Obtaining Vital Distances Using Wearable Inertial Measurement Unit

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Obtaining Vital Distances Using Wearable Inertial Measurement Unit for Real-Time, Biomechanical Feedback Training in Hammer-Throw Ye Wang 1 , Hua Li 1 , Bingjun Wan 2 , Xiang Zhang 3 and Gongbing Shan 2,3,4, * 1 2 3 4

*

Department of Mathematics & Computer Science, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; [email protected] (Y.W.); [email protected] (H.L.) School of Physical Education, Shaanxi Normal University, Xian 710119, China; [email protected] Department of Physical Education, Xinzhou Teachers’ University, Shanxi 034000, China; [email protected] Biomechanics Lab, Faculty of Arts & Science, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada Correspondence: [email protected]; Tel.: 1-403-329-2683

Received: 29 October 2018; Accepted: 30 November 2018; Published: 3 December 2018

 

Abstract: The hammer throw is one of the regular track and field competitions, but unlike other events, it has not seen a new world record for over three decades. The standstill may be caused by the lack of scientifically based training. In our previous work, we have developed a wireless/wearable device for the wire tension measurement in order to develop real-time biomechanical feedback training. In this paper, we show the improvement of our wearable system by adding two sensors for tracking of two vital vertical distances. The paper describes the details related to the development of turning an inertial measurement unit into a tracking device for the dynamic distances. Our preliminary data has shown that the dynamic data of the hip and wrist could be used for revealing the coordination between the upper and the lower limbs during a throw. In conjunction with wearable wire-tension measurement, various motor control patterns employed for hammer throwing could be demystified. Such real-time information could be valuable for hammer-throw learning and optimization. Further studies are required to verify the potentials of the wearable system for its efficiency and effectiveness in coaching practice. Keywords: IMU; dynamic tracking; limbs’ coordination; motor control pattern; motor learning

1. Introduction Optimization of any sport skill requires re-organization of limbs coordination responsible for governing the movement performance [1]. This type of motor learning can be enhanced through a number of methods that are utilized in research and application settings alike. In general, verbal feedback of coaches in real-time is commonly used as a preliminary means of instilling motor learning [1,2]. Due to the rapidity and complexity of some sport skills as well as invisibility of some parameters (e.g., force), the real-time feedback of coaches is often a subjective guess based on experience. For increasing the reliability of feedback in training, biomechanical means are used to supplement the verbal instructions [3–6]. The hammer throw is such a sport skill that needs a combination of a coach’s experience and biomechanical feedback in elite sport training to facilitate motor learning and optimize outcomes. Men’s hammer throw has been part of Olympics track and field competitions since 1900, but unlike other events, the hammer throw has not seen a new world record since 1986 [7]. This standstill may be caused by the lack of scientifically based training. While extensive 3D motion analysis technologies do supply highly trustworthy information for human motor skill quantification [8–11], due to their Appl. Sci. 2018, 8, 2470; doi:10.3390/app8122470

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drawbacks, analysisthe andanalysis feedback has traditionally occurred offline afteroffline completion of a given due to their the drawbacks, and feedback has traditionally occurred after completion testing session, i.e., it is post-measurement feedback, rather than real-time [11–14]. The drawbacks of a given testing session, i.e., it is post-measurement feedback, rather than real-time [11–14]. The of a 3D motion system include complicated operation, high cost, long and setup drawbacks of a capture 3D motion capture system include complicated operation, highcalibration cost, long calibration procedures, time-consuming course on data collection, processing, analysis as well as the movement and setup procedures, time-consuming course on data collection, processing, analysis as well as the constrains by induced dozens of attached onattached a subject’s [15]. These movementinduced constrains bycapture dozensmarkers of capture markers onbody a subject’s body drawbacks [15]. These have hindered the use of 3D motion capture systems in sport training practice. As a consequence, drawbacks have hindered the use of 3D motion capture systems in sport training practice. As a research has been initiated develop real-time biomechanical devices for hammer throw consequence, research has to been initiated to develop real-time feedback biomechanical feedback devices for training, with beginning wire-tension measurement [12]. hammer beginning throw training, with wire-tension measurement [12]. Most Most recently, recently, aa pilot pilot study study [16] [16] using using 3D 3D motion motion capture capture technology technology (Figure (Figure 1) 1) found found that that the the timely displacements of hip and wrist may be used to reveal the upper and lower limbs’ coordination timely displacements of hip and wrist may be used to reveal the upper and lower limbs’ coordination when The pilot pilot study study has has shown shown that that the the timely when analyzing analyzing hammer hammer throw. throw. The timely change change of of vertical vertical displacements of hip and wrist are closely related to the turning speed, the ratio of one-leg/two-leg displacements of hip and wrist are closely related to the turning speed, the ratio of one-leg / two-leg support Therefore, support (power (power generation), generation), and and hammer hammer velocity velocity change change during during the the skill skill performance. performance. Therefore, obtaining obtaining the the dynamic dynamic distance distance data data of of these these two two anatomical anatomical landmarks landmarks would would be be vital vital for for real-time real-time feedback training. feedback training.

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Figure 1. The 3D motion capture of hammer throw. throw. (a) The set-up of the data collection; (b) a sample of the 3D data.

The the the two two studies wouldwould suggestsuggest that a combination of the wire-tension The results resultsof of studies that a combination of the measurement wire-tension and the dynamic vertical-displacements of hip and wrist could have great potential for substituting measurement and the dynamic vertical-displacements of hip and wrist could have great potential for 3D motion capture technology in the skill analysis of the hammer throw. Since real-time wire-tension substituting 3D motion capture technology in the skill analysis of the hammer throw. Since real-time is developedis(i.e., already(i.e., wearable) [12], developing wearables for tracking hip and wire-tension developed already wearable) [12], developing wearables for tracking hipwrist and movements would would realize realize the real-time biomechanical feedbackfeedback learning/training in the hammer wrist movements the real-time biomechanical learning/training in the throw. by the results ofresults the studies, are aiming at aiming developing a practicalawearable hammerEncouraged throw. Encouraged by the of thewe studies, we are at developing practical device for pursuing real-time training. This paper will highlight our approach of using the inertial wearable device for pursuing real-time training. This paper will highlight our approach of using the measurement unit (IMUs) as a practical approach for the development of a wearable system for inertial measurement unit (IMUs) as a practical approach for the development of a wearable system biomechanical feedback training in the hammer throw. for biomechanical feedback training in the hammer throw. 2. Materials and Methods 2. Materials and Methods 2.1. Hardware Configuration 2.1. Hardware Configuration The constitution of the hardware in our system is straightforward. Intuitively shown in Figure 2a, constitution of(6DoF) the hardware inand our asystem straightforward. in Figure a six The degree of freedom IMU [17] Teensyis3.2 board [18] wereIntuitively connectedshown with each other. 2a, a six degree of freedom (6DoF) IMU [17] and a Teensy 3.2 board [18] were connected with each We built these on a breadboard as a testing device with an Arduino Mega board in our previous work other. built thesemeasurement on a breadboard a testing devicethere with an Mega board inand ouraprevious for theWe wire-tension [12].as The 6DoF means is aArduino tri-axial accelerometer tri-axial work for the wire-tension measurement [12]. The 6DoF means there is a tri-axial accelerometer gyroscope, which can return the acceleration and the angular speed, respectively, on the X, Y, and Zand axisa tri-axial gyroscope, which can return acceleration and the as angular speed, of respectively, onathe X, of a coordinate system. In other words,the 6DoF can be described the freedom movement of rigid Y, and Z axis of a coordinate system. In other words, 6DoF can be described as the freedom of body in three-dimensional space, which refers to the following: Forward/back (on X axis), left/right movement of a rigid body in three-dimensional space, which refers to the following: Forward/back (on Y axis), up/down (on Z axis), roll (around X axis), pitch (around Y axis), and yaw (around Z axis). (onour X axis), left/right axis), up/down (on Z axis), rollwhich (around axis), pitch (around Y axis), In particular case,(on weYdo not need the magnetometer, canXbe potentially combined withand the yaw (around Z axis). In our particular case, we do not need the magnetometer, which can be potentially combined with the accelerometer and the gyroscope to construct a 9DoF IMU. In our

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accelerometer theisgyroscope a 9DoFwhich IMU. In the IMU is designed as application, theand IMU designed to as construct a combo board, hasour theapplication, accelerometer, ADXL345, and the a combo board, which has the accelerometer, ADXL345, and the gyroscope, TG3200. The Teensy 3.2 gyroscope, TG3200. The Teensy 3.2 board is a breadboard-friendly microcontroller, which can be board is a breadboard-friendly microcontroller, which can be programmed in Arduino IDE (Integrated programmed in Arduino IDE (Integrated Development Environment). Compared to several Arduino Development Environment). Compared to several Arduino boards, the current one is smaller thanUSB the boards, the current one is smaller than the Arduino UNO and Mega boards, and it has its own Arduino UNO and Mega boards, and it has its own USB (Universal Serial Bus) port while Arduino (Universal Serial Bus) port while Arduino Mini does not have one, which makes it relatively easier Mini does not have one, which makes it relatively easier to be programmed. to be programmed.

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Figure 2. 2. (a) (a) Teensy Teensy 3.2 3.2 and and Inertial Inertial Measurement Measurement Unit Unit (IMU) (IMU) connected connectedon onaabreadboard; breadboard;(b) (b)Teensy Teensy Figure 3.2 and and IMU IMU connected connected on a breadboard with motion capture markers. 3.2

In addition, addition,as asshown shownininFigure Figure2b, 2b,we weattached attachedthree three motion capture markers (two 9 mm In motion capture markers (two areare 9 mm in in diameter and one is 5 mm in diameter) on the sensor device for constructing a capture model diameter and one is 5 mm in diameter) on the sensor device for constructing a capture model for 3D for 3D motion a 10-camera VICON MX40capture motionsystem capture(VICON system Motion (VICONSystems, Motion motion capture capture using a using 10-camera VICON MX40 motion Systems, Oxford Metrics Ltd., Oxford, England). The motion capture rate was set at 200 frames/s. Oxford Metrics Ltd., Oxford, England). The motion capture rate was set at 200 frames/s. Calibration Calibration residuals were determined in accordance with VICON’s guidelines and yielded positional residuals were determined in accordance with VICON’s guidelines and yielded positional data data accurate within 1 mm. VICON used help developa atracking trackingalgorithm algorithmof of the the accurate within 1 mm. The The VICON datadata waswas used to to help usus develop IMUs in the vertical direction. IMUs in the vertical direction. 2.2. Methodology Methodology 2.2. First, we weneeded neededtotoconfigure configure IMU in Arduino an Arduino sketch program by applying the First, thethe IMU in an sketch program by applying the Wire Wire library [19], which allowed communication with I2C (Inter-Integrated circuit) devices. library [19], which allowed communication with I2C (Inter-Integrated circuit) devices. This This configuration program was uploaded the Teensy 3.2 microcontroller. It wastoused to the set configuration program was uploaded to the to Teensy 3.2 microcontroller. It was used set up up the accelerometer and the gyroscope. We set the data format register of the accelerometer to 0 × 09, accelerometer and the gyroscope. We set the data format register of the accelerometer to 0 × 09, which which could set the acceleration range from − 4 g (1 g = 9.8 m/s2) to +4 g. According to the datasheet could set the acceleration range from −4 g (1 g = 9.8 m/s2) to +4 g. According to the datasheet of of ADXL345 [20], it sets device a resolution full resolution mode, where the output resolution increases ADXL345 [20], it sets the the device to a to full mode, where the output resolution increases with with the g range set by the range bits to maintain a 3.9 mg/LSB scale factor. This setting information is the g range set by the range bits to maintain a 3.9 mg/LSB scale factor. This setting information is useful for for converting converting the the unit unit of of the the raw raw output output data We set 08 useful data to to g. g. We set the the power power control control register register to to 00 × × 08 to make the accelerometer in a measurement mode. Should we want to have the minimum power to make the accelerometer in a measurement mode. Should we want to have the minimum power consumption, we thisthis register to 0 to × 00 make in a standby Similarly, configured consumption, wecould couldsetset register 0 to × 00 to itmake it in a mode. standby mode. we Similarly, we the ITG3200 gyroscope’s settings in this program according to its datasheet [21]. The range of rotation configured the ITG3200 gyroscope’s settings in this program according to its datasheet [21]. The range speed was speed set from -2000 (dps) to +2000 degree/second, which waswhich a full-scale of rotation was set degree/second from -2000 degree/second (dps) to +2000 degree/second, was arange. fullThe sensitivity was 14.375 LSB, which was also useful for converting the unit of the raw output scale range. The sensitivity was 14.375 LSB, which was also useful for converting the unit of the data raw to dps. The low pass filter bandwidth was set to 98 Hz, and its sampling rate was set to 100 output data to dps. The low pass filter bandwidth was set to 98 Hz, and its sampling rate was setHz. to In addition, we implemented the functions for reading and outputting the acceleration values relative 100 Hz. In addition, we implemented the functions for reading and outputting the acceleration values to the earth and the and angular speed values the Arduino sketch program. relative to the earth the angular speedinvalues in the Arduino sketch program. The next step was to do some calibration work. We applied the sensitivity sensitivity values, values, 256 256 LSB LSB and and The next step was to do some calibration work. We applied the 14.375 LSB, to convert the units of the raw output of accelerometer and the raw output of gyroscope to 14.375 LSB, to convert the units of the raw output of accelerometer and the raw output of gyroscope

to g and dps, respectively. These values can be found in the datasheets of the sensors. We implemented a function of calculating the offsets (chip specific) of the first 200 samples in the Arduino sketch program.

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Appl. Sci.dps, 2018,respectively. 8, x g and

4 of 9a These values can be found in the datasheets of the sensors. We implemented function of calculating the offsets (chip specific) of the first 200 samples in the Arduino sketch program. Finally, we needed neededtotoapply applyanan algorithm predict orientation. Kalman-based filters Finally, we algorithm to to predict thethe orientation. Kalman-based filters havehave been been widely used in orientation estimation [22]. In the beginning, we tried to implement a widely used in orientation estimation [22]. In the beginning, we tried to implement a complementary complementary Kalman-based algorithm. However, the result of the orientation estimation was bad Kalman-based algorithm. However, the result of the orientation estimation was bad due to a drifting due a kept drifting error in that kept occurring in calculating the velocities. Figure 3 data displays the errortothat occurring calculating the velocities. Figure 3 displays the acceleration obtained acceleration data obtained from the IMU sensor and the corresponding velocity data, which was from the IMU sensor and the corresponding velocity data, which was calculated by the complementary calculated by thefilter. complementary filter. Obviously, the velocity cannot Kalman-based Obviously, Kalman-based the velocity data cannot come back to zerodata at the end come of theback test, to zero isatknown the endasofa the test, error. whichThen, is known as a drifting error. Then,other we started looking some which drifting we started looking for some ways to get thefor accurate other ways toMadgwick’s get the accurate orientation. Madgwick’s [23]MARG is also (magnetic, known as Madgwick’s orientation. algorithm [23] is also known asalgorithm Madgwick’s angular rate, MARG (magnetic, angular rate, and gravity) filter or AHRS (attitude and heading reference systems) and gravity) filter or AHRS (attitude and heading reference systems) algorithm. As Madgwick algorithm. As Madgwick mentions in his work, Kalman-based filters are difficult to implement mentions in his work, Kalman-based filters are difficult to implement because they may require because they may sampling ratesbandwidth. far exceeding thesensor subject bandwidth. Our low sensor device rate, has sampling rates farrequire exceeding the subject Our device has a fairly sampling awhich fairlyis low rate, which is only 50 reasons Hz. This probably one the of the reasons thaterror. we only sampling 50 Hz. This is probably one of the thatiswe experienced velocity drifting experienced the velocity drifting error. As Madgwick claims in his study, his algorithm can be As Madgwick claims in his study, his algorithm can be effective even at lower sampling rates, like 10 Hz. effective even at lower sampling rates, like 10 Hz. In addition, Madgwick compares the performance In addition, Madgwick compares the performance of his algorithm with a Kalman-based filter in his of his algorithm with a indicate Kalman-based filter in has his study, andbetter the results indicate his algorithm has a study, and the results his algorithm a slightly accuracy. Therefore, we decided slightly better accuracy. Therefore, we decided to apply Madgwick’s algorithm. We tried two to apply Madgwick’s algorithm. We tried two versions of his filters to predict the orientation in versions of his filters to predict the orientation in MATLAB R2017a. MATLAB R2017a.

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Figure Figure3. 3.(a) (a)The Theacceleration accelerationdata dataobtained obtainedfrom fromthe theIMU IMUsensor; sensor;(b) (b)the thecorresponding correspondingvelocity velocitydata, data, which was was calculated calculated by by the the complementary complementary Kalman-based filter. which

2.3. Our OurPrototype Prototype and and In-Field In-Field Test Test 2.3. After the the development development of of the the wearables wearables for for vertical vertical distance distance monitoring, monitoring, we we built built our our prototype prototype After for the thereal-time real-time biomechanical biomechanical feedback feedback training training of of the thehammer hammerthrow. throw. The The prototype prototype integrated integrated the the for two newly developed distance sensors into our Arduino Mega board developed in our previous work two newly developed distance sensors into our Arduino Mega board developed in our previous work for wire-tension wire-tension monitoring monitoring during during the the hammer hammer throw throw [12], [12], increasing increasing the the device devicecapacity capacity to tomonitor monitor for three key variables in real time. The two new distance sensors were buried into a self-made belt three key variables in real time. The two new distance sensors were buried into a self-made beltand anda self-made armband, attached to the waist and right wrist, respectively. a self-made armband, attached to the waist and right wrist, respectively. Thenew newdevice devicewas was tested field. A varsity-level athlete 25 81 years, 81 kg, 1.75 The tested in in thethe field. A varsity-level athlete (male,(male, 25 years, kg, 1.75 m with m with seven years training experience) tried out the real-time feedback device. Our wearable seven years training experience) tried out the real-time feedback device. Our wearable device device permitted considerable freedom of movement for the subject with negligibleinfluence influenceon on his his permitted considerable freedom of movement for the subject with negligible performance. Taking Taking advantage advantage of of this, this, we we placed placed no no restrictions restrictions on on the the subject’s subject’s movements movements during during performance. the in-field test to preserve his normal “control style”. Four trials were done. the in-field test to preserve his normal “control style”. Four trials were done. 3. Results and Discussion 3. Results and Discussion The result of the test by applying Madgwick’s filter is satisfied. During a test, we moved our The result of the test by applying Madgwick’s filter is satisfied. During a test, we moved our system device up and down three times. As shown in Figure 4, we got relatively accurate feedback of system device up and down three times. As shown in Figure 4, we got relatively accurate feedback the 3D positioning data. Indeed, the Madgwick algorithm eliminates the drifting error from integrating of the 3D positioning data. Indeed, the Madgwick algorithm eliminates the drifting error from the velocities. The three different curves stand for the changing distances over time on the X axis, integrating the velocities. The three different curves stand for the changing distances over time on Y axis, and Z axis in a 3D space. The dynamic distance on the Z axis (blue lines) shows exactly three the X axis, Y axis, and Z axis in a 3D space. The dynamic distance on the Z axis (blue lines) shows times up and down of the device. The range of vertical movements is ~0.33 m for the first moving-up exactly three times up and down of the device. The range of vertical movements is ~0.33 m for the first moving-up and down, ~29 cm for the second circle, and ~32 cm for the last one, respectively. The next step is to validate the accuracy of the device.

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and down, ~29 cm for the second circle, and ~32 cm for the last one, respectively. The next step is to Appl. Sci. 2018, 8, x 5 of 9 validate the accuracy of the device.

Figure 4. The 3D positioning data obtained by the IMU with the Madgwick filter.

It is well known that 3D motion capture technology provides accurate and objective analysis of a variety of human motor skills [14,24–27]. Therefore, we employed the synchronized data collection of the IMU and 3D motion capture (Figure 1b) for validating and improving the accuracy of the IMU device. There were eight synchronized tests performed to obtain thousands of data for our validation. Since we aimed to gain the dynamic vertical distance, the validation of the Z axis was selected. Figure 5 shows a typical test data. The synchronized data demonstrate a matching vertical excursion over time betweenFigure the IMU data and the accurate 3D motion capture data. The resultsfilter. suggest that our Figure 4. 4. The The 3D 3D positioning positioning data data obtained obtained by by the the IMU IMU with with the the Madgwick Madgwickfilter. device works principally. ItAismagnitude well known that 3D motion capture technology accurate andwas objective of of a comparison shows that the excursionprovides of the VICON data largeranalysis than that It is well known that 3D motion capture technology provides accurate and objective analysis of variety of human motor skills [14,24–27]. Therefore, we employed the synchronized data collection of the IMU data (Figure 5). Askills timely comparison between synchronized data of all data trialscollection revealed a variety of human motor [14,24–27]. Therefore, wethe employed the synchronized the IMU andexcursions 3D motionran capture (Figure 1b) for validating and improving the accuracy of the IMU that the two in a quasi-parallel way, which suggested that we could apply a factor for of the IMU and 3D motion capture (Figure 1b) for validating and improving the accuracy of the IMU device. There were eight synchronized tests performed to obtain thousands of data forthe ourquantitative validation. re-calibrating the IMU device to improve the accuracy of the IMU data. After device. There were eight synchronized tests performed to obtain thousands of data for our validation. Since we aimed to gainthe the dynamic verticalofdistance, validation of the Z axis selected. Figure 5 comparison between excursions all trials,the a re-calibration of was 1.31 determined. Since we aimed to gain thetwo dynamic vertical distance, the validation offactor the Z axis waswas selected. Figure shows a typical test data. The synchronized data demonstrate a matching vertical excursion over time After the simple re-calibration, a renewed synchronized measurement was done and the result is 5 shows a typical test data. The synchronized data demonstrate a matching vertical excursion over between the IMU 6. data and thethe accurate 3Ddata motion capture data. The results suggest that our device shown in Figure This time, average error of our IMU data decreases to under 6%, which time between the IMU data and the accurate 3D motion capture data. The results suggest that our works principally. is accurate enough for sport skills analysis using the biomechanical modeling method [28–32]. device works principally. A magnitude comparison shows that the excursion of the VICON data was larger than that of the IMU data (Figure 5). A timely comparison between the synchronized data of all trials revealed that the two excursions ran in a quasi-parallel way, which suggested that we could apply a factor for re-calibrating the IMU device to improve the accuracy of the IMU data. After the quantitative comparison between the two excursions of all trials, a re-calibration factor of 1.31 was determined. After the simple re-calibration, a renewed synchronized measurement was done and the result is shown in Figure 6. This time, the average data error of our IMU data decreases to under 6%, which is accurate enough for sport skills analysis using the biomechanical modeling method [28–32].

Figure Figure5. 5. A A synchronized synchronized test test data data obtained obtained from from 3D 3D motion motion capture capture (VICON (VICONdata, data, top, top, sampling sampling rate rate 200 200 Hz) Hz)and andour ourIMU IMUdevice devicewithout withoutcalibration calibration(IMU (IMUdata, data,bottom, bottom,sampling samplingrate rate50 50Hz). Hz).

A magnitude comparison shows that the excursion of the VICON data was larger than that of the IMU data (Figure 5). A timely comparison between the synchronized data of all trials revealed that the two excursions ran in a quasi-parallel way, which suggested that we could apply a factor for re-calibrating the IMU device to improve the accuracy of the IMU data. After the quantitative comparison between the two excursions of all trials, a re-calibration factor of 1.31 was determined. Figure 5. A synchronized test data obtained from 3D motion capture (VICON data, top, sampling rate 200 Hz) and our IMU device without calibration (IMU data, bottom, sampling rate 50 Hz).

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After the simple re-calibration, a renewed synchronized measurement was done and the result is shown in Figure 6. This time, the average data error of our IMU data decreases to under 6%, which is Appl. Sci. Sci. 2018, 8, x 8, x for sport skills analysis using the biomechanical modeling method [28–32]. 6 of6 9of 9 accurate enough Appl. 2018,

Figure AA renewed synchronized data obtained from motion capture (VICON data, top, Figure 6. 6. A6.renewed synchronized testtest data obtained from 3D3D motion capture (VICON data, top, Figure renewed synchronized test data obtained from 3D motion capture (VICON data, top, sampling rate 200 Hz) and our IMU device after calibration (IMU data, bottom, sampling rate 50 Hz). sampling raterate 200200 Hz)Hz) andand ourour IMU device after calibration (IMU data, bottom, sampling raterate 50 Hz). sampling IMU device after calibration (IMU data, bottom, sampling 50 Hz).

Finally, ititshould that device needs an initial initial value forapplication. itsapplication. application. Asshown shown Finally, it should be be noted that ourour device needs an initial value for for its As As shown in in Finally, should be noted noted that our device needs an value its in Figure 6, the device will start at zero regardless of its actual vertical position. Therefore, for Figure 6, the device will start at zero regardless of its actual vertical position. Therefore, for its Figure 6, the device will start at zero regardless of its actual vertical position. Therefore, for its its application hammer throw, feedback needs initial heights and wrist application in in the hammer throw, anan accurate feedback needs thethe initial heights of of the hiphip and wrist application inthe the hammer throw, anaccurate accurate feedback needs the initial heights ofthe the hip and wrist and H asshown shown inFigure Figure 7. (H(H hip and Hwrist )wrist as))shown in Figure 7. 7. (Hhip hip and Hwrist as in

Figure 7. The upper and lower limb coordination (i.e., motor control pattern) revealed by the vertical Figure 7. The upper andand lower limb coordination (i.e., motor control pattern) revealed by the vertical Figure 7.of The lower coordination motor control pattern) by the vertical distances hipupper and wrist as welllimb as the wire tension(i.e., during a hammer throw byrevealed a college-level athlete. distances of hip and wrist as well as the wire tension during a hammer throw by a college-level distances of hip and wrist as well as the wire tension during a hammer throw by a college-level athlete. athlete.

TheThe in-field testtest on on thethe college-level athlete using ourour prototype device confirms thethe potential of of in-field college-level athlete using prototype device confirms potential using wire-tension and IMUs in real-time feedback training (Figure 7). In practice, the motor control using wire-tension and IMUs in real-time feedback training (Figure 7). In practice, the motor control of the hammer throw could be divided intointo four phases: Initiation, transition, turns, andand throw. TheThe of the hammer throw could be divided four phases: Initiation, transition, turns, throw.

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The in-field test on the college-level athlete using our prototype device confirms the potential of using wire-tension and IMUs in real-time feedback training (Figure 7). In practice, the motor control of the hammer throw could be divided into four phases: Initiation, transition, turns, and throw. The goal of the initiation phase is to launch the circulation of the hammer around the body. It commonly consists of a forward and backward swing of the hammer (i.e., to set the hammer to motion) and two over-head arm rotations (i.e., to set the hammer into rotation). The transition phase aims to switch the body from standing posture to the first body rotation, building a rotating system of the body and the hammer. The phase of turns accelerates the rotating system of the body and the hammer to their highest circulation. The final phase is the throwing. Our data has revealed the following motor control information: (1) During the transition phase, the upper and lower limbs’ controls are transferring from an unclear coordination pattern to a quasi-out-of-phase coordination in the phase of turns (Figure 7). (2) The transition phase helps the power generation (i.e., wire tension) become in phase (quasi) with the hips’ up-and-down movement, indicating the hammer acceleration depends on the timely flexion/extension of lower limbs. (3) Finally, the characteristic of quasi-out-of-phase between the arm control and wire tension finishes in the transition phase. Would such characteristics appear at different levels of athletes? How can the real-time feedback (i.e., wearable devices) be helpful in optimization of individual hammer-throw skills? Are there additional potentials of wearables in the learning and training of the hammer throw? Future studies are needed to answer the above application questions. 4. Conclusions In conclusion, we used IMUs to build a wearable sensing system to determine the dynamic vertical distances of the hip and wrist during hammer throws. The dynamic data could play a vital role in skill optimization, as they could be used to reveal the coordination between upper and lower limbs. In conjunction with wearable wire-tension measurement, various motor control patterns during the hammer throw could be demystified. Hence, such a wearable system could realize the real-time biomechanical feedback training for the hammer throw. Such an approach has a great potential to become a coach-friendly tool for effectively learning and/or training in practice. In short, our device could make three potential contributions to hammer throw learning and/or training: (1) Making scientific monitoring from a lab-based environment to in field, (2) simplifying a scientific quantification from using a complicated motion capture system to easily-applied wearables, and (3) transferring the biomechanical feedback training from a post-measurement one to a real-time one. Further studies are required to verify the potentials. Author Contributions: Y.W. designed, prototyped, programmed the wearable system, and tested its performance; B.W., X.Z. and G.S. analyzed and interpreted the data; G.S. and H.L. proposed the architecture and improved the design; Y.W., H.L. and G.S. prepared the draft; all authors contributed to the revisions and proof reading of the article. Funding: This research was funded by National Sciences and Engineering Research Council of Canada (NSERC), grant number RGPIN-2014-03648. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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