Deep Learning for Sensor-based Activity Recognition: A Survey

TL;DR

This survey reviews recent deep learning techniques applied to sensor-based human activity recognition, highlighting advancements, challenges, and future research directions in automatic feature extraction and model generalization.

Contribution

It provides a comprehensive overview of deep learning methods in sensor-based activity recognition, emphasizing recent progress and identifying key challenges for future work.

Findings

Deep learning enables automatic feature extraction improving recognition accuracy.

Current methods face challenges in unsupervised and incremental learning.

Survey covers sensor modalities, models, and applications comprehensively.

Abstract

Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.