Multimodal Data-based Deep Learning Model for Human Behavior Recognition towards Smart Health-oriented Office-working
Human factors is the key component of human-centric systems for both human well-being and overall system performance. With such a concern, strategic development plans have been proposed in different countries, such as Healthy China Initiative. The advancement of Internet-of-Things (IoT), intelligent data analytics, and service innovation contribute to the prevailing development of modern office work. A new paradigm named smart health-oriented office-working (SHOW) is defined to prevention and control measures of health issues associated with office work. It is capable of understanding the context and adapting to their demands. Human behavior recognition system is essential to incorporate intelligence into SHOW, however, several issues still exist, including ineffective use of multimodal data, and lack of a privacy-preserving and unobtrusive method. Prevailing studies have utilized cameras, wearables, smartphones, pressure sensors and infrared array sensors to recognize human behavior, by considering the pros and cons of these approaches, we have explored different multimodal data combinations. Moreover, a deep learning model-based recognition algorithm was developed with the adoption of a feature-level fusion strategy. Extensive experiments are conducted to examine and validate the performance of the proposed model using a self-collected dataset. We hope this study would bring an interdisciplinary and collaborative perspective for smart health-oriented office-working and encourage more researches in this emerging and promising field.
Tao Peng, is currently an Associate Professor, and serves as the Associate Head of the Department of Industrial and Systems Engineering, Deputy Director of the Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University. He is a member of CMES, CGS, IEEE, ASME, and the State Key Laboratory of Fluid Power Components and Mechatronic Systems, Zhejiang University, China. He received his bachelor and master degrees from Xi’an Jiaotong University, China, and doctoral degree from the University of Auckland, New Zealand. His research interest focuses on sustainable design, manufacturing, supply chain and services, incorporating innovative smart technologies, big data analytics, and cognitive intelligence. He has been listed one of the World’s Top 2% Scientists by Stanford University, and his work has been funded by NSFC, MIIT, DSTZJ, and several industrial partners, with outcomes of 90+ SCI/EI-indexed publications and 12 Chinese patents. He serves as an Associate Editor of IET Collaborative Intelligent Manufacturing, Editorial Board Member of Sensors and Green Manufacturing Open, Guest Editor of Journal of Manufacturing Systems, Additive Manufacturing, Journal of Cleaner Production, Journal of Engineering Design, Journal of Mechanical Engineering (in Chinese), China Mechanical Engineering (in Chinese), etc.
29 September 2023 (Friday)
Prof. Peng Tao