![]() ![]() Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. As preliminary research, the results of the CNN model show accurate performance for the recognition of working status, suggesting the feasibility of this fully online method. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. Telework has become a universal working style under the background of COVID-19. 4Institute for Healthcare Robotics, Waseda University, Tokyo, Japan.3Department of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China.2Data Center, Nara Institute of Science and Technology, Nara, Japan.1Computational Systems Biology, Division of Information Science, Nara Institute of Science and Technology, Nara, Japan.Altaf-Ul-Amin 1 Shigehiko Kanaya 1,2 Ming Huang 1 * Yongxin Zhang 1 Zheng Chen 1 Haoyu Tian 1 Koshiro Kido 1 Naoaki Ono 1,2 Wei Chen 3 Toshiyo Tamura 4 M. ![]()
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