Domain adaptation framework for personalized human activity recognition models

Human Activity Recognition (HAR) has emerged as a vital measure of quality of life, holding significant implications for human health. The need for effective mobility monitoring in diverse settings, both indoors and outdoors, necessitates the development of scientific and technological tools. To broaden accessibility, wearable devices like smartphones and smartwatches are commonly employed, leveraging sensor data analysis for valuable insights into user activities. HAR, treated as a classification task, involves training a classifier using sensor data and corresponding activity labels. The classifier aims to automatically recognize and classify various activities in future instances. However, the performance of a generic HAR model trained on a diverse population diminishes significantly when applied to a specific user due to inter-subject variability, encompassing variations in activity patterns, behavioral status, gait, and posture. To address this challenge, we propose an innovative auto-supervised domain adaptation approach based on particle filter theory, aiming to automatically construct a personalized HAR classifier. Our approach integrates multiple steps inspired by the particle filter formalism, enabling iterative approximation of the target distribution through temporal samples to personalize the HAR model for the specific user. In extensive experiments on public HAR datasets, we emphasize the critical role of personalization when deploying an HAR classifier for a new user. The results demonstrate that our framework significantly enhances the accuracy of HAR for new users compared to a non-personalized model, achieving an average improvement of 50% across most datasets. Furthermore, we implement our personalized HAR model on an embedded wearable device, enhancing its accessibility for real-world applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.