Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition

TL;DR

This paper evaluates various deep learning architectures and feature extraction methods for sensor-based human activity recognition, demonstrating their effectiveness over traditional approaches through extensive experiments.

Contribution

It provides a comprehensive experimental comparison of deep learning models, training mechanisms, and feature representations for activity recognition using sensor data.

Findings

Deep learning approaches outperform traditional methods.

Contrastive learning improves recognition accuracy.

Feature representations significantly impact model performance.

Abstract

The extensive ubiquitous availability of sensors in smart devices and the Internet of Things (IoT) has opened up the possibilities for implementing sensor-based activity recognition. As opposed to traditional sensor time-series processing and hand-engineered feature extraction, in light of deep learning's proven effectiveness across various domains, numerous deep methods have been explored to tackle the challenges in activity recognition, outperforming the traditional signal processing and traditional machine learning approaches. In this work, by performing extensive experimental studies on two human activity recognition datasets, we investigate the performance of common deep learning and machine learning approaches as well as different training mechanisms (such as contrastive learning), and various feature representations extracted from the sensor time-series data and measure their effectiveness for the human activity recognition task.