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III INTERNATIONAL CONGRESS ON COMPUTATIONAL BIOENGINEERING M. Cerrolaza, H. Rodrigues, M. Doblaré, J. Ambrosio, M. Viceconti (Eds.) Isla de Margarita, Venezuela, September 17 to 19, 2007

SELECTING BIOMECHANICAL VARIABLES FOR DETECT GAIT EVENTS USING COMPUTATIONAL VISION Daniela Sofia S. Sousa*, João Manuel R. S. Tavares*, Miguel Velhote Correia**, Emília Mendes***, António Veloso****, Vera Silva****, Filipa João**** * INEGI – Instituto de Engenharia Mecânica e Gestão Industrial, Porto, Portugal FEUP – Faculdade de Engenharia da Universidade do Porto, Porto, Portugal e-mail: [email protected], [email protected] ** INEB – Instituto de Engenharia Biomédica / FEUP, Portugal e-mail: [email protected] *** CRPG – Centro de Reabilitação Profissional de Gaia, Arcozelo, Portugal e-mail: emí[email protected] **** FMH – Faculdade de Motricidade Humana, Universidade Técnica de Lisboa, Portugal e-mail: [email protected], [email protected], [email protected]

Keywords: Clinical Gait Analysis, Gait Events, Computational Vision, Biomechanics. Abstract. In clinical gait analysis gait events, such as initial contact and toe off, are needed for biomechanical data normalization and for several temporal/distance parameters estimation. Therefore, there is a major necessity to identify the key gait events accurately. In this paper it will be presented an algorithm to detected stance phase and swing phase events using just visual information acquired by image cameras. The performance of our algorithm was compared with the “golden standard” method (based on data from force platforms) considering normal video digital cameras and high-speed cameras also. 1 INTRODUCTION Usually, in gait analysis the term “gait cycle” designates the time interval between two successive events of walking; typically, between to successive heelstrikes of the right foot. The terminology adopted to divide the gait cycle in sub-phases differs from author to author. In this work it will be used the gait cycle periods adopted in [1] and the time information of the gait phases will be complemented with Perry’s intervals durations [2]. According to [1, 2], the two

Daniela Sofia S. Sousa, João Manuel R. S. Tavares, Miguel Velhote Correia et all

major gait phases are the stance and swing phases. These gait phases can still be divided in loading response, mid-stance, terminal stance, pre-swing and initial swing, mid-swing, and terminal swing, respectively and in the presented order. Seven gait events limited the start and end of the previous sub-phases: initial contact, opposite toe off, heel rise, opposite initial contact, toe off, feet adjacent and tibia vertical. The loading response starts when the observed foot is on the ground (initial contact) and lasts 10% of the gait cycle duration, TGC. The mid-stance starts when the opposite foot leaves the ground (opposite toe off) and corresponds to 20% of TGC. Terminal stance is when the heel begins to lift the ground (heel rise, 20% of TGC). Pre-swing starts when the contralateral foot touches the ground (opposite initial contact, 10% of TGC). Initial swing starts with toe off and lasts 13% of TGC (toe off). Mid-swing is when the two feet are side by side (feet adjacent, 14% of TGC) and terminal swing is when the global angle of the tibia is around 90º (tibia vertical, 13% of TGC). The main purpose of this work is to propose a methodology for gait events detection based on visual data. We are interested in the detection of all gait events: initial contact, opposite toe off, heel rise and opposite initial contact, toe off, feet adjacent and tibia vertical. Finally, our visual-based algorithm, used in this paper for the detection of the initial contact and the toe off, is validated, considering the detection of the start and end of the contact phases, by the usage of the information obtained from force platforms. 2 METHODS Our event detection method was developed to automatically estimate the following events: initial contact (IC), heel rise (HR), toe off (TO), feet adjacent (FA) and tibia vertical (TV). These events are identified through the association of the characteristics points detected on the kinematic curves of the markers used in the gait evaluation and on some main characteristics of the pretended gait events. Thus, IC was found by searching for an absolute minimum in the zcoordinates of the heel marker. The HR event is determined by finding the time when the heel marker start moving in the sagital and frontal planes. The first peak of the difference between forefoot vertical coordinates corresponds to the TO instant. FA is the instant when the progression coordinates of the heel markers assume similar values. Finally, TV event occurs when the angle between the tibia and the plane of the movement is around 90º. A main design requirement of the proposed methodology is the automatic determination of gait events. Moreover, it should be avoided readjusting algorithms parameters. In addition, for different gait patterns, we should be able to apply the proposed algorithm without the need to study the kinematic characteristics associated. Search restrictions and thresholds where added to our algorithm; however, their setups were easily achieved using a generic mechanism full automatic and adaptable to different kinematic data. For the detection of each event desired, the width of the searching window to be used is defined based on the assumption that the peak of the z-coordinate of the heel right marker occurs at the toe off instant (60 % of time cycle) [3], on percentages of the typical gait intervals according to [2] and on the duration of the gait cycle in analysis. Gait cycle duration is defined by the user of our methodology; however, whenever there are more than one gait


cycle acquired, this information can be estimated from the elapse time between two successive maximums of the vertical position that the heel has. Through these assumptions, we normalize the gait data associated, and so we avoid the need of doing manual adjustments. The main objectives of our search restriction are: 1) adjust the event detection to the corresponding gait cycle; 2) respect some additional conditions needed for the detection of a particular event. Pappa’s assumption [3] has an associated error, because TO happens before the instant associated to the highest z-coordinate in the heel marker curve. Empirically, this error was estimated: 6.6% of the duration of the gait cycle in analysis. So, in our algorithm is used a minimum width for the search window of 20% of the cycle duration; 10% before and 10% after the pretended event. It should be noted that, as far are the events from the peak of the z-coordinate of the heel right marker, possible errors related with the previous assumptions have more and more influence. That occurs because the peak of the z-coordinate of heel right marker works as a reference of the searching window used (Figure 1).

Figure 1: Error propagation center in a reference point.

The chosen IC width of the searching window used is from last TO till the next TO. The only restriction is to limit the search to just one IC event in the searching interval. For HR, a pair of thresholds is used to characterize the start of the movement in the sagital and frontal planes.They are defined as equal to the maximums values in the corresponding non-movement phase of those signals (from opposite TO till 10% of the cycle duration). The width of the search window used is chosen from the opposite TO to TO; this interval should contain only one HR event (Figure 2). The first peak of the difference between the forefoot vertical coordinates from HR to PJ corresponds to the toe off instant. The FA search window of our methodology starts on the opposite IC and ends on IC. In FA detection, the width of the searching window should assure that only contains one FA event; because, during the gait cycle, before HR, the opposite foot passes through the observed foot. Finally, for the tibia vertical detection a search restriction for this event is approximately the opposite TO, for each the tibia is also in a vertical position, thus TV event will be between TO and IC. In this study, kinematic and kinetic data was acquired using two experimental setups: a normal speed setup and a high-speed setup. In the normal speed setup, visual data from seven non-disabled subjects (3 men, 4 women, average age of 30 years, average weight of 70 kg and average height of 170 cm) was acquired using four image cameras, working at a sampling frequency of 50 Hz. On the other hand, the ground reaction force (GRF) was obtained using a force platform (Kistler Instruments AG, Winterthur, Switzerland), working a sampling frequency of 1000 Hz. The synchronization between the cameras and the force platform used was done manually.


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Figure 2: Visual detection of the IC (left image) and HR (right image) events; search windows are defined by circles and the desired instant is marked by an asterisk.

The results obtained with our methodology on the gait data acquired using the normal speed setup were compared with the one obtained on data obtained using a high-speed setup. This last setup belongs to the Gait Laboratory of the Faculdade de Motricidade Humana da Universidade Técnica de Lisboa and is composed by four high-speed image cameras, working at a sampling frequency of 100 Hz, that are automatically synchronized with the force platform, also of that laboratory, (Kistler Instruments AG, Winterthur, Switzerland) that works at a sampling frequency of 1000 Hz. In this second study, it was used the gait data of seven normal individuals (7 men, average age of 40 years, average weight of 76 kg and average height of 174 cm). In both experimental setups considered, the walking trials were performed barefoot, in a walk of 6 m length at a self-selected speed, in such a way that the subjects could land their right foot on the force platform reliably. In order to verify how accurate are the detection of the initial contact and toe off events by our algorithm, we compare the events detected with the ones obtained from the data of force platforms. Thus, the IC event is associated with the instant at which the vertical component of the ground reaction force exceeded 10 N and the TO event when the vertical component falls bellow 5 N. The CI and TO instants obtained by our visual algorithm and by the force platform based procedure were then subtracted. Bland and Altman’s method was used as a descriptive statistical comparison between our technique and the “golden standard”. A Friedman’s twoway analysis of variance by ranks was used to determine if there are statistical significant differences between the results obtained by the two methods. Finally, to test the performance of HR, PJ and TV detections the averages of the normalized durations of all gait phases were calculated and the time intervals associated were compared with the ones defined in [1, 2]. 3 RESULTS All cases of DA identification are within 3 frames for the two experimental setups considered and the TO detections are within 1 frame for the setup with high-speed cameras and approximately 6 frames for the setup with normal speed cameras. Bland and Altman’s analysis for the setup with normal speed cameras give an error of -0.083 ± 0.051 s (mean ± standard


deviation) for IC detection, and -0.0019 ± 0.025 s for TO detection. Instead, the setup with high-speed cameras has errors of 0.0073 ± 0.0064 for IC detection and 0.015 ± 0.025 for TO detection. Bland Altman also suggests the order of the events detection. The detection order for both setups do not agree, but as in the setup with high-speed cameras the synchronization was automatic, we would consider it as the best setup to verify the behaviour of our visual algorithm. In addiction, we could suspect that IC and TO events are identified sooner by the force platform based method. On the data of both experimental setups used, no statistical significantly differences (p > 0.05) were found between the results of toe off detection obtained using the force platform base method and our method. The hypothesis that both methods give similar results could not be rejected (H0) by the Friedman’s test. However, the same test rejects H0 for the initial contact detection. In Figure 3 it is represented the averages of the normalised durations of the gait cycle intervals of our samples. Figure 4 presents the gait events instants associated to the vertical component of the ground reaction force of a particular individual whose data was acquired using the normal setup. These two representations permit to compare the performance of our algorithm with the expected intervals durations described by Perry [2]: 10%, 20%, 20%, 10%, 13%, 14%, 13%. Loading response Mid-stance Terminal stance Pre-sw ing Initial sw ing 0%

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Figure 3: Normalized average interval durations obtained using our visual algorithm on data acquired by the normal setup.

4 DISCUSSION The algorithm proposed in this paper has a similar performance as the visual algorithms described in the literature; especially on data acquired using the experimental setup with highspeed cameras. For these setup all the cases considered are within a time gap of 0.015 s (1.4% of the average gait cycle time) for IC detection and 0.055 s (5.1% of the average gait cycle time) for TO detection. Using the data acquired with the normal speed experimental setup all the cases considered are within a time gap of 0.12 s (9.6% of the average gait cycle time) for IC detection and 0.049 s (4% of the average gait cycle time) for TO detection. Ghoussayni [4] reports average differences and absolute average differences within 0.025 s (2.3% of the average gait cycle time) for IC detection and within 0.17 s (15.6% of the average gait cycle time) for TO diction and concluded that his algorithm was sufficiently reliable and accurate.


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Figure 4: All gait events for a particular individual’s gait data acquired with the normal setup; GRF and gait events were normalized as percentages of the gait cycle duration.

This study suggests that the force platform based method can be replaced by our visual algorithm for the detection of the initial contact and toe off, and the other gait events that are usually undetectable by the force platform based methodologies can also be estimated by our algorithm. Moreover, our method that can be applied to others gait data without the need of additional adjustments. We also verify that our algorithm should detect sooner the instants of the initial contact and of toe off. In this work, we found also an indicator of a probable error associated with the normal setup used as, between both setups considered, there was a difference of 0.045 s in the precision of the results obtained by our visual algorithm. 5 ACKNOWLEDGMENTS This work was partially done in the scope of projects: “Avaliação Computacional e Tecnológica Integrada do Desempenho e Funcionalidade dos Cidadãos com Incapacidades Músculo-esqueléticas” (ref. 242/4.2/C/REG, financially supported by Programa Operacional Sociedade do Conhecimento, Portugal) and “Segmentation, Tracking and Motion Analysis of Deformable (2D/3D) Objects using Physical Principles” (ref. POSC/EEASRI/55386/2004, financially supported by Fundação para a Ciência e a Tecnologia, Portugal). 6 REFERENCES [1] M. Whittle, Gait analysis an introduction, Oxford Boston: Butterworth-Heinemann, 2003. [2] J. Perry, Gait analysis: Normal and Pathological Function, Thorofare, NJ: Slack Incorporated, 1992. [3] I. P. I. Pappas, M. R. Popovic, T. Keller, V. Dietz and M. Morari, A reliable gait phase detection system, Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 9, pp. 113-125, 2001. [4] S. Ghoussayni, C. Stevens, S. Durham and D. Ewins, Assessment and validation of a simple automated method for the detection of gait events and intervals, Gait & Posture, vol. 20, pp. 266-272, 2004.