Multiscale Deep Feature Learning for Human Activity Recognition Using Wearable Sensors

• Published in: IEEE transactions on industrial electronics (1982) Vol. 70; no. 2; pp. 2106 - 2116
• Main Authors: Tang, Yin, Zhang, Lei, Min, Fuhong, He, Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Accuracy; Activity recognition; Artificial neural networks; Classification; Convolution; convolutional neural networks (CNNs); Feature extraction; Human activity recognition; Human performance; Kernel; Machine learning; multiscale; sensory data; Task analysis; Ubiquitous computing; weakly supervised learning; Wearable computers; Wearable technology

Deep convolutional neural networks (CNNs) achieve state-of-the-art performance in wearable human activity recognition (HAR), which has become a new research trend in ubiquitous computing scenario. Increasing network depth or width can further improve accuracy. However, in order to obtain the optimal...