Design Space Exploration on Efficient and Accurate Human Pose Estimation from Sparse IMU-Sensing
This work addresses the need for resource-efficient and privacy-preserving human pose estimation in applications like sports and rehabilitation, though it is incremental in optimizing sensor configurations.
The paper tackles the trade-off between accuracy and hardware efficiency in human pose estimation using sparse IMU sensors, achieving a 32.7% accuracy improvement and reducing sensors by two compared to state-of-the-art, with an optimal configuration yielding a mesh error of 6.03 cm.
Human Pose Estimation (HPE) to assess human motion in sports, rehabilitation or work safety requires accurate sensing without compromising the sensitive underlying personal data. Therefore, local processing is necessary and the limited energy budget in such systems can be addressed by Inertial Measurement Units (IMU) instead of common camera sensing. The central trade-off between accuracy and efficient use of hardware resources is rarely discussed in research. We address this trade-off by a simulative Design Space Exploration (DSE) of a varying quantity and positioning of IMU-sensors. First, we generate IMU-data from a publicly available body model dataset for different sensor configurations and train a deep learning model with this data. Additionally, we propose a combined metric to assess the accuracy-resource trade-off. We used the DSE as a tool to evaluate sensor configurations and identify beneficial ones for a specific use case. Exemplary, for a system with equal importance of accuracy and resources, we identify an optimal sensor configuration of 4 sensors with a mesh error of 6.03 cm, increasing the accuracy by 32.7% and reducing the hardware effort by two sensors compared to state of the art. Our work can be used to design health applications with well-suited sensor positioning and attention to data privacy and resource-awareness.