Improving physical activity recognition using a new deep learning architecture and post-processing techniques

• Published in: Engineering applications of artificial intelligence Vol. 92; p. 103679
• Main Authors: Gil-Martín, Manuel, San-Segundo, Rubén, Fernández-Martínez, Fernando, Ferreiros-López, Javier
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2020
• Subjects: Classification; Convolutional neural networks; Deep learning structure; Inertial signals; Physical activity recognition; Inertial signals; Convolutional neural networks; Physical activity recognition; Deep learning structure; Classification
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2020.103679

This paper proposes a Human Activity Recognition system composed of three modules. The first one segments the acceleration signals into overlapped windows and extracts information from each window in the frequency domain. The second module detects the performed activity at each window using a deep learning structure based on Convolutional Neural Networks (CNNs). The first part of this structure has several layers associated to each sensor independently and the second part combines the outputs from all sensors in order to classify the physical activity. The third module integrates the window-level decision in longer periods of time, obtaining a significant performance improvement (from 89.83% to 96.62%). These are the best classification results on the PAMAP2 dataset with a Leave-One-Subject-Out (LOSO) evaluation.