Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition

TL;DR

This paper introduces FusionActNet, a multi-structural neural network architecture that effectively classifies human activities using wearable accelerometer and gyroscope data, outperforming existing methods on benchmark datasets.

Contribution

The paper presents a novel multi-structural architecture with a two-stage training process and a guidance module for improved activity recognition accuracy.

Findings

Achieves 97.35% accuracy on UCI HAR dataset.

Achieves 95.35% accuracy on Motion-Sense dataset.

Outperforms state-of-the-art methods in accuracy, precision, recall, and F1 score.

Abstract

A person's movement or relative positioning can be effectively captured by different types of sensors and corresponding sensor output can be utilized in various manipulative techniques for the classification of different human activities. This letter proposes an effective scheme for human activity recognition, which introduces two unique approaches within a multi-structural architecture, named FusionActNet. The first approach aims to capture the static and dynamic behavior of a particular action by using two dedicated residual networks and the second approach facilitates the final decision-making process by introducing a guidance module. A two-stage training process is designed where at the first stage, residual networks are pre-trained separately by using static (where the human body is immobile) and dynamic (involving movement of the human body) data. In the next stage, the guidance module along with the pre-trained static or dynamic models are used to train the given sensor data. Here the guidance module learns to emphasize the most relevant prediction vector obtained from the static or dynamic models, which helps to effectively classify different human activities. The proposed scheme is evaluated using two benchmark datasets and compared with state-of-the-art methods. The results clearly demonstrate that our method outperforms existing approaches in terms of accuracy, precision, recall, and F1 score, achieving 97.35% and 95.35% accuracy on the UCI HAR and Motion-Sense datasets, respectively which highlights both the effectiveness and stability of the proposed scheme.