Shang, MengMengShangde Bleecker, CamillaCamillade BleeckerVanrenterghem, JosJosVanrenterghemde Ridder, RoelRoelde RidderVerschueren, SabineSabineVerschuerenVaron, CarolinaCarolinaVaronDe Raedt, WalterWalterDe RaedtVanrumste, BartBartVanrumste2025-04-032025-04-0320252169-3536WOS:001446493800008https://imec-publications.be/handle/20.500.12860/45490Jump monitoring for volleyball players during training or a match can be crucial to prevent injuries, yet the measurement requires considerable workload and cost using traditional methods such as video analysis. Also, existing methods do not provide accurate differentiation between different types of jumps. In this study, an unobtrusive system with a single inertial measurement unit (IMU) on the waist was proposed to recognize the types of volleyball jumps. A Multi-Layer Temporal Convolutional Network (MS-TCN) was applied for sequence-to-sequence (seq-to-seq) classification without using the sliding window technique. The model was evaluated on volleyball players during a lab session with a fixed protocol of jumping and landing tasks, and during four volleyball training sessions, respectively. The MS-TCN model achieved better performance than a state-of-the-art deep learning model but with lower computational cost. In the lab sessions, most jump counts showed small differences between the predicted jumps and video-annotated jumps, with an overall count showing a Limit of Agreement (LoA) of 0.1±3.40 (r =0.884). For comparison, the proposed algorithm showed slightly worse results than VERT (a commercial jumping assessment device) with a LoA of 0.1±2.08 (r =0.955) but the differences were still within a comparable range. In the training sessions, the recognition of three types of jumps exhibited a mean difference from observation of less than 10 jumps: block, smash, and overhead serve. These results showed the potential of using a single IMU to recognize the types of volleyball jumps. The proposed architecture provided high resolution of recognition and required fewer parameters compared with state-of-the-art models.Journal article10.1109/ACCESS.2025.3545560WOS:001446493800008HUMAN ACTIVITY RECOGNITIONFREQUENCYRISKCNN