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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7.7K Views.  University of Groningen. 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.

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

Electrophysiological Motor Unit Number Estimation (MUNE) Measuring Compound Muscle Action Potential (CMAP) in Mouse Hindlimb Muscles

Compound muscle action potential (CMAP) and motor unit number estimation (MUNE) are electrophysiological techniques that can be used to monitor the functional status of a motor unit pool in vivo. These measures can provide insight into the normal development and degeneration of the neuromuscular system. These measures have clear translational potential because they are routinely applied in diagnostic and clinical human studies. We present electrophysiological techniques similar to those employed in humans to allow recordings of mouse sciatic nerve function. The CMAP response represents the electrophysiological output from a muscle or group of muscles following supramaximal stimulation of a peripheral nerve. MUNE is an electrophysiological technique that is based on modifications of the CMAP response. MUNE is a calculated value that represents the estimated number of motor neurons or axons (motor control input) supplying the muscle or group of muscles being tested. We present methods for recording CMAP responses from the proximal leg muscles using surface recording electrodes following the stimulation of the sciatic nerve in mice. An incremental MUNE technique is described using submaximal stimuli to determine the average single motor unit potential (SMUP) size. MUNE is calculated by dividing the CMAP amplitude (peak-to-peak) by the SMUP amplitude (peak-to-peak). These electrophysiological techniques allow repeated measures in both neonatal and adult mice in such a manner that facilitates rapid analysis and data collection while reducing the number of animals required for experimental testing. Furthermore, these measures are similar to those recorded in human studies allowing more direct comparisons.

Electrophysiological Motor Unit Number Estimation MUNE Measuring Compound Muscle Action Potential CMAP in Mouse Hindlimb Muscles

We present refined protocols that allow in vivo monitoring of motor unit function in the mouse. Techniques to measure compound muscle action potential (CMAP) and motor unit number estimation (MUNE) in the mouse hind limb muscles innervated by the sciatic nerve are described.

Compound muscle action potential (CMAP) and motor unit number estimation (MUNE) are electrophysiological techniques that can be used to monitor the ...

Force and Position Control in Humans - The Role of Augmented Feedback

During motor behaviour, humans interact with the environment by for example manipulating objects and this is only possible because sensory feedback is constantly integrated into the central nervous system and these sensory inputs need to be weighted in order meet the task specific goals. Additional feedback presented as augmented feedback was shown to have an impact on motor control and motor learning. A number of studies investigated whether force or position feedback has an influence on motor control and neural activation. However, as in the previous studies the presentation of the force and position feedback was always identical, a recent study assessed whether not only the content but also the interpretation of the feedback has an influence on the time to fatigue of a sustained submaximal contraction and the (inhibitory) activity of the primary motor cortex using subthreshold transcranial magnetic stimulation. This paper describes one possible way to investigate the influence of the interpretation of feedback on motor behaviour by investigating the time to fatigue of submaximal sustained contractions together with the neuromuscular adaptations that can be investigated using surface EMG. Furthermore, the current protocol also describes how motor cortical (inhibitory) activity can be investigated using subthreshold TMS, a method known to act solely on the cortical level. The results show that when participants interpret the feedback as position feedback, they display a significantly shorter time to fatigue of a submaximal sustained contraction. Furthermore, subjects also displayed an increased inhibitory activity of the primary cortex when they believed to receive position feedback compared when they believed to receive force feedback. Accordingly, the results show that interpretation of feedback results in differences on a behavioural level (time to fatigue) that is also reflected in interpretation-specific differences in the amount of inhibitory M1 activity.

Controlling an identical movement with position or force feedback results in different neural activation and motor behavior. This protocol describes how to investigate behavioral changes by looking at neuromuscular fatigue and how to evaluate motor cortical (inhibitory) activity using subthreshold TMS with respect to the interpretation of augmented feedback.

During motor behaviour, humans interact with the environment by for example manipulating objects and this is only possible because sensory feedback is ...

A Method for Evaluating the Reinforcing Properties of Ethanol in Rats without Water Deprivation, Saccharin Fading or Extended Access Training

Operant oral self-administration methods are commonly used to study the reinforcing properties of ethanol in animals. However, the standard methods require saccharin/sucrose fading, water deprivation and/or extended training to initiate operant responding in rats. This paper describes a novel and efficient method to quickly initiate operant responding for ethanol that is convenient for experimenters and does not require water deprivation or saccharin/sucrose fading, thus eliminating the potential confound of using sweeteners in ethanol operant self-administration studies. With this method, Wistar rats typically acquire and maintain self-administration of a 20% ethanol solution in less than two weeks of training. Furthermore, blood ethanol concentrations and rewards are positively correlated for a 30 min self-administration session. Moreover, naltrexone, an FDA-approved medication for alcohol dependence that has been shown to suppress ethanol self-administration in rodents, dose-dependently decreases alcohol intake and motivation to consume alcohol for rats self-administering 20% ethanol, thus validating the use of this new method to study the reinforcing properties of alcohol in rats.

This protocol describes a novel and efficient method to quickly initiate operant responding for ethanol in rats that, contrary to standard methods, does not require water deprivation or saccharin/sucrose fading to initiate responding.

Operant oral self-administration methods are commonly used to study the reinforcing properties of ethanol in animals. However, the standard methods ...

The Rodent Psychomotor Vigilance Test (rPVT): A Method for Assessing Neurobehavioral Performance in Rats and Mice

The human Psychomotor Vigilance Test (PVT) is a widely used procedure for measuring changes in fatigue and sustained attention. The present article describes a rodent version of the PVT&#8212;termed the &#34;rPVT&#34;&#8212;that measures similar aspects of attention (i.e., performance accuracy, motor speed, premature responding, and lapses in attention). Data are presented that demonstrate both the short- and long-term usefulness of the rPVT when employed with laboratory rats. Rats easily learn the rPVT, and learning to perform the basic procedure takes less than two weeks of training. Once acquired, rat performances in the rPVT show a high degree of similarity to these same performance measures in the human PVT, including similarities in, lapses in attention, reaction times, vigilance decrements across session time (i.e., the human &#34;time-on-task&#34; effects), and the response-stimulus interval (RSI) effect described for humans. Thus the rPVT can be an extremely valuable tool for assessing the effects of a wide range of variables on sustained attention quite similar to human PVT performances, and thus can be useful for developing novel treatments for neurobehavioral dysfunctions.

The Rodent Psychomotor Vigilance Test rPVT: A Method for Assessing Neurobehavioral Performance in Rats and Mice

A rat version of the human Psychomotor Vigilance Test (PVT) is described that measures aspects of attention similar to those measured with the human PVT, including aspects of human vigilance such as performance accuracy, motor speed, premature responding, and lapses in attention.

The human Psychomotor Vigilance Test (PVT) is a widely used procedure for measuring changes in fatigue and sustained attention. The present article ...

Basic Methods for the Study of Reproductive Ecology of Fish in Aquaria

Captive-rearing observations are valuable for revealing aspects of fish behavior and ecology when continuous field investigations are impossible. Here, a series of basic techniques are described to enable observations of the reproductive behavior of a wild-caught gobiid fish, as a model, kept in an aquarium. The method focuses on three steps: collection, transport, and observations of reproductive ecology of a substrate spawner. Essential aspects of live fish collection and transport are (1) preventing injury to the fish, and (2) careful acclimation to the aquarium. Preventing harm through injuries such as scratches or a sudden change of water pressure is imperative when collecting live fish, as any physical damage is likely to negatively affect the survival and later behavior of the fish. Careful acclimation to aquaria decreases the incidence death and mitigates the shock of transport. Observations during captive rearing include (1) the identification of individual fish and (2) monitoring spawned eggs without negative effects to the fish or eggs, thereby enabling detailed investigation of the study species' reproductive ecology. The subcutaneous injection of a visible implant elastomer (VIE) tag is a precise method for the subsequent identification of individual fish, and it can be used with a wide size range of fish, with minimal influence on their survival and behavior. If the study species is a substrate spawner that deposits adhesive eggs, an artificial nest site constructed from polyvinyl chloride (PVC) pipe with the addition of a removable waterproof sheet will facilitate counting and monitoring the eggs, lessening the investigator's influence on the nest-holding and egg-guarding behavior of the fish. Although this basic method entails techniques that are seldom mentioned in detail in research articles, they are fundamental for undertaking experiments that require the captive rearing of a wild fish.

A series of basic methods to enable the study of the reproductive ecology of fish kept in aquaria are described. These are useful protocols for collecting fish using SCUBA, transporting live fish, and observing the reproductive behavior of wild-caught fish kept in aquaria.

Captive-rearing observations are valuable for revealing aspects of fish behavior and ecology when continuous field investigations are impossible. Here, a ...

An Automated Method to Determine the Performance of Drosophila in Response to Temperature Changes in Space and Time

Temperature is a ubiquitous environmental factor that affects how species distribute and behave. Different species of Drosophila fruit flies have specific responses to changing temperatures according to their physiological tolerance and adaptability. Drosophila flies also possess a temperature sensing system that has become fundamental to understanding the neural basis of temperature processing in ectotherms. We present here a temperature-controlled arena that permits fast and precise temperature changes with temporal and spatial control to explore the response of individual flies to changing temperatures. Individual flies are placed in the arena and exposed to pre-programmed temperature challenges, such as uniform gradual increases in temperature to determine reaction norms or spatially distributed temperatures at the same time to determine preferences. Individuals are automatically tracked, allowing the quantification of speed or location preference. This method can be used to rapidly quantify the response over a large range of temperatures to determine temperature performance curves in Drosophila or other insects of similar size. In addition, it can be used for genetic studies to quantify temperature preferences and reactions of mutants or wild-type flies. This method can help uncover the basis of thermal speciation and adaptation, as well as the neural mechanisms behind temperature processing.

An Automated Method to Determine the Performance of Drosophila in Response to Temperature Changes in Space and Time

Here we present a protocol to automatically determine the locomotor performance of Drosophila at changing temperatures using a programmable temperature-controlled arena that produces fast and accurate temperature changes in time and space.

Temperature is a ubiquitous environmental factor that affects how species distribute and behave. Different species of Drosophila fruit flies have specific ...

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Collective animal behavior arises from individual motivations and social interactions that are critical for individual fitness. Fish have long inspired investigations into collective motion, specifically, their ability to integrate environmental and social information across ecological contexts. This demonstration illustrates techniques used for quantifying behavioral responses of fish, in this case, Golden Shiner (Notemigonus crysoleucas), to visual stimuli using computer visualization and digital image analysis. Recent advancements in computer visualization allow for empirical testing in the lab where visual features can be controlled and finely manipulated to isolate the mechanisms of social interactions. The purpose of this method is to isolate visual features that can influence the directional decisions of the individual, whether solitary or with groups. This protocol provides specifics on the physical Y-maze domain, recording equipment, settings and calibrations of the projector and animation, experimental steps and data analyses. These techniques demonstrate that computer animation can elicit biologically-meaningful responses. Moreover, the techniques are easily adaptable to test alternative hypotheses, domains, and species for a broad range of experimental applications. The use of virtual stimuli allows for the reduction and replacement of the number of live animals required, and consequently reduces laboratory overhead.
This demonstration tests the hypothesis that small relative differences in the movement speeds (2 body lengths per second) of virtual conspecifics will improve the speed and accuracy with which shiners follow the directional cues provided by the virtual silhouettes. Results show that shiners directional decisions are significantly affected by increases in the speed of the visual cues, even in the presence of background noise (67% image coherency). In the absence of any motion cues, subjects chose their directions at random. The relationship between decision speed and cue speed was variable and increases in cue speed had a modestly disproportionate influence on directional accuracy.

Here, we present a protocol to demonstrate a behavioral assay that quantifies how alternative visual features, such as motion cues, influence directional decisions in fish. Representative data are presented on the speed and accuracy where Golden Shiner (Notemigonus crysoleucas) follow virtual fish movements.

Collective animal behavior arises from individual motivations and social interactions that are critical for individual fitness. Fish have long inspired ...

An Instrumented Pull Test to Characterize Postural Responses

Impairment of postural reflexes, termed postural instability, is a common and disabling deficit in Parkinson's disease. To assess postural reflexes, clinicians typically employ the pull test to grade corrective responses to a backward perturbation at the shoulders. However, the pull test is prone to issues with reliability and scaling (score/4). Here, we present an instrumented version of the pull test to more precisely quantify postural responses. Akin to the clinical test, pulls are manually administered except pull force is also recorded. Displacements of the trunk and feet are captured by a semi-portable motion tracking system. Raw data represent distance traveled (in millimeter units), making subsequent interpretation and analysis intuitive. The instrumented pull test also detects variabilities influencing pull test administration, such as pull force, thereby identifying and quantifying potential confounds that can be accounted for by statistical techniques. The instrumented pull test could have application in studies seeking to capture early abnormalities in postural responses, track postural instability over time, and detect responses to therapy.

Impairment of postural reflexes, termed postural instability, is difficult to quantify. Clinical assessments such as the pull test suffer issues with reliability and scaling. Here, we present an instrumented version of the pull test to objectively characterize postural responses.

Impairment of postural reflexes, termed postural instability, is a common and disabling deficit in Parkinson's disease. To assess postural reflexes, ...

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has been described. The exercise-induced cTnT response distorts the diagnostic role of the cTnT assay. Although high-intensity interval exercise (HIE) is growing in popularity and concerns remain about its safety, available data related to cTnT release after HIE is limited, which hampers the use of HIE as a health intervention. Here, we present three representative HIE protocols [traditional HIE (repeated 4 min cycling at 90% V&#775;O2max interspersed with 3 min rest, 200 kJ/session); sprint interval exercise (SIE, repeated 1 min cycling at 120% V&#775;O2max interspersed with 1.5 min rest, 200 kJ/session); and repeated sprint exercise (RSE, 40 x 6 s all-out sprints interspersed with 9 s rest)] and one representative MCE protocol (continuous cycling exercise at an intensity of 60% V&#775;O2max, 200 kJ/session). Forty-seven sedentary, overweight young women were randomly assigned to one of four groups (HIE, SIE, RSE, and MCE). Six bouts of respective exercise were performed by every single group, with each being 48 h apart. Meanwhile, for four groups, the duration of the entire testing period was identical, being 10 days. Before and after the first and final exercise bouts, an assessment was conducted of cTnT. The current study provides a frame of reference giving a clear picture of how a specific exercise session affects the circulating cTnT concentration at the early stage of training. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise, especially for HIE.

Here, we present protocols of high-intensity interval and moderate-intensity continuous exercise to observe the response of circulating cardiac troponin T (cTnT) concentration to acute exercise over 10 days. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise.

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has ...

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal  Phase  of 5km Treadmill Running

Running is beneficial for physical health, but it is also accompanied by many injuries. However, the main factors leading to running injury remain unexplained. This study investigated the effects of long running distance on lower-limb kinematic variables and the lower limb kinematic difference of between the initial (IR) and terminal phase (TR) of 5 km running was compared. Ten amateur runners ran on a treadmill at the speed of 10 km/h. Dynamic kinematic data was collected at the phase of IR (0.5 km) and TR (5 km), respectively. The peak angle, peak angular velocities, and range of motion were recorded in this experiment. The main results demonstrated the following: ankle eversion and knee abduction were increased at TR; ROMs of ankle and knee were increased in the frontal plane at TR than IR; a larger peak angular velocity of ankle dorsiflexion and hip interrotation were found in TR compared to IR. These changes during the long distance running may provide some specific details for exploring potential reasons of running injuries.

This study investigated the biomechanical characteristics of the lower extremity kinematic variables between the initial and terminal phase of 5 km treadmill running. The lower-limb kinematic data of 10 runners were collected using a three-dimensional motion capture system on a treadmill at the initial phase (0.5 km) and the terminal phase (5 km), respectively.