Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes

TL;DR

This paper presents a method to continuously update and extend bootstrapped human activity recognition systems in smart homes, improving their accuracy over time amidst changing resident behaviors and home layouts.

Contribution

It introduces an updating procedure using contrastive learning and seed points to enhance HAR systems without starting from scratch each time.

Findings

Improved segmentation accuracy of activity recognition models.

Effective adaptation to changing resident behaviors.

Validated on CASAS datasets showing practical benefits.

Abstract

Developing human activity recognition (HAR) systems for smart homes is not straightforward due to varied layouts of the homes and their personalized settings, as well as idiosyncratic behaviors of residents. As such, off-the-shelf HAR systems are effective in limited capacity for an individual home, and HAR systems often need to be derived "from scratch", which comes with substantial efforts and often is burdensome to the resident. Previous work has successfully targeted the initial phase. At the end of this initial phase, we identify seed points. We build on bootstrapped HAR systems and introduce an effective updating and extension procedure for continuous improvement of HAR systems with the aim of keeping up with ever changing life circumstances. Our method makes use of the seed points identified at the end of the initial bootstrapping phase. A contrastive learning framework is trained using these seed points and labels obtained for the same. This model is then used to improve the segmentation accuracy of the identified prominent activities. Improvements in the activity recognition system through this procedure help model the majority of the routine activities in the smart home. We demonstrate the effectiveness of our procedure through experiments on the CASAS datasets that show the practical value of our approach.