Spatial Variability Learning of Biomechanical Dynamics in Daily Lives
Ming Liu, Qingxue Zhang · 2024
With the rapid development of modern electronics and computation capability, biomechanical mining is attracting more and more attention. Due to the complexity and inter-person variance among the biomechanical dynamics, it is important to study the spatial variability to not only optimize the activity recognition performance but also deepen the understanding of inter-sensor-location and inter-subject differences. We here propose a system with both wearable motion sensing and deep learning, for comprehensive biomechanical dynamics understanding. More specifically, the system allows collection of motion data from diverse body locations. Further, the deep learning algorithm then learns the signals and yields the physical activity types. The experiments have promisingly indicated the spatial variability of biomechanical dynamics capturing and analysis. This study will benefit the understanding of biomechanical dynamics.