AI-assisted Automatic Jump Detection and Height Estimation in Volleyball Using a Waist-worn IMU
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915279216312320 |
|---|---|
| author | Xu, Weiyi Wang, Chunzhuo Shang, Meng De Bleecker, Camilla Vega, Maria Torres Vanrenterghem, Jos Vanrumste, Bart |
| author_facet | Xu, Weiyi Wang, Chunzhuo Shang, Meng De Bleecker, Camilla Vega, Maria Torres Vanrenterghem, Jos Vanrumste, Bart |
| contents | The physical load of jumps plays a critical role in injury prevention for volleyball players. However, manual video analysis of jump activities is time-intensive and costly, requiring significant effort and expensive hardware setups. The advent of the inertial measurement unit (IMU) and machine learning algorithms offers a convenient and efficient alternative. Despite this, previous research has largely focused on either jump classification or physical load estimation, leaving a gap in integrated solutions. This study aims to present a pipeline to automatically detect jumps and predict heights using data from a waist-worn IMU. The pipeline leverages a Multi-Stage Temporal Convolutional Network (MS-TCN) to detect jump segments in time-series data and classify the specific jump category. Subsequently, jump heights are estimated using three downstream regression machine learning models based on the identified segments. Our method is verified on a dataset comprising 10 players and 337 jumps. Compared to the result of VERT in height estimation (R-squared=-1.53), a commercial device commonly used in jump landing tasks, our method not only accurately identifies jump activities and their specific types (F1-score=0.90) but also demonstrates superior performance in height prediction (R-squared=0.50). This integrated solution offers a promising tool for monitoring physical load and mitigating injury risk in volleyball players. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05907 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-assisted Automatic Jump Detection and Height Estimation in Volleyball Using a Waist-worn IMU Xu, Weiyi Wang, Chunzhuo Shang, Meng De Bleecker, Camilla Vega, Maria Torres Vanrenterghem, Jos Vanrumste, Bart Signal Processing The physical load of jumps plays a critical role in injury prevention for volleyball players. However, manual video analysis of jump activities is time-intensive and costly, requiring significant effort and expensive hardware setups. The advent of the inertial measurement unit (IMU) and machine learning algorithms offers a convenient and efficient alternative. Despite this, previous research has largely focused on either jump classification or physical load estimation, leaving a gap in integrated solutions. This study aims to present a pipeline to automatically detect jumps and predict heights using data from a waist-worn IMU. The pipeline leverages a Multi-Stage Temporal Convolutional Network (MS-TCN) to detect jump segments in time-series data and classify the specific jump category. Subsequently, jump heights are estimated using three downstream regression machine learning models based on the identified segments. Our method is verified on a dataset comprising 10 players and 337 jumps. Compared to the result of VERT in height estimation (R-squared=-1.53), a commercial device commonly used in jump landing tasks, our method not only accurately identifies jump activities and their specific types (F1-score=0.90) but also demonstrates superior performance in height prediction (R-squared=0.50). This integrated solution offers a promising tool for monitoring physical load and mitigating injury risk in volleyball players. |
| title | AI-assisted Automatic Jump Detection and Height Estimation in Volleyball Using a Waist-worn IMU |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2505.05907 |