Deep Wavelet Convolutional Neural Networks for Multimodal Human Activity Recognition Using Wearable Inertial Sensors
Author:
Vuong Thi Hong1ORCID, Doan Tung2ORCID, Takasu Atsuhiro1ORCID
Affiliation:
1. Department of Informatics, National Institute of Informatics, Tokyo 101-0003, Japan 2. Department of Computer Engineering, School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 11615, Vietnam
Abstract
Recent advances in wearable systems have made inertial sensors, such as accelerometers and gyroscopes, compact, lightweight, multimodal, low-cost, and highly accurate. Wearable inertial sensor-based multimodal human activity recognition (HAR) methods utilize the rich sensing data from embedded multimodal sensors to infer human activities. However, existing HAR approaches either rely on domain knowledge or fail to address the time-frequency dependencies of multimodal sensor signals. In this paper, we propose a novel method called deep wavelet convolutional neural networks (DWCNN) designed to learn features from the time-frequency domain and improve accuracy for multimodal HAR. DWCNN introduces a framework that combines continuous wavelet transforms (CWT) with enhanced deep convolutional neural networks (DCNN) to capture the dependencies of sensing signals in the time-frequency domain, thereby enhancing the feature representation ability for multiple wearable inertial sensor-based HAR tasks. Within the CWT, we further propose an algorithm to estimate the wavelet scale parameter. This helps enhance the performance of CWT when computing the time-frequency representation of the input signals. The output of the CWT then serves as input for the proposed DCNN, which consists of residual blocks for extracting features from different modalities and attention blocks for fusing these features of multimodal signals. We conducted extensive experiments on five benchmark HAR datasets: WISDM, UCI-HAR, Heterogeneous, PAMAP2, and UniMiB SHAR. The experimental results demonstrate the superior performance of the proposed model over existing competitors.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference57 articles.
1. Hoelzemann, A., Romero, J.L., Bock, M., Laerhoven, K.V., and Lv, Q. (2023). Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition Using Wrist-Worn Inertial Sensors. Sensors, 23. 2. An approach to sport activities recognition based on an inertial sensor and deep learning;Pajak;Sens. Actuators A Phys.,2022 3. Adel, B., Badran, A., Elshami, N.E., Salah, A., Fathalla, A., and Bekhit, M. (2022, January 29–31). A Survey on Deep Learning Architectures in Human Activities Recognition Application in Sports Science, Healthcare, and Security. Proceedings of the International Conference on Innovations in Computing Research, Athens, Greece. 4. Saha, A., Roy, M., and Chowdhury, C. (2023). IoT-Based Human Activity Recognition for Smart Living. IoT Enabled Computer-Aided Systems for Smart Buildings, Springer Nature B.V. 5. Najeh, H., Lohr, C., and Leduc, B. (2023). Convolutional Neural Network Bootstrapped by Dynamic Segmentation and Stigmergy-Based Encoding for Real-Time Human Activity Recognition in Smart Homes. Sensors, 23.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|