Sensor-based Human Activity Recognition facilitates unobtrusive monitoring of human movements. However, determining the most effective sensor placement for optimal classification performance remains challenging. This paper introduces a novel methodology to resolve this issue, using real-time 2D pose estimations derived from video recordings of target activities. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. We validate our approach through a feasibility study, applying inertial sensors to monitor 13 different activities across ten subjects. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach, demonstrating its efficacy. This research significantly advances the field of Human Activity Recognition by providing a lightweight, on-device solution for determining the optimal sensor placement, thereby enhancing data anonymization and supporting a multimodal classification approach.

翻译：基于传感器的人体活动识别能够实现对人体运动的无干扰监测。然而，确定传感器的最优放置位置以实现最佳分类性能仍是一个挑战。本文提出了一种新颖的方法来解决该问题，利用目标活动视频记录中提取的实时二维姿态估计。所获取的骨架数据为识别最优传感器位置提供了一种独特策略。我们通过一项可行性研究验证了该方法，使用惯性传感器对十名受试者的13种不同活动进行监测。结果表明，基于视觉的传感器放置方法与传统深度学习方法相比具有相当的效果，证明了其有效性。本研究通过提供一种轻量级、设备端解决方案来确定最佳传感器放置位置，显著推动了人体活动识别领域的发展，从而增强了数据匿名化能力，并支持多模态分类方法。