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

Authors: Mahela Pandukabhaya, Tharaka Fonseka, Madhumini Kulathunge, Roshan Godaliyadda, Parakrama Ekanayake, Chanaka Senanayake, Vijitha Herath.

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.

Keywords: benchmarking, extended Kalman filter, joint angles, IMU, performance space, psychomotor skills, clustering, table tennis, wearables.

Available at https://doi.org/10.1109/ACCESS.2025.3536837.