Assessing Distribution Shift in Human Activity Recognition for Domain Generalization

2026-06-23

AI Summary

This study addresses the challenge of distribution shift in sensor-based human activity recognition (HAR), which arises from variations in devices, sensor placements, sampling rates, and user behaviors. It presents the first systematic investigation into four concrete types of such shifts and introduces a unified open-source benchmark platform alongside diverse datasets. The authors comprehensively evaluate 28 domain generalization methods against an empirical risk minimization baseline. Experimental results demonstrate that current domain generalization approaches offer only marginal improvements over the baseline, highlighting their limited efficacy in handling real-world distribution shifts. These findings underscore the shortcomings of existing techniques and provide critical insights and open resources to guide future research in robust HAR systems.

Abstract

While the field of Human Activity Recognition (HAR) continues to draw interest from researchers and advance in important ways, some key challenges remain. One of the most difficult aspects of building HAR models that show good performance in real-world settings is dealing with data diversity from device and sensor heterogeneity, and contextual changes that are intrinsic to real-world applications. While data diversity in HAR has been well-acknowledged in the literature, there remains a gap in understanding the effect of various types of distribution shifts on HAR models and the domain generalization problem that arises. Towards that end, this paper systematically evaluates 4 different types of distribution shifts, including variations in device type, sensor placement, sampling rate, and user behavior. Quantifying their effects, we illustrate that diversity shifts predominantly define all types of shifts, indicating the existence of unique features that are not shared across different domains. We then introduce a uniform HAR-based distribution shift benchmarks and conduct a comprehensive evaluation of up to 28 domain generalization methods. Our analysis exposes the limitations of current domain generalization algorithms in achieving model generalizability, marginally outperforming the empirical risk minimization baseline. This work represents the first systematic exploration of domain generalization and adaptation concerning specific distribution shifts in sensor-based HAR, offering an open-source benchmark platform and datasets to spur further research.

Problem

• distribution shift
• human activity recognition
• domain generalization
• sensor heterogeneity
• data diversity

Innovation

• distribution shift
• domain generalization
• human activity recognition
• sensor heterogeneity
• benchmark