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

TL;DR

This paper presents a real-time, vision-based method for determining optimal sensor placement in human activity recognition, improving data collection efficiency and privacy with a lightweight, on-device approach.

Contribution

It introduces a novel real-time 2D pose estimation technique to identify optimal sensor locations, validated through a feasibility study with inertial sensors across multiple activities.

Findings

Vision-based sensor placement achieves comparable accuracy to deep learning methods.

The approach enhances data anonymization and supports multimodal classification.

It provides a lightweight, on-device solution for sensor placement optimization.

Abstract

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.