  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.
