The ‘Dancing with Mathematics’ workshop includes a variety of hands-on educational activities that combine these two seemingly incompatible disciplines through motion-capture technologies. A heterogeneous group of researchers with diverse academic backgrounds and expertise, that collaborate in the HORIZON-WIDERA project ‘TransEET’ (Transforming Education with Emerging Technologies) has: first, extended two digital technologies widely used for mathematics education (GeoGebra and MaLT2) with motion-capture technologies for embodied interaction; and then, co-developed the ‘Dancing with Mathematics’ pilot educational activities connecting dance and mathematics for different grades. In the workshop, participants will have the opportunity to engage in an innovative learning experience of using their bodies to express mathematical concepts for creating dancing animations. The workshop aims to collect feedback from the arts-related community to feed the redesign phase of the resources for the main phase of the TransEET project and discuss sustainable ways to support arts integration in the main body of school disciplines.
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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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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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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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Introduction: Falling causes long term disability and can even lead to death. Most falls occur during gait. Therefore improving gait stability might be beneficial for people at risk of falling. Recently arm swing has been shown to influence gait stability. However at present it remains unknown which mode of arm swing creates the most stable gait. Aim: To examine how different modes of arm swing affect gait stability. Method: Ten healthy young male subjects volunteered for this study. All subjects walked with four different arm swing instructions at seven different gait speeds. The Xsens motion capture suit was used to capture gait kinematics. Basic gait parameters, variability and stability measures were calculated. Results: We found an increased stability in the medio-lateral direction with excessive arm swing in comparison to normal arm swing at all gait speeds. Moreover, excessive arm swing increased stability in the anterior–posterior and vertical direction at low gait speeds. Ipsilateral and inphase arm swing did not differ compared to a normal arm swing. Discussion: Excessive arm swing is a promising gait manipulation to improve local dynamic stability. For excessive arm swing in the ML direction there appears to be converging evidence. The effect of excessive arm swing on more clinically relevant groups like the more fall prone elderly or stroke survivors is worth further investigating. Conclusion: Excessive arm swing significantly increases local dynamic stability of human gait.
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Inertial measurement units (IMUs) allow for measurements of kinematic movements outside the laboratory, persevering the athlete-environment relationship. To use IMUs in a sport-specific setting, it is necessary to validate sport-specific movements. The aim of this study was to assess the concurrent validity of the Xsens IMU system by comparing it to the Vicon optoelectronic motion system for lower-limb joint angle measurements during jump-landing and change-of-direction tasks. Ten recreational athletes performed four tasks; single-leg hop and landing, running double-leg vertical jump landing, single-leg deceleration and push off, and sidestep cut, while kinematics were recorded by 17 IMUs (Xsens Technologies B.V.) and eight motion capture cameras (Vicon Motion Systems, Ltd). Validity of lower-body joint kinematics was assessed using measures of agreement (cross-correlation: XCORR) and error (root mean square deviation and amplitude difference). Excellent agreement was found in the sagittal plane for all joints and tasks (XCORR > 0.92). Highly variable agreement was found for knee and ankle in transverse and frontal plane. Relatively high error rates were found in all joints. In conclusion, this study shows that the Xsens IMU system provides highly comparable waveforms of sagittal lower-body joint kinematics in sport-specific movements. Caution is advised interpreting frontal and transverse plane kinematics as between-system agreement highly varied.
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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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Objective: This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors. Design: Prospective study. Subjects: Chronic fall-prone and non-fall-prone stroke survivors. Methods: Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a “fall calendar” and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression. Results: Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64). Conclusion: Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
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Background Objective gait analysis that fully captures the multi-segmental foot movement of a clubfoot may help in early identification of a relapse clubfoot. Unfortunately, this type of objective measure is still lacking in a clinical setting and it is unknown how it relates to clinical assessment. Research question The aim of this study was to identify differences in total gait and foot deviations between clubfoot patients with and without a relapse clubfoot and to evaluate their relationship with clinical status. Methods In this study, Ponseti-treated idiopathic clubfoot patients were included and divided into clubfoot patients with and without a relapse. Objective gait analysis was done resulting in total gait and foot scores and clinical assessment was performed using the Clubfoot Assessment Protocol (CAP). Additionally, a new clubfoot specific foot score, the clubFoot Deviation Index (cFDI*), was calculated to better capture foot kinematics of clubfoot patients. Results Clubfoot patients with a relapse show lower total gait quality (GDI*) and lower clinical status defined by the CAP than clubfoot patients without a relapse. Abnormal cFDI* was found in relapse patients, reflected by differences in corresponding variable scores. Moderate relationships were found for the subdomains of the CAP and total gait and foot quality in all clubfoot patients. Significance A new total foot score was introduced in this study, which was more relevant for the clubfoot population. The use of this new foot score (cFDI*) besides the GDI*, is recommended to identify gait and foot motion deviations. Along with clinical assessment, this will give an overview of the overall status of the complex, multi-segmental aspects of a (relapsed) clubfoot. The relationships found in this study suggest that clinical assessment might be indicative of a deviation in total gait and foot pattern, therefore hinting towards personalised screening for better treatment decision making.
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An important performance determinant in wheelchair sports is the power exchanged between the athletewheelchair combination and the environment, in short, mechanical power. Inertial measurement units (IMUs) might be used to estimate the exchanged mechanical power during wheelchair sports practice. However, to validly apply IMUs for mechanical power assessment in wheelchair sports, a well-founded and unambiguous theoretical framework is required that follows the dynamics of manual wheelchair propulsion. Therefore, this research has two goals. First, to present a theoretical framework that supports the use of IMUs to estimate power output via power balance equations. Second, to demonstrate the use of the IMU-based power estimates during wheelchair propulsion based on experimental data. Mechanical power during straight-line wheelchair propulsion on a treadmill was estimated using a wheel mounted IMU and was subsequently compared to optical motion capture data serving as a reference. IMU-based power was calculated from rolling resistance (estimated from drag tests) and change in kinetic energy (estimated using wheelchair velocity and wheelchair acceleration). The results reveal no significant difference between reference power values and the proposed IMU-based power (1.8% mean difference, N.S.). As the estimated rolling resistance shows a 0.9–1.7% underestimation, over time, IMU-based power will be slightly underestimated as well. To conclude, the theoretical framework and the resulting IMU model seems to provide acceptable estimates of mechanical power during straight-line wheelchair propulsion in wheelchair (sports) practice, and it is an important first step towards feasible power estimations in all wheelchair sports situations.
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