Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

TL;DR

This review paper discusses recent advances in deep learning techniques for human activity recognition using wearable sensors, highlighting progress, challenges, and future research directions in the field.

Contribution

It systematically categorizes existing deep learning methods for wearable-based HAR and provides a comprehensive analysis of current trends and challenges.

Findings

Deep learning has significantly improved HAR accuracy.

Emerging trends include multi-modal sensor fusion and real-time processing.

Major challenges involve data privacy and model generalization.

Abstract

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human--computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.