The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (p = 0.007). Cluster 2 showed the lowest knee and hip flexion angles (p < 0.013). Cluster 3 showed the greatest hip external rotation angles (p = 0.006). In F-GAME, Cluster 3 presented the greatest knee external rotation and lowest knee flexion angles (p = 0.003). Clinically relevant differences towards ACL injury identified in the laboratory reflected at-risk patterns only in part when cutting on the field: in the field, low-risk players exhibited similar kinematic patterns as the high-risk players. Therefore, in-lab injury risk screening may lack ecological validity.
MULTIFILE
Wearable inertial sensors (WIS) facilitate the preservation of the athlete-environment relationship by allowing measurement outside the laboratory. WIS systems should be validated for team sports movements before they are used in sports performance and injury prevention research. The aim of the present study was to investigate the concurrent validity of a wearable inertial sensor system in quantifying joint kinematics during team sport movements. Ten recreationally active participants performed change-of-direction (single-leg deceleration and sidestep cut) and jump-landing (single-leg hop, single-leg crossover hop, and double-leg vertical jump) tasks while motion was recorded by nine inertial sensors (Noraxon MyoMotion, Noraxon USA Inc.) and eight motion capture cameras (Vicon Motion Systems Ltd). Validity of lower-extremity joint kinematics was assessed using measures of agreement (cross-correlation: XCORR) and error (root mean square deviation; and amplitude difference). Excellent agreement (XCORR >0.88) was found for sagittal plane kinematics in all joints and tasks. Highly variable agreement was found for frontal and transverse plane kinematics at the hip and ankle. Errors were relatively high in all planes. In conclusion, the WIS system provides valid estimates of sagittal plane joint kinematics in team sport movements. However, researchers should correct for offsets when comparing absolute joint angles between systems.
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This paper describes the work that is done by a group of I3 students at Philips CFT in Eindhoven, Netherlands. I3 is an initiative of Fontys University of Professional Education also located in Eindhoven. The work focuses on the use of computer vision in motion control. Experiments are done with several techniques for object recognition and tracking, and with the guidance of a robot movement by means of computer vision. These experiments involve detection of coloured objects, object detection based on specific features, template matching with automatically generated templates, and interaction of a robot with a physical object that is viewed by a camera mounted on the robot.
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In wheelchair sports, there is an increasing need to monitor mechanical power in the field. When rolling resistance is known, inertial measurement units (IMUs) can be used to determine mechanical power. However, upper body (i.e., trunk) motion affects the mass distribution between the small front and large rear wheels, thus affecting rolling resistance. Therefore, drag tests – which are commonly used to estimate rolling resistance – may not be valid. The aim of this study was to investigate the influence of trunk motion on mechanical power estimates in hand-rim wheelchair propulsion by comparing instantaneous resistance-based power loss with drag test-based power loss. Experiments were performed with no, moderate and full trunk motion during wheelchair propulsion. During these experiments, power loss was determined based on 1) the instantaneous rolling resistance and 2) based on the rolling resistance determined from drag tests (thus neglecting the effects of trunk motion). Results showed that power loss values of the two methods were similar when no trunk motion was present (mean difference [MD] of 0.6 1.6 %). However, drag test-based power loss was underestimated up to −3.3 2.3 % MD when the extent of trunk motion increased (r = 0.85). To conclude, during wheelchair propulsion with active trunk motion, neglecting the effects of trunk motion leads to an underestimated mechanical power of 1 to 6 % when it is estimated with drag test values. Depending on the required accuracy and the amount of trunk motion in the target group, the influence of trunk motion on power estimates should be corrected for.
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In this paper, we present a framework for gamified motor learning through the use of a serious game and high-fidelity motion capture sensors. Our implementation features an Inertial Measurement Unit and a set of Force Plates in order to obtain real-time, high-frequency measurements of patients' core movements and centre of pressure displacement during physical rehabilitation sessions. The aforementioned signals enable two mechanisms, namely a) a game avatar controlled through patient motor skills and b) a rich data stream for post-game motor performance analysis. Our main contribution is a fine-grained processing pipeline for sensor signals, enabling the extraction of a reliable and accurate mapping between patient motor movements, in-game avatar controls and overall motor performance. Moreover, we discuss the potential of this framework towards the implementation of personalised therapeutic sessions and present a pilot study conducted in that direction.
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In wheelchair sports, the use of Inertial Measurement Units (IMUs) has proven to be one of the most accessible ways for ambulatory measurement of wheelchair kinematics. A three-IMU configuration, with one IMU attached to the wheelchair frame and two IMUs on each wheel axle, has previously shown accurate results and is considered optimal for accuracy. Configurations with fewer sensors reduce costs and could enhance usability, but may be less accurate. The aim of this study was to quantify the decline in accuracy for measuring wheelchair kinematics with a stepwise sensor reduction. Ten differently skilled participants performed a series of wheelchair sport specific tests while their performance was simultaneously measured with IMUs and an optical motion capture system which served as reference. Subsequently, both a one-IMU and a two-IMU configuration were validated and the accuracy of the two approaches was compared for linear and angular wheelchair velocity. Results revealed that the one-IMU approach show a mean absolute error (MAE) of 0.10 m/s for absolute linear velocity and a MAE of 8.1◦/s for wheelchair angular velocity when compared with the reference system. The twoIMU approach showed similar differences for absolute linear wheelchair velocity (MAE 0.10 m/s), and smaller differences for angular velocity (MAE 3.0◦/s). Overall, a lower number of IMUs used in the configuration resulted in a lower accuracy of wheelchair kinematics. Based on the results of this study, choices regarding the number of IMUs can be made depending on the aim, required accuracy and resources available.
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Accurate assessment of rolling resistance is important for wheelchair propulsion analyses. However, the commonly used drag and deceleration tests are reported to underestimate rolling resistance up to 6% due to the (neglected) influence of trunk motion. The first aim of this study was to investigate the accuracy of using trunk and wheelchair kinematics to predict the intra-cyclical load distribution, more particularly front wheel loading, during hand-rim wheelchair propulsion. Secondly, the study compared the accuracy of rolling resistance determined from the predicted load distribution with the accuracy of drag test-based rolling resistance. Twenty-five able-bodied participants performed hand-rim wheelchair propulsion on a large motor-driven treadmill. During the treadmill sessions, front wheel load was assessed with load pins to determine the load distribution between the front and rear wheels. Accordingly, a machine learning model was trained to predict front wheel load from kinematic data. Based on two inertial sensors (attached to the trunk and wheelchair) and the machine learning model, front wheel load was predicted with a mean absolute error (MAE) of 3.8% (or 1.8 kg). Rolling resistance determined from the predicted load distribution (MAE: 0.9%, mean error (ME): 0.1%) was more accurate than drag test-based rolling resistance (MAE: 2.5%, ME: −1.3%).
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The Rollz Motion Smart rollator detects posture, gait and activity of a user and provides feedback. • Various programs to train the user and optimize walking performance. • Measuring gait parameters like velocity, step time, step length, distance between person and rollator. • Visual, tactile and auditory cues help the user to take the first step or maintain a suitable walking rhythm.
DOCUMENT
The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (p = 0.007). Cluster 2 showed the lowest knee and hip flexion angles (p < 0.013). Cluster 3 showed the greatest hip external rotation angles (p = 0.006). In F-GAME, Cluster 3 presented the greatest knee external rotation and lowest knee flexion angles (p = 0.003). Clinically relevant differences towards ACL injury identified in the laboratory reflected at-risk patterns only in part when cutting on the field: in the field, low-risk players exhibited similar kinematic patterns as the high-risk players. Therefore, in-lab injury risk screening may lack ecological validity.
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The ‘precision’ in Precision Livestock Farming is not the same as that of precision engineering in high-tech industry. It’s not about micro- and nanometers, but about the precise control of farming management with the aid of sensing and data processing. However, there is overlap in the technological domain, for instance regarding sensors and robots. So, both worlds can learn from each other. That’s why in Den Bosch, mechanical engineering students pursuing a minor in ‘Machines in Motion’ are working on a farming application. Mikroniek offers a sneak preview.
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