  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.
