  Monitoring the number of jumps 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 sample-wise classification. The model was evaluated on
ten volleyball players and twenty-six 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
sample-wise architecture provided high resolution of recognition and the MS-TCN
required fewer parameters to train compared with state-of-the-art models.
