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In badminton, accurate service height detection is critical for ensuring fairness. We developed an automated service fault detection system that employed computer vision and machine learning, specifically utilizing the YOLOv5 object detection model. Comprising two cameras and a workstation, our system identifies elements, such as shuttlecocks, rackets, players, and players’ shoes. We developed an algorithm that can pinpoint the shuttlecock hitting event to capture its height information. To assess the accuracy of the new system, we benchmarked the results against a high sample-rate motion capture system and conducted a comparative analysis with eight human judges that used a fixed height service tool in a backhand low service situation. Our findings revealed a substantial enhancement in accuracy compared with human judgement; the system outperformed human judges by 3.5 times, achieving a 58% accuracy rate for detecting service heights between 1.150 and 1.155 m, as opposed to a 16% accuracy rate for humans. The system we have developed offers a highly reliable solution, substantially enhancing the consistency and accuracy of service judgement calls in badminton matches and ensuring fairness in the sport. The system’s development signifies a meaningful step towards leveraging technology for precision and integrity in sports officiation.

This study investigated how midsole hardness of basketball footwear affects lower extremity biomechanics and impacts perception in drop vertical jumps. Eighteen male basketball players performed drop vertical jumps from three heights (31 cm, 46 cm, 61 cm) in basketball shoes of different midsole hardness (50, 60 Asker C). Biomechanical variables of the lower extremity and subjective perception were measured. This study found a significant drop height effect on the lower extremity biomechanics (p < 0.05), with greater ground reaction forces, joint kinetics, and prelanding muscle activation levels observed at higher drop heights. Basketball shoes with a softer midsole led to higher forefoot peak force (p = 0.028) amid lower rearfoot peak force (p = 0.046), lower peak flexion moments at the ankle (p = 0.024) and hip joints (p = 0.029), and greater prelanding muscle activation in the rectus femoris (p = 0.042) and tibialis anterior (p = 0.043). It is concluded that changing midsole hardness within a commercially relevant range triggered a different prelanding muscle activation strategy and hence altered the magnitudes of ground reaction forces and joint loadings during landing. Subjectively, participants perceived higher landing impacts with greater drop heights, though the strength of the associations were weak.