RNN-based deep learning for physical activity recognition using smartwatch sensors: A case study of simple and complex activity recognition

Mathematical Biosciences and Engineering

Sakorn Mekruksavanich¹  Anuchit Jitpattanakul² 

[1] 1. Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao 56000, Thailand;2. Department of Mathematics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand 3. Intelligent and Nonlinear Dynamic Innovations Research Center, Science and Technology Research Institute, King Mongkut's University of Technology North Bangkok, Bangkok 10800, Thailand;

关键词: human activity recognition;    complex human activity;    smartwatch sensors;    bidirectional gated recurrent unit;    deep learning;   

DOI  :  10.3934/mbe.2022265

来源: DOAJ

【 摘 要 】

Currently, identification of complex human activities is experiencing exponential growth through the use of deep learning algorithms. Conventional strategies for recognizing human activity generally rely on handcrafted characteristics from heuristic processes in time and frequency domains. The advancement of deep learning algorithms has addressed most of these issues by automatically extracting features from multimodal sensors to correctly classify human physical activity. This study proposed an attention-based bidirectional gated recurrent unit as Att-BiGRU to enhance recurrent neural networks. This deep learning model allowed flexible forwarding and reverse sequences to extract temporal-dependent characteristics for efficient complex activity recognition. The retrieved temporal characteristics were then used to exemplify essential information through an attention mechanism. A human activity recognition (HAR) methodology combined with our proposed model was evaluated using the publicly available datasets containing physical activity data collected by accelerometers and gyroscopes incorporated in a wristwatch. Simulation experiments showed that attention mechanisms significantly enhanced performance in recognizing complex human activity.

【 授权许可】

Unknown