Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening

TLDR

Human Activity Recognition identifies actions from sensor signals, deep learning has improved performance, yet few studies have explored test data sharpening. The study proposes a 1D CNN for HAR that uses a divide‑and‑conquer classifier and test data sharpening. The method trains a binary 1D CNN to distinguish abstract activities, then two multi‑class 1D CNNs for specific activities, and applies test data sharpening at prediction to boost accuracy. On two benchmark datasets, the approach outperforms the two‑stage 1D CNN‑only method and other state‑of‑the‑art techniques.

Abstract

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.