The authors would like to thankfully acknowledge the funding sources provided by the Dutch national research organization (NWO). Furthermore, the authors would like to thankfully acknowledge the Dutch Royal Football Association (KNVB) for facilitating the research programme by giving access to their research facilities. Lastly, the authors would like to thankfully acknowledge Thijs Wiggers for his contribution to the research programme.

All methods described in this section have been approved by the ethical committee of the Center for Human Movement Sciences of the University of Groningen (Register number: 201800904).
1. Field test and inertial measurement unit preparation
<ol>
	<li>Set two cones at least 1 m from each other to determine the start of the field test. 
	NOTE: The 1-m distance between the cones enables the subject to run easily through the starting point of the field test. This distance can be adjusted to the preference of the test leader.</li>
	<li>Determine the end point of the field test by rolling measuring tape from the starting point of the test until a 30-m linear distance has been travelled.</li>
	<li>Set two cones at least 1 m from each other to determine the end point of the field test.</li>
	<li>Prepare the IMUs to get properly attached to the subject&#39;s body. 
	NOTE: See Table of Materials for IMU dimensions and weight characteristics.
	<ol>
		<li>Cut stretch tape into 5 pieces the size of 10 cm x 10 cm.</li>
		<li>Cut double-sided adhesive tape (e.g., toupee tape) into 5 pieces equal to the size of the IMUs that are used.</li>
		<li>Attach a piece of double-sided adhesive tape to each IMU.</li>
		<li>Label each IMU, so that it can be recognized individually during data analysis.</li>
	</ol>
	</li>
</ol>
2. Subject preparation
<ol>
	<li>Obtain information about the subject&#39;s gender, age, body weight, and height. Ask the subject to fill in a questionnaire about their background in team sports. Obtain written informed consent from subjects that meet the inclusion criteria. 
	NOTE: Examples of questions: (i) For how many years do you play soccer? (ii) At which level do you play soccer? (iii) How many hours per week do you have soccer training during the past 6 months? (iv) What is your playing position? (v) Did you experience any pain or did you sustain a musculoskeletal injury at the lower extremity during the past 6 months?</li>
	<li>Determine if the subject meets the inclusion criteria. 
	NOTE: Include subjects when they did not experience any musculoskeletal injuries or pain in the lower extremities in the 6 months before executing the protocol; Subjects should have more than 1-year experience in competing team sports at amateur level.</li>
	<li>Ask the subject to change into sports clothing (e.g., a soccer shirt, soccer shorts and soccer shoes). 
	NOTE: Because sensors will be placed on the tibia, soccer socks are unwanted.</li>
	<li>Prepare the IMUs for attaching to the subject&#39;s body.
	<ol>
		<li>Align all 5 IMUs next to each other.</li>
		<li>Activate all five IMUs at the same time by pushing a button on top of the sensor. The sensor is activated when a green light is blinking. 
		NOTE: From this moment, each IMU samples data at 500 Hz. Data is stored on a SD card internally. Data has to be uploaded to a laptop or computer after the test has been completed.</li>
		<li>Ensure that a mechanical peak has been generated by tapping all IMUs on a hard surface at the same time (e.g., on a table). 
		NOTE: The mechanical peak is needed for synchronizing the IMU signals. Synchronization of the IMU signals is performed during data processing (section 5). This section is not necessary when commercially available sensors are used. In that case, use the corresponding software to synchronize the sensors.</li>
	</ol>
	</li>
	<li>Attach the IMUs to the subject&#39;s body (Figure 1).
	<ol>
		<li>Shave the subject&#39;s body hair at the following anatomical locations: at the sacrum between both posterior superior iliac spines, the anteromedial bony part of both right and left tibia, and the lateral part of both right and left thigh (i.e., tractus illiotibialis). 
		NOTE: The anatomical locations where sensors should be placed can be determined by palpation.</li>
		<li>Spray adhesive spray on the anatomical locations described in step 2.5.1. Wait 5&#8722;10 s to make sure the adhesive spray is dry. 
		NOTE: Hold the spray at least 10 cm (4 inches) away from the skin and spray the desired area with a sweeping motion.</li>
		<li>Remove the protective layer of the double-sided adhesive tape from the IMUs.</li>
		<li>Place the IMU at the described anatomical locations. Write down the anatomical location with the corresponding label of the IMU (e.g., right shank: IMU 1).</li>
		<li>Attach the stretching tape on top of each IMU to make sure the sensor is additionally secured to the skin.</li>
	</ol>
	</li>
</ol>
3. IMU sensor calibration
<ol>
	<li>Instruct the subject to stand still in a neutral position with their feet hip-width apart and their hands in their side. Maintain this position for a minimum period of 5 s.</li>
	<li>Instruct the subject to flex their left hip and knee to a 90&#176; angle in the sagittal plane followed by extending the hip to their neutral position as described in step 3.1. 
	NOTE: For definitions of kinematic variables, see Figure 2.</li>
	<li>Repeat step 3.2 for the right hip and knee.</li>
	<li>Instruct the participant to flex their trunk towards the ground and return to their neutral position.</li>
	<li>Wait for a minimum period of 5 s.</li>
	<li>Repeat steps 3.1&#8722;3.5 once.</li>
</ol>
4. Execute the 30-m linear sprint test
<ol>
	<li>Instruct the subject to perform a warming up (e.g., the soccer specific FIFA 11+ warming up programme20).</li>
	<li>Inform the subject about the test protocol.
	<ol>
		<li>Clearly state that the risk of sustaining an injury during the test is not higher than during normal training and that the subject can abort the test at any time, without giving any reason.</li>
		<li>Instruct the subject to stand in the correct starting position, with their preferred foot standing on the starting line and their shoulders behind the starting line on the field.</li>
		<li>Instruct the subject that the test leader will count down from 3 to 0 followed by shouting &#39;Start&#39;. Instruct that when &#39;Start&#39; has been called, the test begins.</li>
		<li>Inform the subject to sprint as fast as possible until the 30-m end point has been reached. After the 30-m finish point has been reached, the subject has to decelerate as fast as possible to a standstill position.</li>
		<li>Allow the subject to ask questions. If needed, allow the subject to perform a practice run to familiarize the subject with the protocol.</li>
	</ol>
	</li>
	<li>Ask the subject if the instructions were clear.</li>
	<li>Make sure the subject is in the correct starting position.</li>
	<li>Count down from &#39;3&#39; to &#39;0&#39; and shout &#39;Start&#39; to start the test. Start the timer when the start sign has been given.</li>
	<li>Encourage the subject in order to achieve maximal performance.</li>
	<li>Stop the timer when the participant has reached their standstill position.</li>
	<li>Repeat steps 4.4&#8722;4.6 until three sprints have been performed. 
	NOTE: Let participants rest for at least 2 min between trials. Include the fastest sprint for data analysis.</li>
	<li>Instruct the subject to perform a cooling down.</li>
	<li>Detach the IMUs from the subject by removing the stretch tape and double-sided adhesive tape from the subject&#39;s body.</li>
</ol>
5. Data processing
<ol>
	<li>Connect the IMU with a computer by using a USB cable. Export the raw IMU data to a specific folder on the computer.</li>
	<li>Open MATLAB (version r2018b). Import the raw IMU data files (i.e., accelerometer, gyroscope and magnetometer data).</li>
	<li>Synchronize the raw IMU data files.
	<ol>
		<li>Import the acceleration data file of one sensor (e.g., pelvis sensor).</li>
		<li>Calculate jerk by differentiating the X, Y and Z acceleration signals. Sum up the X, Y, and Z jerk to obtain the total jerk.</li>
		<li>Obtain the mechanical peak by finding the index value in the data file where the total jerk has reached its maximal value. The index value is the start of the measurement.</li>
		<li>Delete all data points of the acceleration data, magnetometer data and gyroscope data before the index value of the sensor.</li>
		<li>Repeat steps 5.3.1&#8722;5.3.3 for each raw data file of the corresponding sensor.</li>
		<li>Determine which sensor contains the lowest amount of data points by obtaining the number of sampled data points for each data file.</li>
		<li>Cut all other data files equal to the size of the sensor that registered signals for the shortest time period.</li>
	</ol>
	</li>
	<li>Filter gyroscope data using a second order low-pass Butterworth filter with a cutoff frequency of 12 Hz. 
	NOTE: The filter and particular cutoff frequency were chosen based on visual data inspection in previous pilot experiments.</li>
	<li>Obtain sensor orientation with respect to the global earth frame by calculating the orientation quaternion of the sensor using a Madgwick filter35. 
	NOTE: An extensive description of how sensor orientation with respect to the global earth frame is calculated is described in Madgwick et al.35.</li>
	<li>Align the sensor coordinate frame to body segment.
	<ol>
		<li>Select the index numbers of the data file when the subject was standing still during calibration (step 3.1). 
		NOTE: It is assumed that the longitudinal axis of the sensor is similar to the gravity vector.</li>
		<li>Use the index numbers of step 5.6.1 to calculate the average orientation of each sensor with respect to the global reference frame during the static calibration. Then rotate the sensor frame of each sensor, so that it aligns with the global reference frame during static calibration.</li>
		<li>Select the index numbers of the data file when the calibration movement of the left leg was performed (step 3.2).</li>
		<li>Rotate the orientation of the left leg sensors in such a way that the calibration movement is a rotation about the frontal axis only.</li>
		<li>Repeat steps 5.6.3 and 5.6.4 for the calibration movements of the right leg and trunk.</li>
	</ol>
	</li>
	<li>Obtain joint orientations by expressing the orientation of the distal body segment in the coordinate frame of the proximal segment for each joint.</li>
	<li>Obtain joint angles by decomposing the obtained joint orientations into &#39;XZY&#39; Euler angles. 
	NOTE: How to decompose the obtained joint orientations into &#39;XZY&#39; Euler angles is described in the work of Diebel36.</li>
	<li>Obtain joint angular velocities expressing the gyroscope signals of each distal segment in the coordinate frame of its corresponding proximal segment minus the angular velocity of the proximal segment.</li>
	<li>Identify each step during the linear sprint by using a step detection algorithm.
	<ol>
		<li>Import the filtered gyroscope data in MATLAB.</li>
		<li>Use a peak detection function to identify peaks in the gyroscope signal. 
		NOTE: Peak height was set at 286.5 &#176;&#183;s-1 and minimal peak distance was set at 100 samples (= 0.2 s).</li>
	</ol>
	</li>
	<li>For each step, calculate the maximum value for hip angle, knee angle, hip angular velocity, and knee angular velocity.</li>
	<li>For each step, calculate the minimum value for hip angle, knee angle, hip angular velocity, and knee angular velocity.</li>
	<li>For each step, calculate the hip range of motion by subtracting the minimum hip angle from the maximum hip angle.</li>
	<li>For each step, calculate the knee range of motion by subtracting the minimum knee angle from the maximum knee angle.</li>
	<li>Save the processed data to a specific folder on the computer in order to use it for further analysis.</li>
</ol>
6. Data analysis
<ol>
	<li>Import the processed IMU data in MATLAB.</li>
	<li>Divide the sprint into an acceleration, a top speed and deceleration phase based on the steps identified by the step detection algorithm. 
	NOTE: Sprint phases in this article were chosen arbitrarily. The acceleration phase is defined as step 3 to 837, whereas the deceleration phase is defined as the last eight steps of the sprint. Top speed data was derived from steps performed between these phases.</li>
	<li>Select the angular velocity data for data analysis.</li>
	<li>Calculate the mean values and standard deviation of the kinematic variables of all steps during each phase of the 30-m linear sprint test.</li>
	<li>Repeat step 6.3 and 6.4 for the angle data.</li>
</ol>

The authors have nothing to disclose.

Current methods to monitor athletes in team sports do not register lower extremity kinematics, which could be a useful measure for identifying injury-risk factors. The gold standard for analysing lower extremity kinematics during sprinting are optoelectronic measurement systems29,30,31,32. Although optoelectronic measurement systems serve as a gold standard, these systems lack ecological validity due to their restricted measurement area. The sensor setup presented in this article overcomes the limitations of the current measurement systems, and is relatively cheap. The possibility to register lower extremity kinematics in the field, measured by the sensor setup, may improve athlete monitoring practice.
Previous studies that examined sprint kinematics29,31,37,38,39 reported hip angles ranging from 210&#176; extension to 90&#176; flexion. Furthermore, these studies reported knee angles ranging from 160&#176; minimal flexion and 40&#176; maximal flexion. The values observed in this study are within the range previously reported. One study38 reported hip angular velocities ranging from -590 &#176;&#183;s-1 to 700 &#176;&#183;s-1 and knee angular velocities ranging from -1,000 &#176;&#183;s-1 to 1,100 &#176;&#183;s-1. Although the values observed in this study were higher, they show a similar trend over time. The method has been validated and could be used for athlete monitoring in the field40.
The current study has some limitations that need to be addressed. First, apart from the characteristics of the IMUs that have been used, users must be aware that the signals derived from the IMUs are affected by several sources of error which limit the possible range of applications41. First, the oscillation of the soft tissues around the bones (i.e., soft tissue artefacts42) can affect the registration of kinematics. For this reason, it is important to carefully attach the IMUs to the subject&#39;s body according to the steps described in the protocol. Although necessary steps were undertaken, it should be noted that the current study did not include extra elastic straps to prevent erroneous sensor movement. This could improve results and may be seen as a limitation of this study. Second, ferromagnetic disturbances from other devices (mainly inside buildings) change the magnitude or direction of the measured magnetic field vector of the IMU&#39;s magnetometer, thus causing errors in estimated orientation43. Therefore, sources of ferromagnetic disturbance should be avoided as much as possible. Moreover, it must be noted that the sensor setup is not applicable to sliding tackles since the sensors will detach from the skin as a result of making contact to the ground surface. Thus, participants should be instructed not to perform sliding tackles during small sided games. A possible solution for this problem could be to integrate the sensor setup in smart garments (i.e., a Smart Sensor Tights).
The kinematic variables obtained by the sensor setup could be used in a segmental model in order to monitor athletes in the field. Previous research found reduced maximum combined hip flexion and knee extension angle (i.e., theoretical hamstring length) after each half of a soccer match simulation44. In the same study, an increase in shank angular velocity has been observed during the ends of each half. The lower hamstring length combined with an increased shank velocity may indicate an increased risk of excessive hamstring strain after fatigue. Such alterations in sprinting kinematics may be detected in a field setting using an inertial measurement unit (IMU) driven segmental model. Besides changes in joint kinematics, forces that act on the body as a whole can be estimated as well. Ground reaction forces (GRF) describe the biomechanical loading experienced by the total musculoskeletal system, and can be estimated using Newton&#39;s second law of motion (i.e., F = m &#183; a). Current research in running used GRF estimation to optimize sprint performance45,46&#160;or assessing potential injury risk47,48,49,50. These studies suggest that loading rates, vertical impact force peaks and horizontal breaking force are related to musculoskeletal overuse injuries. Although it is a challenge to estimate GRF accurately during highly dynamic team-sport specific movements51,52, the possibility to monitor these variables during measurements on the field could provide new information to optimize performance, or to prevent injuries.
The results presented in this paper are limited to monitoring lower extremity kinematics during a linear sprint, focussing on hamstring strain injury mechanism. However, it should be noted that hip and groin injuries also occur frequently in team sports14,17,53,54,55. These injuries are probably caused by repetitive involvement of kicking and change of direction. Thus, future research should not only limit their focus on sprinting in relationship with the hamstring strain injury mechanism but also focus on expanding knowledge regarding change of direction tasks56&#160;and kicking57,58,59&#160;in relationship with hip and groin injuries.
To conclude, this sensor setup could be integrated in smart garments. Smart garments may enable to register lower extremity kinematics in the field during team sport specific tasks, which could improve monitoring athletes in the future. This may help professionals in a daily sports setting to evaluate their training programs and optimize them, aiming to reduce injury risk.

Team sports (e.g., soccer and field hockey) are characterized by alternating brief explosive actions such as high-intensity running or sprinting, with longer periods of less demanding activities like walking or jogging1,2,3,4,5,6. Over the last decades, the physical demands of the game evolved with more distance covered at high speed and sprinting, faster ball speeds and more passes7,8.
Athletes constantly train hard in order to maintain and improve their physical capacity to withstand the physical demands of the game. The correct application of a training stimulus in combination with sufficient recovery induces responses that lead to adaptation of the human body, improving fitness and performance9. On the contrary, an imbalance between a training stimulus and recovery can lead to prolonged fatigue and an undesirable training response (maladaptation), which increases the risk of injury in both professional and amateur team sport athletes10,11,12,13.
One of the major risks accompanied with high training and match stimuli are muscle strain injuries. Muscle strain injuries constitute more than a third of all time-loss injuries in team sports and cause more than a quarter of the total injury absence, with the hamstrings being the most frequently involved14,15,16,17. Furthermore, the number of athletes that sustain a hamstring strain injury rises each year18,19, despite that multiple programs have been introduced to prevent hamstring strain injuries12,13,20,21. Consequently, this has a negative influence from sportive22 and financial23 perspectives.&#173; Thus, adequate monitoring of individual athletes is essential for optimizing training schedules, minimizing injury risk and optimizing performance.
Current athlete monitoring practice in team sports is mainly based on position data measured by local or global positioning systems24,25. These systems monitor activity with GPS-based metrics such as distance covered, average running velocity or accelerometry-based metrics such as PlayerLoad26,27,28. A disadvantage of these measures is that they do not include lower extremity kinematics. Optoelectronic measurement systems serve as a gold standard to perform a kinematic analysis of the lower extremities during a linear sprint29,30,31,32. The disadvantages of these systems are a lack of ecological validity due to their restricted measurement area, the need of an expert to operate the system and the time consuming data analysis. Thus, this method is not suitable for daily sports practice.
Rapid development in sensor technology may overcome the limitations of current methods to monitor athletes. The recent reliability, miniaturisation and data storage possibilities of inertial measurement units (IMU) enables in-field application of sensor technology. IMUs contain an accelerometer, gyroscope and magnetometer which measure the acceleration, angular velocity and magnetic field, in three orthogonal axes respectively33,34. With sensors securely fixed to body segments, sensor fusion algorithms and a biomechanical model, it is possible to estimate joint kinematics33. The registration of joint kinematics in combination with information about the acceleration of different body segments may improve athlete monitoring in team sports.
By coupling the IMU sensor setup to a standardized field test, it can be illustrated how lower extremity kinematics are registered during linear sprinting in the field, which could be a useful measure for identifying injury-risk factors. The sensor setup could provide additional information to current monitoring measures that professionals may use for optimizing training schedules to improve performance and minimize injury risk. Therefore, the main purpose of this article is to demonstrate an inertial sensor setup for estimating hip and knee joint kinematics of team sport athletes in the field.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer software</td>
			<td>The MathWorks, Inc., Natick, MA, USA</td>
			<td></td>
			<td>Matlab Version 2018b</td>
		</tr>
		<tr>
			<td>Cones</td>
			<td>Nike</td>
			<td></td>
			<td>n = 4</td>
		</tr>
		<tr>
			<td>Double-sided adhesive tape</td>
			<td></td>
			<td></td>
			<td>For attaching IMUs on the skin</td>
		</tr>
		<tr>
			<td>Inertial Measurement Units</td>
			<td>MPU-9150, Invensense, San Jose, California, United States</td>
			<td></td>
			<td>n = 5; Dimensions: 3.5 x 2.5 x 1.0 cm; Weight: 0,011 kg; Sample frequency: 500Hz; Accelerometer: &#177; 16 G, Gyroscope: &#177; 2000 &#176;/s</td>
		</tr>
		<tr>
			<td>Measuring tape</td>
			<td></td>
			<td></td>
			<td>Minimal length: 30 meters</td>
		</tr>
		<tr>
			<td>Pre-tape spray</td>
			<td>Mueller Tuffner, Mueller Sports Medicine, Inc., Wisconsin, United States</td>
			<td></td>
			<td>Contents: 283 g</td>
		</tr>
		<tr>
			<td>Stretch Tape</td>
			<td>Fixomull, BSN Medical, Almere, The Netherlands</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

an inertial measurement unit based method to estimate hip and knee joint kinematics in team sport athletes on the field

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The disadvantage of these measurement systems is that they do not register lower extremity kinematics, which could be a useful measure for identifying injury-risk factors. Rapid development in sensor technology may overcome the limitations of the current measurement systems. With inertial measurement units (IMUs) securely fixed to body segments, sensor fusion algorithms and a biomechanical model, joint kinematics could be estimated. The main purpose of this article is to demonstrate a sensor setup for estimating hip and knee joint kinematics of team sport athletes in the field. Five male subjects (age 22.5 &#177; 2.1 years; body mass 77.0 &#177; 3.8 kg; height 184.3 &#177; 5.2 cm; training experience 15.3 &#177; 4.8 years) performed a maximal 30-meter linear sprint. Hip and knee joint angles and angular velocities were obtained by five IMUs placed on the pelvis, both thighs and both shanks. Hip angles ranged from 195&#176; (&#177; 8&#176;) extension to 100.5&#176; (&#177; 8&#176;) flexion and knee angles ranged from 168.6&#176; (&#177; 12&#176;) minimal flexion and 62.8&#176; (&#177; 12&#176;) maximal flexion. Furthermore, hip angular velocity ranged between 802.6 &#176;&#183;s-1 (&#177; 192 &#176;&#183;s-1) and -674.9 &#176;&#183;s-1 (&#177; 130 &#176;&#183;s-1). Knee angular velocity ranged between 1155.9 &#176;&#183;s-1 (&#177; 200 &#176;&#183;s-1) and -1208.2 &#176;&#183;s-1 (&#177; 264 &#176;&#183;s-1). The sensor setup has been validated and could provide additional information with regard to athlete monitoring in the field.&#160;This may help professionals in a daily sports setting to evaluate their training programs, aiming to reduce injury and optimize performance.

Five subjects (all male; all soccer players; age 22.5 &#177; 2.1 years; body mass 77.0 &#177; 3.8 kg; height 184.3 &#177; 5.2 cm; training experience 15.3 &#177; 4.8 years) performed a maximal 30-m linear sprint. Hip angles ranged between 100.5&#176; (&#177; 8&#176;) maximal flexion and 183.1&#176; (&#177; 8&#176;) maximal extension during acceleration, 104.1&#176; (&#177; 8&#176;) maximal flexion and 195&#176; (&#177; 8&#176;) maximal extension during top speed, and 128.4&#176; (&#177; 11&#176;) maximal flexion and 171.9&#176; (&#177; 23&#176;) minimal flexion during deceleration. Hip angular velocities ranged between 744.9 &#176;&#183;s-1 (&#177; 154 &#176;&#183;s-1) and &#8211;578 &#176;&#183;s-1 (&#177; 99 &#176;&#183;s-1) during acceleration, 802.6 &#176;&#183;s-1 (&#177; 192 &#176;&#183;s-1) and -674.9 &#176;&#183;s-1 (&#177; 130 &#176;&#183;s-1) during top speed, and 447.7 &#176;&#183;s-1 (&#177; 255 &#176;&#183;s-1) and -430.3 &#176;&#183;s-1 (&#177; 189 &#176;&#183;s-1) during deceleration.
Furthermore, knee angles ranged between 73.5&#176; (&#177; 12&#176;) maximal flexion and 162.6&#176; (&#177; 7&#176;) minimal flexion during acceleration, 62.8&#176; (&#177; 12&#176;) maximal flexion and 164.8&#176; (&#177; 6&#176;) minimal flexion during top speed, and 81.1&#176; (&#177; 16&#176;) maximal flexion and 168.6&#176; (&#177; 12&#176;) minimal flexion during deceleration. Knee angular velocity varied between 935.8 &#176;&#183;s-1 (&#177; 186 &#176;&#183;s-1) and -1137.8 &#176;&#183;s-1 (&#177; 214 &#176;&#183;s-1) during acceleration, between 1155.9 &#176;&#183;s-1 (&#177; 200 &#176;&#183;s-1) and -1208.2 &#176;&#183;s-1 (&#177; 264 &#176;&#183;s-1) during top speed, and 1000.1 &#176;&#183;s-1 (&#177; 282 &#176;&#183;s-1) and -1004.3 &#176;&#183;s-1 (&#177; 324 &#176;&#183;s-1). Figure 3 illustrates continuous kinematic data of one trial of the linear 30-m sprint test, whereas Figure 4 and Figure 5 illustrate kinematic data of one stride cycle during acceleration, top speed and deceleration of one trial.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60857/60857fig1.jpg" /> 
Figure 1: Representation of sensor placement. (A) Sensor placement on the right and left shank. (B) Sensor placement on the pelvis, and right and left thigh. <a href="https://www.jove.com/files/ftp_upload/60857/60857fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60857/60857fig2.jpg" /> 
Figure 2: Definitions for hip and knee joint angles and angular velocities. (A) Representation of the neutral position in the sagittal plane. Joint angles in neutral position are 180&#176;. (B) Representation of hip joint (&#952; hip), knee joint (&#952; knee) and range of motion (ROM). <a href="https://www.jove.com/files/ftp_upload/60857/60857fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60857/60857fig3.jpg" /> 
Figure 3: Visualization of sprint kinematics of one trial during accelerating, top speed and decelerating phase. An asterix indicates when a step has been detected. (A) Left and right hip flexion and extension angles over time. (B) Left and right hip angular velocities over time. (C) Left and right knee angles over time. (D) Left and right knee angular velocities over time. <a href="https://www.jove.com/files/ftp_upload/60857/60857fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60857/60857fig4.jpg" /> 
Figure 4: Polar plot in which hip joint angle (&#176;) and angular velocities (flexion/extension) of one step are illustrated during acceleration, top speed and deceleration. <a href="https://www.jove.com/files/ftp_upload/60857/60857fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60857/60857fig5.jpg" /> 
Figure 5: Polar plot in which knee joint angle (&#176;) and angular velocities (flexion/extension) of one step are illustrated during acceleration, top speed and deceleration. <a href="https://www.jove.com/files/ftp_upload/60857/60857fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Monitoring athletes is essential for improving performance and reducing injury risk in team sports. Current methods to monitor athletes do not include the lower extremities. Attaching multiple inertial measurement units to the lower extremities could improve monitoring athletes in the field.

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The ...

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Video: An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

The purpose of this study was two-fold: 1) demonstrate a technique that can be used to directly estimate the inertial properties of a below-knee prosthesis, and 2) contrast the effects of the proposed technique and that of using intact limb inertial properties on joint kinetic estimates during walking in unilateral, transtibial amputees. An oscillation and reaction board system was validated and shown to be reliable when measuring inertial properties of known geometrical solids. When direct measurements of inertial properties of the prosthesis were used in inverse dynamics modeling of the lower extremity compared with inertial estimates based on an intact shank and foot, joint kinetics at the hip and knee were significantly lower during the swing phase of walking. Differences in joint kinetics during stance, however, were smaller than those observed during swing. Therefore, researchers focusing on the swing phase of walking should consider the impact of prosthesis inertia property estimates on study outcomes. For stance, either one of the two inertial models investigated in our study would likely lead to similar outcomes with an inverse dynamics assessment.

Body segmental inertial properties are required for inverse dynamics modeling. Using an oscillation and reaction board technique, inertial properties of below-knee prostheses were measured. Using direct measures of prosthesis inertia in the inverse dynamics model of the prosthetic leg resulted in lower magnitudes of resultant joint forces and moments.

The purpose of this study was two-fold: 1) demonstrate a technique that can be used to directly estimate the inertial properties of a below-knee ...

Bioengineering

Biomechanical Analysis Methods to Assess Professional Badminton Players' Lunge Performance

Under the condition of simulating a badminton court in the laboratory, this study used the injury mechanism model to analyze the maximal right lunge movements of eight professional badminton players and eight amateur players. The purpose of this protocol is to study the differences in kinematics and joint moment of the right knee and ankle. A motion capture system and force plate were used to capture data of the joint movements of the lower extremity and the vertical ground reaction force (vGRF). Sixteen young men who did not have any sports injuries in the past 6 months took part in the study. The subjects performed a maximal right lunge from the start position with their right foot, stepping on and fully contacting with the force plate, hit the shuttlecock with an underhand stroke to the designated position in the backcourt, and then returned to the start/end position. All subjects wore the same badminton shoes to avoid a difference in impact from different badminton shoes. The amateur players showed a greater range of ankle movement and reverse joint moment on the frontal plane, and a larger internal joint rotation moment on the horizontal plane. The professional badminton players exhibited greater knee moment on the sagittal and frontal planes. Therefore, these factors should be considered in the development of the training program to reduce the risk of sports injuries in knee and ankle joints. This study simulates the real badminton court and calibrates the range of activities of each movement of the subjects so that the subjects complete the experimental action in a natural state with high quality. A limitation of this study is that it does not combine joint load and muscle activity. Another limitation is that the sample size is small and should be expanded in future studies. This research method can be applied to the lower limb biomechanical research of other footwork in the badminton project.

Here, we present a protocol to evaluate the differences in injury mechanisms between professional and amateur players when performing a badminton maximal right lunge movement by analyzing lower limb kinematics.

Under the condition of simulating a badminton court in the laboratory, this study used the injury mechanism model to analyze the maximal right lunge ...

A Novel Application of Musculoskeletal Ultrasound Imaging

Ultrasound is an attractive modality for imaging muscle and tendon motion during dynamic tasks and can provide a complementary methodological approach for biomechanical studies in a clinical or laboratory setting. Towards this goal, methods for quantification of muscle kinematics from ultrasound imagery are being developed based on image processing. The temporal resolution of these methods is typically not sufficient for highly dynamic tasks, such as drop-landing. We propose a new approach that utilizes a Doppler method for quantifying muscle kinematics. We have developed a novel vector tissue Doppler imaging (vTDI) technique that can be used to measure musculoskeletal contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities using ultrasound. The goal of this preliminary study was to investigate the repeatability and potential applicability of the vTDI technique in measuring musculoskeletal velocities during a drop-landing task, in healthy subjects. The vTDI measurements can be performed concurrently with other biomechanical techniques, such as 3D motion capture for joint kinematics and kinetics, electromyography for timing of muscle activation and force plates for ground reaction force. Integration of these complementary techniques could lead to a better understanding of dynamic muscle function and dysfunction underlying the pathogenesis and pathophysiology of musculoskeletal disorders.

We describe a new ultrasound-based vector tissue Doppler imaging technique to measure muscle contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities. This approach provides complementary measurements of dynamic muscle function and could lead to a better understanding of mechanisms underlying musculoskeletal disorders.

Ultrasound is an attractive modality for imaging muscle and tendon  motion during dynamic tasks and can provide a complementary methodological  approach ...

Medicine

Biplanar Videoradiography Dataset for Model-based Pose Estimation Development and New User Training

Measuring the motion of the small foot bones is critical for understanding pathological loss of function. Biplanar videoradiography is well-suited to measure in vivo bone motion, but challenges arise when estimating the rotation and translation (pose) of each bone. The bone's pose is typically estimated with marker- or model-based methods. Marker-based methods are highly accurate but uncommon in vivo due to their invasiveness. Model-based methods are more common but are currently less accurate as they rely on user input and lab-specific algorithms. This work presents a rare in vivo dataset of the calcaneus, talus, and tibia poses, as measured with marker-based methods during running and hopping. A method is included to train users to improve their initial guesses into model-based pose estimation software, using marker-based visual feedback. New operators were able to estimate bone poses within 2&#176; of rotation and 1 mm of translation of the marker-based pose, similar to an expert user of the model-based software, and representing a substantial improvement over previously reported inter-operator variability. Further, this dataset can be used to validate other model-based pose estimation software. Ultimately, sharing this dataset will improve the speed and accuracy with which users can measure bone poses from biplanar videoradiography.

This work presents an in vivo dataset with bone poses estimated with marker-based methods. A method is included here to train operators in improving their initial estimates for model-based pose estimation and reducing inter-operator variability.

Measuring the motion of the small foot bones is critical for understanding pathological loss of function. Biplanar videoradiography is well-suited to ...

Engineering

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Bone stress injuries are common in sports and military trainings. Repetitive large ground impact forces during training could be the cause. It is essential to determine the effect of high ground impact forces on lower-body bone deformation to better understand the mechanisms of bone stress injuries. Conventional strain gauge measurement has been used to study in vivo tibia deformation. This method is associated with limitations including invasiveness of the procedure, involvement of few human subjects, and limited strain data from small bone surface areas. The current study intends to introduce a novel approach to study tibia bone strain under high impact loading conditions. A subject-specific musculoskeletal model was created to represent a healthy male (19 years, 80 kg, 1,800 mm). A flexible finite element tibia model was created based on a computed tomography (CT) scan of the subject&#39;s right tibia. Laboratory motion capture was performed to obtain kinematics and ground reaction forces of drop-landings from different heights (26, 39, 52 cm). Multibody dynamic computer simulations combined with a modal analysis of the flexible tibia were performed to quantify tibia strain during drop-landings. Calculated tibia strain data were in good agreement with previous in vivo studies. It is evident that this non-invasive approach can be applied to study tibia bone strain during high impact activities for a large cohort, which will lead to a better understanding of injury mechanism of tibia stress fractures.

During landing, lower-body bones experience large mechanical loads and are deformed. It is essential to measure bone deformation to better understand the mechanisms of bone stress injuries associated with impacts. A novel approach integrating subject-specific musculoskeletal modeling and finite element analysis is used to measure tibial strain during dynamic movements.

Bone stress injuries are common in sports and military trainings. Repetitive large ground impact forces during training could be the cause. It is ...

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis.
The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject&#39;s height or leg length.
Absolute measurement errors were least during toe off (3.12 &#177; 5.44 degrees) compared to heel strike (5.81 &#177; 5.26 degrees). There were significant (p &#60; 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p &#62; 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle.
The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to ...

Biplanar Videoradiography to Study the Wrist and Distal Radioulnar Joints

Accurate measurement of skeletal kinematics in vivo is essential for understanding normal joint function, the influence of pathology, disease progression, and the effects of treatments. Measurement systems that use skin surface markers to infer skeletal motion have provided important insight into normal and pathological kinematics, however, accurate arthrokinematics cannot be attained using these systems, especially during dynamic activities. In the past two decades, biplanar videoradiography (BVR) systems have enabled many researchers to directly study the skeletal kinematics of the joints during activities of daily living. To implement BVR systems for the distal upper extremity, videoradiographs of the distal radius and the hand are acquired from two calibrated X-ray sources while a subject performs a designated task. Three-dimensional (3D) rigid-body positions are computed from the videoradiographs via a best-fit registrations of 3D model projections onto&#160;to each BVR view. The 3D models are density-based image volumes of the specific bone derived from independently acquired computed-tomography data. Utilizing graphics processor units and high-performance computing systems, this model-based tracking approach is shown to be fast and accurate in evaluating the wrist and distal radioulnar joint biomechanics. In this study, we first summarized the previous studies that have established the submillimeter and subdegree agreement of BVR with an in vitro&#160;optical motion capture system in evaluating the wrist and distal radioulnar joint kinematics. Furthermore, we used BVR to compute the center of rotation behavior of the wrist joint, to evaluate the articulation pattern of the components of the implant upon one another, and to assess the dynamic change of ulnar variance during pronosupination of the forearm. In the future, carpal bones may be captured in greater detail with the addition of flat panel X-ray detectors, more X-ray sources (i.e., multiplanar videoradiography), or advanced computer vision algorithms.

Biplanar videoradiography (BVR) is an advanced imaging technique for understanding the three-dimensional movement of skeletal bones and implants. Combining density-based image volumes and videoradiographs of the distal upper extremity, BVR is used to study the in vivo motion of the wrist and distal radioulnar joint, as well as joint arthroplasties.

Accurate measurement of skeletal kinematics in vivo is essential for understanding normal joint function, the influence of pathology, disease progression, ...

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, multiple ligaments, and approximately 20 muscles. Unfortunately, shoulder pathologies (e.g., rotator cuff tears, joint dislocations, arthritis) are common, resulting in substantial pain, disability, and decreased quality of life. The specific etiology for many of these pathologic conditions is not fully understood, but it is generally accepted that shoulder pathology is often associated with altered joint motion. Unfortunately, measuring shoulder motion with the necessary level of accuracy to investigate motion-based hypotheses is not trivial. However, radiographic-based motion measurement techniques have provided the advancement necessary to investigate motion-based hypotheses and provide a mechanistic understanding of shoulder function. Thus, the purpose of this article is to describe the approaches for measuring shoulder motion using a custom biplanar videoradiography system. The specific objectives of this article are to describe the protocols to acquire biplanar videoradiographic images of the shoulder complex, acquire CT scans, develop 3D bone models, locate anatomical landmarks, track the position and orientation of the humerus, scapula, and torso from the biplanar radiographic images, and calculate the kinematic outcome measures. In addition, the article will describe special considerations unique to the shoulder when measuring joint kinematics using this approach.

Biplane videoradiography can quantify shoulder kinematics with a high degree of accuracy. The protocol described herein was specifically designed to track the scapula, humerus, and the ribs during planar humeral elevation, and outlines the procedures for data collection, processing, and analysis. Unique considerations for data collection are also described.

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, ...

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Several hip pathologies have been attributed to abnormal morphology with an underlying assumption of aberrant biomechanics. However, structure-function relationships at the joint level remain challenging to quantify due to difficulties in accurately measuring dynamic joint motion. The soft tissue artifact errors inherent in optical skin marker motion capture are exacerbated by the depth of the hip joint within the body and the large mass of soft tissue surrounding the joint. Thus, the complex relationship between bone shape and hip joint kinematics is more difficult to study accurately than in other joints. Herein, a protocol incorporating computed tomography (CT) arthrography, three-dimensional (3D) reconstruction of volumetric images, dual fluoroscopy, and optical motion capture to accurately measure the dynamic motion of the hip joint is presented. The technical and clinical studies that have applied dual fluoroscopy to study form-function relationships of the hip using this protocol are summarized, and the specific steps and future considerations for data acquisition, processing, and analysis are described.

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Dual fluoroscopy accurately captures in vivo dynamic motion of human joints, which can be visualized relative to reconstructed anatomy (e.g., arthrokinematics). Herein, a detailed protocol to quantify hip arthrokinematics during weight-bearing activities of daily living is presented, including the integration of dual fluoroscopy with traditional skin marker motion capture.

Several hip pathologies have been attributed to abnormal morphology with an underlying assumption of aberrant biomechanics. However, structure-function ...

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

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