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

Abstract

ABSTRACT 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.