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.

翻译：人体姿态估计（HPE）在运动分析、康复训练或工作安全等场景中评估人体动作时，需在不泄露敏感个人数据的前提下实现精准感知。因此，本地化处理至关重要，而惯性测量单元（IMU）可替代常见摄像头传感方式，以适配此类系统的有限能量预算。现有研究鲜少讨论精度与硬件资源高效利用之间的核心权衡问题。我们通过模拟方式对IMU传感器的数量与位置分布进行设计空间探索（DSE），着力解决这一权衡问题。首先，基于公开的人体模型数据集，针对不同传感器配置生成IMU数据并训练深度学习模型；同时提出一种综合评估指标以量化精度与资源之间的权衡关系。将DSE作为工具评估传感器配置，并为特定应用场景甄别最优配置。以精度与资源权重相等的系统为例，我们确定的最优传感器配置包含4个传感器，网格误差为6.03厘米，相较现有技术精度提升32.7%，且硬件使用量减少两个传感器。本研究可为健康应用设计提供适配的传感器布局方案，同时兼顾数据隐私保护与资源意识。