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Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport | IEEE Journals & Magazine | IEEE Xplore

Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport


This research framework utilizes wearable IMU data to analyze psychomotor skill performance. Extended Kalman filtering is employed to extract Euler angles from the IMU da...

Abstract:

Mastering psychomotor skills, such as those essential in sports, rehabilitation, and professional training, often requires a precise understanding of motion patterns and ...Show More

Abstract:

Mastering psychomotor skills, such as those essential in sports, rehabilitation, and professional training, often requires a precise understanding of motion patterns and performance metrics. This study proposes a versatile framework for optimizing psychomotor learning through human motion analysis. Utilizing a wearable IMU sensor system, the motion trajectories of a given psychomotor task are acquired and then linked to points in a performance space using a predefined set of quality metrics specific to the psychomotor skill. This enables the identification of a benchmark cluster in the performance space, which represents a group of reference points that define optimal performance across multiple criteria, allowing correspondences to be established between the performance clusters and sets of trajectories in the motion space. As a result, common or specific deviations in the performance space can be identified, enabling remedial actions in the motion space to optimize performance. A thorough validation of the proposed framework is done in this paper using a Table Tennis forehand stroke as a case study. The resulting quantitative and visual representation of performance empowers individuals to optimize their skills and achieve peak performance.
This research framework utilizes wearable IMU data to analyze psychomotor skill performance. Extended Kalman filtering is employed to extract Euler angles from the IMU da...
Published in: IEEE Access ( Volume: 13)
Page(s): 25432 - 25445
Date of Publication: 29 January 2025
Electronic ISSN: 2169-3536

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