Article: Siamese-based tennis movement gesture assessment via 3D tracking and spatio-temporal scoring Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.36 pp.58 - 71 Abstract: This study proposes SiamAttn-3D + spatio-temporal scoring module (ST-ScoreNet), an end-to-end framework for objective tennis movement assessment. The SiamAttn-3D tracker employs 3D spatio-temporal attention to achieve robust joint localisation (84.6% success rate at >160 km/h racket speeds), overcoming motion blur and occlusion challenges. Joint trajectories feed into ST-ScoreNet, which integrates graph convolutions and bidirectional gated recurrent unit (GRUs) to model biomechanical constraints and temporal dynamics. Evaluated on the Tennis-ITF dataset, the system attains a 92.3% F1-score in stroke assessment (κ = 0.89 vs. coach ratings) - a 6.9% improvement over state-of-the-art methods. Real-time processing at 23 frames per second (FPS) enables instantaneous feedback, reducing hardware costs by 83% compared to sensor-based solutions. Limitations include sensitivity to weather degradation and athlete anthropometrics, with federated learning proposed for future personalisation. Inderscience Publishers - linking academia, business and industry through research

Title: Siamese-based tennis movement gesture assessment via 3D tracking and spatio-temporal scoring

Authors: Qing Miao; Chengzhao Li; Mingfang Wu

Addresses: School of Physical Education, Pingdingshan University, Pingdingshan 467000, China ' School of Physical Education, Pingdingshan University, Pingdingshan 467000, China ' School of Physical Education, Pingdingshan University, Pingdingshan 467000, China

Abstract: This study proposes SiamAttn-3D + spatio-temporal scoring module (ST-ScoreNet), an end-to-end framework for objective tennis movement assessment. The SiamAttn-3D tracker employs 3D spatio-temporal attention to achieve robust joint localisation (84.6% success rate at >160 km/h racket speeds), overcoming motion blur and occlusion challenges. Joint trajectories feed into ST-ScoreNet, which integrates graph convolutions and bidirectional gated recurrent unit (GRUs) to model biomechanical constraints and temporal dynamics. Evaluated on the Tennis-ITF dataset, the system attains a 92.3% F1-score in stroke assessment (κ = 0.89 vs. coach ratings) - a 6.9% improvement over state-of-the-art methods. Real-time processing at 23 frames per second (FPS) enables instantaneous feedback, reducing hardware costs by 83% compared to sensor-based solutions. Limitations include sensitivity to weather degradation and athlete anthropometrics, with federated learning proposed for future personalisation.

Keywords: twin networks; posture evaluation; tennis motion analysis; spatio-temporal modelling.

DOI: 10.1504/IJICT.2025.149052

International Journal of Information and Communication Technology, 2025 Vol.26 No.36, pp.58 - 71

Received: 05 Jul 2025
Accepted: 15 Aug 2025
Published online: 10 Oct 2025 *

Title: Siamese-based tennis movement gesture assessment via 3D tracking and spatio-temporal scoring

Keep up-to-date