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Sensor-based Human Activity Recognition facilitates unobtrusive monitoring ofhuman movements. However, determining the most effective sensor placement foroptimal classification performance remains challenging. This paper introduces anovel methodology to resolve this issue, using real-time 2D pose estimationsderived from video recordings of target activities. The derived skeleton dataprovides a unique strategy for identifying the optimal sensor location. Wevalidate our approach through a feasibility study, applying inertial sensors tomonitor 13 different activities across ten subjects. Our findings indicate thatthe vision-based method for sensor placement offers comparable results to theconventional deep learning approach, demonstrating its efficacy. This researchsignificantly advances the field of Human Activity Recognition by providing alightweight, on-device solution for determining the optimal sensor placement,thereby enhancing data anonymization and supporting a multimodal classificationapproach.

A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition