A Supervised Autoencoder for Human Activity Recognition with Inertial Sensors

Abstract

Human Activity Recognition (HAR) with inertial sensors is one of the most active research fields. Various machine learning algorithms have been proposed in HAR for classifying human activities. However, these methods heavily rely on the quality of hand-crafted features, requiring extensive feature engineering. Recent deep learning approaches have tried to perform training in an end-to-end manner. We propose a new learning scheme based on Supervised Autoencoder with Self-Attention (SAE-SA). Our main idea is two-fold: (1) Through the dimensional reduction in supervised autoencoder, our model is robust to noisy input signals sensor data (2) We incorporate the self-attention mechanism in the classifier layer of supervised autoencoder, which focuses more to the relevant signals, and can learn the features without any knowledge of the aggregated signal data. We evaluate SAE-SA on benchmark datasets: WISDM v2.0, and PAMAP2. We achieve accuracy of 95.76%, and 97.60% respectively, which is the state-of-the-art results in HAR. © 2023 IEEE.