance phase and a global minimum at around 50% of the stance phase.

Figure 3.1: Time normalized hip joint center differences for all DVJ trials, with the shaded portion representing standard deviation.

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Figure 3.2: Time normalized knee joint center differences for all DVJ trials, with the shaded portion representing standard deviation.

Figure 3.3: Time normalized ankle joint center differences for all DVJ trials, with the shaded portion representing standard deviation. 38

The time normalized plots for the joint center differences for the right side of the lower body during the DC trials followed patterns similar to the plots for the DVJ trials (Figure 3.4, Figure 3.5, and Figure 3.6). An ACL injury is most likely to occur on the plant leg during a cutting task where the subject cuts 45 degrees to the left, so calculations were only completed for the right side of the body.

Figure 3.4: Time normalized hip joint center difference for all DC trials, with the shaded portion representing standard deviation.

39

Figure 3.5: Time normalized knee joint center difference for all DC trials, with the shaded portion representing standard deviation.

Figure 3.6: Time normalized ankle joint center difference for all DC trials, with the shaded portion representing standard deviation. 40

Using the joint centers of the lower body calculated by each system, the differences in knee flexion/extension and abduction/adduction angles between the two systems were calculated for all trials using projection angles (Table 3.2). The time normalized knee angle differences were also plotted to determine if there were any trends throughout the trials (Figure 3.7, Figure 3.8, Figure 3.9, and Figure 3.10). Large differences in the flexion/extension angles occurred at the start and end of the stance phase for both legs, while abduction/adduction angles were rather consistent throughout the entire trial.

Table 3.2: Knee angle differences for all trials.

R Flex/Ext R Abd/Add L Flex/Ext L Abd/Add

DVJ Standard Mean (°) Deviation (°) 3.7 1.0 2.3 0.7 3.9 1.0 3.4 0.4

41

DC Standard Mean (°) Deviation (°) 3.8 1.2 2.7 0.3 -

Figure 3.7: Time normalized knee flexion/extension angle differences for all DVJ trials, with the shaded portion representing standard deviation.

Figure 3.8: Time normalized knee abduction/adduction angle differences for all DVJ trials, with the shaded portion representing standard deviation. 42

Figure 3.9: Time normalized knee flexion/extension angle differences for all DC trials, with the shaded portion representing standard deviation.

Figure 3.10: Time normalized knee abduction/adduction angle differences for all DC trials, with the shaded portion representing standard deviation. 43

Chapter 4: Discussion

Joint Center Differences This study is the first to compare joint center differences between markerless and marker-based motion capture systems during drop-vertical jump and drop-to-cut movements. Previously, the only similar study compared results from both markerless and marker-based systems during walking trials, and found hip joint center differences of 16 ± 7 mm, knee joint center differences of 14 ± 7 mm, and ankle joint center differences of 18 ± 6 mm (Corazza et al., 2010b). The walking data, however, was collected using two fewer cameras (eight instead of ten) that had lower resolution than the cameras used in this study (640 x 480 pixels vs. 1024 x 1024 pixels) (Corazza et al., 2010b). Possible reasons for the higher average joint center differences in our study (Table 3.1) include the fast nature of the movements that we asked our subjects to complete or the accuracy of the markerless and marker-based models. Drop-vertical jump and drop-to-cut movements require fast decelerations and accelerations, while the subject moves at a nearly constant speed during walking trials. The possible sources of error will be further investigated in the rest of this section (Figure 4.1).

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Marker-based

Markerless

Camera calibration

Camera calibration

Marker placement

Visual hull creation

Marker reconstruction

Model generation

Low

High Nonrigid motion

Tracking

Figure 4.1: Possible causes of joint center position differences grouped by system along with the likelihood of contribution to difference.

Marker-based motion capture measurement accuracy, challenges, and improvements Non-rigid motion To investigate the reason for the differences in joint center positions and joint angles calculated by the two systems, the eigenvalue percent differences during the stance phase were plotted for the marker-based results for both the DVJ and DC trials (Figure 4.2 and Figure 4.3). The eigenvalue percent difference is a quantitative calculation for examining how much nonrigid motion occurs in a cluster of markers throughout the trial. The eigenvalue percent differences of both the right and left shank 45

clusters were nominal; however, the right and left thigh clusters showed large eigenvalue percent differences of up to 14% in the DVJ trials and nearly 8% in the DC trials.

2 0

Eigenvalue Percent Difference

-2 -4 -6 -8 -10 -12 L Shank L Thigh R Shank R Thigh

-14 -16 0

20

40 60 Percent of Stance Phase

80

100

Figure 4.2: Time normalized eigenvalue percent differences for all DVJ trials.

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4 R Shank R Thigh


2

0

-2

-4

-6

-8

-10 0

20


80

100

Figure 4.3: Time normalized eigenvalue percent differences for all DC trials.

To determine the source of the large eigenvalue percent differences in the thighs, the distances between a knee marker and a marker in the thigh cluster (FM2) were calculated throughout the stance phase of every trial. The distance between the two markers in the markerless reference pose was calculated to determine the distance the markers moved throughout the trial. The right leg markers experienced a maximum of nearly 18 mm of nonrigid marker motion during the DVJ trials, while the left leg markers experienced greater than 16 mm of nonrigid motion (Figure 4.4). During the DC trials, the right leg markers moved nearly 8 mm from the static pose (Figure 4.5).

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18 Left Right

16 14

Distance (mm)

12 10 8 6 4 2 0 0

20


80

100

Figure 4.4: Change in distance between knee marker and FM2 marker for DVJ trials.

8 Right 7

Distance (mm)

6 5 4 3 2 1 0 0

20


80

100

Figure 4.5: Change in distance between knee marker and FM2 marker for DC trials. 48

The results in Figure 4.4 and Figure 4.5 strongly suggest that a large amount of soft tissue artifact was occurring in the thighs during all trials. The results in this experiment were similar to a study completed by Fuller et al., who found soft tissue motion responsible for markers on the thigh moving up to 20 mm relative to the anatomical landmark that the marker was supposed to represent (Fuller et al., 1997). Although our study only found the motion of two markers relative to each other, the error of the markers relative to the anatomical landmark that they were supposed to represent could have been even greater than 18 mm if both of the markers moved in the same direction. Even though the point cluster marker set was developed to minimize errors caused by soft tissue movement, such as the 18 mm of relative movement that we calculated, our non-optimized PCT code was likely not designed for errors that large. An alternative solution to minimize error caused by soft tissue movement would have been to use an optimized PCT algorithm that redistributes the mass of each point in the cluster. By using this optimized algorithm, the authors of the point cluster technique predict “substantial improvements” (Andriacchi et al., 1998). To determine if there was a relationship between the eigenvalue percent difference for the marker-based clusters and the associated joint center difference during the DVJ trials, these variables were plotted against each other (Figure 4.6). When the entire trial was considered (plot on left) the coefficient of determination (r2 value) was calculated to be 0.39, with a correlation coefficient of 0.62. However, when 75% of the stance phase was chosen (plot on right, frames 20-94) based on the pattern of differences 49

in Figure 3.2 and force plate data, the coefficient of determination was increased to 0.83, with a correlation coefficient of 0.91. Frame 20 was chosen as an approximation of the point in the trial where the impact on the force plate in the vertical direction reached a maximum for both legs. The actual maximum vertical force occurred at an average of the 17th frame for the right leg and the 21st frame for the left leg. Frame 20 also corresponded closely to the local minima seen in Figure 3.2. These local minima actually occurred in the 16th frame for the right leg and the 19th frame for the left leg. The last 5% of the trial was not included in this analysis because the data seemed to be influenced by end effects due to data processing. This influence will be discussed in further detail later in this section. The local minima seen in Figure 3.2 actually occurred in the 92nd frame for the right leg and 94th frame for the left leg. Therefore, frames 20 through 94 were chosen based on an approximation of the stance phase from the subject’s largest impact force through 95% of the stance phase, and to determine the relationship of the parabolashaped portion of the joint center position difference curves.

50

34

34

r2=0.39

32

30

Joint Center Difference (mm)


30

28

26

24

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26

24

22

22

20

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18 3

r2=0.83

32

4

5

6 7 8 Eigenvalue Percent Difference

9

10

18 4

5


9

10

Figure 4.6: Eigenvalue vs. joint center differences during DVJ trials for the right leg (left – all frames, right – frames 20-94).

Similarly, when plotting the same variables for the left thigh cluster and left knee joint center, the coefficient of determination was 0.40 for all frames compared to 0.77 for frames 20-94 (Figure 4.7). This segment results in the correlation coefficient being increased from 0.63 to 0.88 for the left leg.

51

34

34

r2=0.40

30

28

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24

30

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20 4

r2=0.77

32



32

6


14

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20 6

7

8

9 10 11 12 Eigenvalue Percent Difference

13

14

15

Figure 4.7: Eigenvalue vs. joint center differences during DVJ trials for the left leg (left – all frames, right – frames 20-94).

Next, the distance between the knee marker and an arbitrary cluster marker throughout the DVJ trials was plotted against the eigenvalue percent difference throughout the DVJ trials to determine if skin tissue artifact was causing the eigenvalue percent difference. For the right leg and the segment containing frames 20-94, the correlation coefficient was 0.96 (Figure 4.8). For the left leg, the correlation coefficient was 0.98 (Figure 4.9).

52

10

r2=0.92


9

8

7

6

5

4

3 0

5

10 Distance (mm)

15

20

Figure 4.8: Change in distance between knee marker and FM2 marker vs. eigenvalue percent difference during DVJ trials for the right leg. 15

r2=0.97

14


13 12 11 10 9 8 7 6 5 0

2

4

6

8 10 Distance (mm)

12

14

16

18

Figure 4.9: Change in distance between knee marker and FM2 marker vs. eigenvalue percent difference during DVJ trials for the left leg. 53

The correlation coefficients of 0.98 and 0.96 very strongly suggest that the soft tissue movement did affect the marker-based results for the knee joint center. This result was expected, as soft tissue movement has been shown to alter marker-based results in multiple studies and is the largest source of error during marker-based motion capture studies (Leardini et al., 2005). The next important observation in our study was that the eigenvalue percent differences of the thighs were strongly suggested to affect the knee joint center differences that were calculated between the two systems. With correlation coefficients of 0.91 and 0.88 for the right and left legs (Figure 4.6 and Figure 4.7), respectively, the nonrigid motion of the thigh likely directly affected the joint center difference in the knee. This relationship could indicate that the marker-based results were responsible for the majority of the difference in the knee joint center. This relationship would also suggest that the markerless results were more accurate than the marker-based results for the knee joint center during the DVJ trials. The nonrigid motion in our study, especially of the thighs, was likely caused by a combination of three different sources: soft tissue movement, muscle contractions, and movement of the subject’s clothing. Soft tissue movement included either fatty tissue movement or skin movement. The skin of humans is not tightly attached to bones and muscles, and in most places on the body a layer of fat exists between the skin and the bone. Therefore, any markers on the skin would have moved as this layer of skin and fat moved. In this study, nonrigid motion due to soft tissue movement likely occurred when the subject’s feet impacted the force plates during the DVJ trials. The result of the 54

impact caused the fatty tissue in the thighs to oscillate while the subject attempted to regain their balance before jumping again. The next potential cause of nonrigid motion was a result of the muscle contractions that took place throughout the trials. When the activation of the quadriceps muscles (and others) changed throughout the trial, this activation also caused the markers placed above the muscle to move. For instance, when a muscle such as the quadriceps muscle shortens, the cross-sectional area of the middle of the muscle tends to increase. Therefore, any markers placed atop that muscle would also be displaced a small amount. Nonrigid motion resulting from muscle contraction likely would have affected the results in the middle portion of the trial when the subject was using their leg muscles to control their balance as they landed, squatted, and then pushed off as they began their jump. Relative movement between the shorts and skin was the last potential source of error due to nonrigid motion (Figure 2.5). The thigh cluster markers were all placed on the subject’s shorts, so even though the shorts were tight on the subject’s thighs, it was possible that the markers moved a small amount during testing. Movement of the shorts was most likely when the subject was squatting because this was the position that the shorts experienced the most stretching. Despite knowing that there were three sources of nonrigid motion during testing, we could not quantify how much each source contributed to the difference in joint center positions between the two systems based on our testing. Further testing on this group of subjects would be required, and it is very likely that each source of nonrigid motion would vary from subject to subject based on their body composition. For example, 55

measurements of a subject with a large amount of adipose tissue and small muscle mass would likely determine that soft tissue oscillation contributed to the majority of nonrigid motion. In contrast, measurements of a subject with a large muscle mass and low body fat composition would likely determine movement caused by muscle contractions contributed to the majority of nonrigid motion. Similar testing could explain why there was more nonrigid motion in the drop-vertical jump trials than the drop-to-cut trials. Although this study cannot definitively state whether the measurements made using the markerless motion capture approach were more accurate that the marker-based results, these results indicate that the marker-based measurements were largely affected by soft tissue motion; therefore, it appears that markerless motion capture has the potential to be a viable motion capture solution in biomechanics studies involving rapid movements. Future work that compares the markerless measurements to direct measurements of the bones (such as those made using dual plane fluoroscopy or bone pins) is necessary to determine if it is more accurate than marker-based motion capture. Marker reconstruction Other than nonrigid motion and marker placement, other errors in the markerbased system may have resulted from the marker reconstruction step in Vicon Nexus. First, the accuracy of the marker reconstruction in Vicon Nexus depended on how many cameras could see each marker. For example, if eight cameras could see a marker then the accuracy when the marker is reconstructed would likely be very good. However, if only two cameras could see a marker then the accuracy would likely be decreased, and if fewer than two cameras could see a marker there would be a gap in that trajectory. Much 56

of the accuracy of the marker reconstruction depends on the placement of the cameras around the room. This source of error was likely small because the cameras were welldistributed around the room to minimize instances where fewer than two cameras could see each marker. Moreover, with the redundant marker set we used, it was possible to fill gaps in individual marker trajectories using the trajectories of close markers on the same segments. Another likely source of error was that we filtered the marker-based data but did not filter the markerless results. In this technique, all reconstructed data points were used to filter the data and obtain smoother trajectories. However, we did not compare all of the reconstructed data points to the markerless results. Rather, we trimmed our data set to include only the stance phase, so it is possible that the additional reconstructed points that we did not use affected the stance phase data. Since we did not also filter our markerless data, it is possible filtering was responsible for some of the joint center position differences, especially the larger differences measured at the beginning and end of our knee and ankle joint center difference plots. Marker placement The choice of the marker set and the placement of these markers on the anatomical landmarks may have caused some error in our study. As previously mentioned, the PCT marker set was chosen to attempt to minimize the error caused by soft tissue movement. If the markers were not placed correctly, however, then the marker set would not have been able to minimize the effects of soft tissue movement. Reasons for incorrect placement of the markers could have included that the markers were placed 57

on surfaces rather than points, this layer of skin, muscle, and fat over the anatomical landmarks varied in thickness and composition, and the palpation procedure that was used by the expert placing markers (Della Croce et al., 2005). Markerless motion capture measurement accuracy, challenges, and improvements Visual hull creation Since markerless motion capture is a relatively new method of collecting data, we experienced many challenges throughout the data capture and processing stages. The first of these challenges was creating a test set-up that would result in accurate visual hulls during the processing stage. The biggest challenge within this step was accurately separating the subject from the background in each camera view. To aid in the background subtraction step, the subjects wore red clothing to differentiate themselves from the white background. This set-up was appropriate for testing in our laboratory, but if we wanted to collect data during a game or practice then a similar set-up would not likely be possible. Therefore, either the background subtraction algorithm should be improved to separate the subject from the background in any condition, or color differentiation in the images should be improved by increasing the amount of light in the testing environment. Additionally, during preliminary trials, we could not capture videos that would enable us to clearly re-create the feet in the visual hulls. The subject’s shadows created phantom volumes between the feet and under the feet in the visual hulls. These phantom volumes caused inaccurate tracking of the shank and foot segments. To eliminate the effect of the shadows, two of the cameras were placed directly on the ground and aimed 58

to capture only the movement of the feet. Although the shadows were still visible to the eight other cameras, they were not visible to the two ground cameras and the phantom volumes were eliminated when the visual hulls were created. Tracking of the shank and foot segments was improved as a result. Even though the camera placement used in this study eliminated the phantom volumes at the feet, it reduced the number of camera views on the upper body by two and new phantom volumes were created in the upper back area. These phantom volumes were not likely to largely affect our results because we were primarily focused on calculating joint center differences in the lower body. However, it is possible that our results were negatively influenced, as our tracking algorithm performed a global optimization. Therefore, large differences in the model and visual hull upper body surfaces would disproportionally affect the cost function and reduce the impact of small differences in the lower body surfaces. To eliminate phantom volumes, it is necessary to find an appropriate camera set-up before testing subjects. Since we were studying lower body kinematics, we focused our cameras on the feet. However, if we were studying the upper body, we would have focused our cameras on the upper body. Other alternatives to eliminate phantom volumes would have included using more than ten cameras, adjusting the height of the cameras, or adjusting the field of view for each camera. Tracking During the visual hull tracking step, our algorithm required an initial guess to begin the optimization calculations. Depending on our initial guess, the optimized location of the joint center positions changed, and therefore, the difference between the 59

markerless and marker-based joint center positions also changed. When processing one or two trials, processing a series of visual hulls with multiple initial guesses was a reasonable solution to this problem. When processing the 54 trials for this study, however, providing multiple initial guesses, tracking trials multiple times, and selecting the best results by hand was not a reasonable solution. Therefore, the results for the joint center position differences from this study could either increase or decrease by a few millimeters if different initial guesses were provided during the tracking step. The cause of the differences because of different initial guesses was the optimization algorithm. The optimization algorithm determined the most accurate tracking solution by attempting to find the global minimum of the cost function which minimized the difference between the model and visual hull surface. However, if the optimization algorithm calculated a solution that was actually a local minimum rather than a global minimum, there would have been error in the tracking step. This issue is common in many optimization algorithms. Model Generation One of the largest problems of the markerless motion capture method was the ability of the model generator to accurately identify the joint center positions when creating the subject-specific model. An example of this problem was seen in Figure 2.13. Despite the symmetric pose, the lower body joint center positions varied from the right to left side and were not symmetric as we expected. When the authors of the model generator algorithm compared the joint center position results of their algorithm to the gold standard location, they calculated differences of 47 ± 35 mm for the hip joint, 14 ± 7 60

mm for the knee joint, and 9 ± 7 mm for the ankle joint (Corazza et al., 2010a). To determine whether this accuracy is a hindrance for a certain application, further studies are likely needed. For example, it is possible that markerless methods could still be used to group subjects based on their landing kinematics, even with the amount of error reported by Corazza et al. Another difficulty in the creation of our subject-specific models was the lack of a laser scanner to create the reference poses for our subjects. When the authors of the model generator algorithm used a user-generated visual hull to track a trial instead of a laser scan, they found average rms differences of 16 mm for the knee, 15 mm for the hip, and 17 mm for the ankle (Corazza et al., 2010a). In our study, however, using a reference visual hull to track instead of a laser scan likely caused much smaller errors because the joint center positions were adjusted to match the marker-based results. For many researchers, the improved accuracy of the tracking algorithm using a laser scan may not offset the $15,000 to $200,000 required to purchase a laser scanner. Therefore, because of the high costs of a laser scanner and the errors that currently exist in the model generator, developing a more robust subject-specific model generation program may be an important step towards markerless motion capture being used by researchers more often in the future. By changing the joint center positions for the subject-specific model to match the marker-based joint center positions, we likely caused the average joint center differences between the two systems to be lower than if we had tracked the visual hulls with the original markerless joint centers that were output from the model generator. The marker61

based joint center positions that we calculated were likely more accurate than the joint center positions calculated by the markerless model generator for the reference pose. This assumption was based on a qualitative analysis of the accuracy of the joint center positions in the models that were output from the model generator, such as the one shown in Figure 2.13, and because palpation is the gold standard for identifying joint center positions. Although this step led to smaller joint center position differences, it required the user to manually identify joint center positions by either using a marker-based system or by hand digitization of the marker positions in multiple camera views followed by three-dimensional reconstruction of these positions. Other challenges A drawback of using the markerless motion capture system was the extra time required for the internal and external calibration of the cameras. A separate video for each of the ten cameras was required to obtain the internal calibration parameters for that respective camera. Therefore, capturing and processing the calibration videos for the markerless system took several more hours than the time required to calibrate the markerbased system. As markerless systems continue to develop, the time needed for calibration is likely to decrease. Calibration of the marker-based system took less than five minutes because the calibration algorithm was built into the Vicon software that we used. However, neither system’s calibration likely played a large role in the joint center differences. The next challenge we faced was the large size of the videos that we captured. For each trial, every camera recorded a video that was around one gigabyte in size, 62

resulting in nearly ten gigabytes of data for every trial. These videos needed to be saved to the data collection computers and eventually exported to a readable video file (.avi), so any necessary data transfer steps caused a delay in collecting data and processing the data. Similarly, the markerless data processing steps were time consuming because of the large video file sizes, the complexity of the processing algorithms, and the high resolution of our visual hulls. Each visual hull took between two and four minutes to create, although additional time was required in our processing because of the ground cameras and having to combine the two separate visual hulls into one visual hull. The tracking step took between one and two minutes to track each visual hull. Because each trial was between 40 and 80 frames in length, visual hull creation and tracking were the two most time consuming processing steps in our study. As data transfer rates and processing times continue to increase in speed, the time needed for markerless processing will become less of a burden. Angle Differences As shown in Table 3.2, the maximum knee angle difference was measured in the left leg’s flexion/extension angles at 3.9°. In both of the previously mentioned ACL injury pre-screening tests, a difference of four degrees in knee flexion angle or two degrees in knee adduction angle could determine if a subject is classified as an at-risk athlete and suggested for a re-training program. Similarly, a difference of four degrees in knee flexion angles is likely clinically relevant for OA studies (Lathrop et al., 2011).

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Since the joint centers were directly used to calculate the knee angles, any differences in the joint center calculations would propagate to cause differences in the angle calculations. For example, if the marker-based system determined the hip joint was 20 mm more medial than where the markerless system calculated the hip joint to be, the knee adduction/abduction angle would also have been incorrect. Therefore, using the projection angles method to calculate the difference in joint angles was not necessarily the best indicator of how similar our markerless angle results were to our marker-based angle results. However, our angle differences do give a general idea of how much of a difference to expect in knee angle calculations between the two systems.

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Chapter 5: Conclusion

The current “gold standard” system for human biomechanical research entails collecting data using a marker-based motion capture system with multiple reflective markers placed on the subject. Despite the widespread use of marker-based motion capture, issues with the current systems include the time and expertise required to place the markers, potential for influencing natural movement, and the inability to collect data during game or practice situations. Similarly, the accuracy of these systems has been questioned, as there is no agreed upon accuracy of measured joint center locations or joint angles using the marker-based technique. More recently, markerless motion capture methods have been developed that require only the use of multiple video cameras. Benefits of these systems include the potential to collect subject data during games or practices, the ability to capture a subject’s natural movements, and reducing subject preparation time. Due to these methods being developed only a short time ago, the accuracy of these markerless systems remains relatively unknown. In this study, we quantified the difference between measurements made using a markerless motion capture system and a marker-based motion capture system during two movements that are commonly performed during ACL injury risk studies. Although the lower body joint center differences that we calculated between the markerless and 65

marker-based systems had a maximum average difference of 28 mm, we caution that this study does not quantify the joint center errors relative to the true joint centers. Similarly, even though marker-based techniques are very common in biomechanics research, this study found additional evidence that marker-based results are affected by soft-tissue movement. This soft tissue movement was likely the largest factor in the average joint center differences that we measured. This study shows that markerless motion capture methods have the potential to be useful in biomechanical research, and more specifically, ACL injury studies. With the seemingly low knee joint angle differences (under 4°) and average joint center position differences (under 28 mm) between the two systems, the markerless system was able to calculate similar results to those of the marker-based approach. In the future, another study that includes a “gold standard” method of measuring bone movement is needed to determine the accuracy of both the markerless and the marker-based systems.

Contributions The main contributions of this research are:

Quantified joint center differences between markerless and marker-based motion capture techniques. By simultaneously recording subjects performing two tasks commonly performed during ACL injury research using both markerless and markerbased motion capture systems, differences in lower body joint center calculations were determined between the two systems. These joint center differences were higher than in 66

a previous study that had subjects perform a walking task, but this was likely due to the faster movements and changes in direction. This study was also the first study to determine the suitability for using markerless motion capture to measure fast movements by using both systems to record the movements that are commonly used in ACL injury research.

Quantified knee angle differences between markerless and marker-based motion capture techniques. By using the joint center positions computed by each system, the knee angles were computed for each data set using the projection angles method. The angle differences were low, although these results were likely influenced by the differences in joint centers that we calculated. Since four degrees could possibly be the difference between suggesting an intervention program for an athlete and having the athlete continue their existing landing technique, further testing would likely be required to determine if a markerless system is suitable for use in pre-screening programs.

Provided evidence of soft tissue motion directly affecting marker-based motion capture results. By plotting the eigenvalue percent difference of the marker-based results against the joint center differences, it was determined that the nonrigid motion of the thighs was likely to be a large factor in the joint center differences for the left and right knees during a segment containing 75% of the subjects’ stance phase (i.e. approximately the portion of stance following impact). Soft tissue motion was the largest source of error during marker-based motion capture, especially during dynamic 67

movements, and it seemed to have directly impacted the joint center position differences in our study.

Further investigation should be completed to determine the true accuracy of markerless motion capture methods. Before markerless motion capture techniques are approved as an additional tool in biomechanical research, studies that compare markerless results to more accurate motion capture methods should be completed. These studies could include comparisons to data collected using bone pins or x-rays. Only then will we have a better idea of the true accuracy of the markerless motion capture system.

Additional applications Future studies based on this work could include:

Quantify the error in markerless motion capture techniques by comparing its results to those obtained simultaneously using bone pins or x-ray. As previously mentioned, these results are necessary to evaluate the accuracy of the system when measuring bone movements. By comparing the measurements to those of a marker-based system, this study has provided proof that markerless systems have the potential to be useful in biomechanics research, but questions remain about its true accuracy.

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Develop more robust markerless motion capture tracking techniques. Based on this study, more robust markerless methods could be developed that would especially help track the motion of the feet and hips. In the lower body, from qualitative observations, the foot segments had the largest errors during markerless tracking. This result may have been because the foot segment was composed of very few points when compared with other segments; therefore, the feet had relatively small contributions to the objective function that was minimized during tracking. The hip joint centers were the most inaccurately placed joint center locations by the model generator in this study and one other previous study. Additionally, any future markerless accuracy studies could show which segments have the largest tracking errors, and tracking of these segments could be improved upon.

Continue to investigate error in marker-based measurements caused by soft tissue movement. This study provides additional evidence that soft tissue motion can directly affect marker-based motion capture results. More research into how soft tissue motion affects measurements of dynamic movements should be undertaken, as little quantifiable evidence currently exists on this topic. Future marker-based studies should continue to develop methods to minimize soft tissue error by either developing new marker-based approaches or expanding current techniques.

Develop markerless techniques for use in widespread screening studies. Ultimately, markerless motion capture could play a large role in future ACL injury pre-screening 69

methods. Markerless methods have the potential to be faster during the testing of athletes because no markers are required. These methods also have the potential to be more accurate than marker-based techniques because soft-tissue movement has a smaller affect on markerless results. Before using markerless methods in widespread screening programs, we suggest that researchers ensure markerless methods can predict ACL injury risk with the same accuracy as the marker-based systems that were used to develop the screening programs.

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