2024/11/25 by Mahela Pandukabhaya, Pandukabhaya, Mahela, Tharaka Fonseka +11 · 1 citation
Computer Science · Health Professions · Medicine · #FOS: Electrical engineering #Signal Processing (eess.SP) #Sports and Physical Education Research #Stroke Rehabilitation and Recovery #Virtual Reality Applications and Impacts #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2411.16168
openalex publication_date 2024/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.