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.

通过使用从目标活动的视频记录中得出的实时 2D 姿势估计派生的得出骨架数据，本文引入了一种解决这个问题的新方法，为识别最佳传感器位置提供了一种独特的策略。我们通过进行可行性研究验证了我们的方法，将惯性传感器应用于监测十个被试者的 13 种不同活动。我们的发现表明，基于视觉的传感器放置方法与传统的深度学习方法提供了可比较的结果，从而证明了它的有效性。该研究通过提供一种轻量级的、设备上的解决方案来确定最佳传感器位置，从而改进了数据匿名化，支持多模态分类方法，显著推进了人体活动识别领域。