Assessment of 1m 105c Springboard Dive by Machine Learning Image Processing and an Inertial Measurement Unit
Accurate assessment of athletes' movement patterns is essential for supporting coaches in evaluating sport-specific motor tasks and identifying technical flaws, improving performance. In this study, we introduce a system that integrates high-frame-rate video analysis, machine learning algorithms, and inertial measurement units (IMUs) to enable precise estimation of human body pose during one-meter springboard diving, specifically focusing on the forward 2½ somersault in tuck position dive (Code 105c).
Authors
- Lucio Caprioli (ORCID: https://orcid.org/0009-0005-4049-5225)
- Vincenzo Bonaiuto (ORCID: https://orcid.org/0000-0002-2328-4793)
- Andrea Zanela (ORCID: https://orcid.org/0000-0001-5606-4175)
- Aatheethyaa Dhanasekaran (ORCID: https://orcid.org/0009-0006-1493-851X)
- Manuele Scifoni
- Flavio Centurioni
Institutions
- National Agency for New Technologies, Energy and Sustainable Economic Development (IT)
- University of Rome Tor Vergata (IT)
- Engineering (Italy) (IT)
Publication Details
- Published
- 2025-10-29
- DOI
- https://doi.org/10.1109/star66750.2025.11264801
- Primary Topic
- Sports Performance and Training
- Type
- article
- Field-Weighted Citation Impact
- 0.00