Enhancing squat movement classification performance with a gated long-short term memory with transformer network model.

Bodyweight squat is one of the basic sports training exercises. Automatic classification of aberrant squat movements can guide safe and effective bodyweight squat exercise in sports training. This study presents a novel gated long-short term memory with transformer network (GLTN) model for the classification of bodyweight squat movements. Twenty-two healthy young male participants were involved in an experimental study, where they were instructed to perform bodyweight squat in nine different movement patterns, including one acceptable movement defined according to the National Strength and Conditioning Association and eight aberrant movements. Data were acquired from four customised inertial measurement units placed at the thorax, waist, right thigh, and right shank, with a sampling frequency of 200 Hz. The results show that compared to state-of-art deep learning models, our model enhances squat movement classification performance with 96.34% accuracy, 96.31% precision, 96.45% recall, and 96.32% F-score. The proposed model provides a feasible wearable solution to monitoring aberrant squat movements that can facilitate performance and injury risk assessment during sports training. However, this model should not serve as a one-size-fits-all solution, and coaches and practitioners should consider individual's specific needs and training goals when using it.

Automatic temporal event detection of the Ollie movement during skateboarding using wearable IMUs.

The Ollie movement is about the most dangerous fundamental skateboarding skill. This study proposed a peak heuristic algorithm to detect the key temporal events of the Ollie movement during skateboarding using IMUs. The proposed algorithm was used to detect four key temporal events including take-off (TO), peak flight height (HP), front wheel landing (FL), and back wheel landing (RL). Based on these temporal events, three temporal phases including ascending, descending, and flight were identified. The results showed that our proposed method could help accurately identify these key temporal events and phases. Knowledge of the temporal information about the Ollie movement could provide a basis for quantitative assessment of riders' performance and injury risks. Practically, this proposed algorithm can benefit the outdoor injury risk monitoring of the skateboarding movement.