Contactless Continuous Activity Recognition based on Meta-Action Temporal Correlation in Mobile Environments
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Contactless Continuous Activity Recognition based on Meta-Action Temporal Correlation in Mobile Environments Lin Wang1
· Xing Su2 · Hecheng Su1 · Nan Jing1
Accepted: 23 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Continuous activity recognition (CAR) plays an important role in human daily indoor activity monitoring and can be widely used in smart home, human-computer interaction and user authentication. Due to the privacy issue and limited coverage of video signals, RF-based CAR has attracted more and more attention in recent years. This paper focuses on three key problems in RF-based CAR: denoising, segmentation and recognition. We present the design and implementation of a contactless and sensorless continuous activity recognition system, namely WiCheck. Our basic idea is to utilize the temporal correlation between two adjacent actions in continuous activity to eliminate the cumulative error in continuous activity segmentation. Firstly, the multi-layer optimized noise elimination method is used to decrease the environment interference. Secondly, a method based on dual-swing window is proposed to reduce the cumulative error of continuous activity segmentation. Finally, WiCheck is implemented in different indoor environments, and 6 continuous activity sequences are designed to evaluate and analyze the influencing factors. The continuous activity recognition accuracy of WiCheck to two actions and three actions can approach 90% and 75%, respectively. Keywords Continuous activity · WiFi signal · Segmentation · Activity recognition
1 Introduction With the rise of the Internet of Things and edge computing [3, 9, 10, 14], continuous behavior recognition has become one of the hot spots in the field of mobile computing. Continuous activity recognition is one of the key technologies for indoor human monitoring [25] and location-based services [11]. It is conceivable that elderly Lin Wang
[email protected] Xing Su [email protected] Hecheng Su [email protected] Nan Jing [email protected] 1
School of Information Science and Engineering, Yanshan University, 438 Hebei Avenue, Qinhuangdao, People’s Republic of China
2
Beijing Institute of Computer Technology and Application, 52 Yongding Road, Beijing, People’s Republic of China
people living alone may need immediate medical attention, and the intelligent life perception system can make it possible and give health recommendations by monitoring people’s continuous behavior. In recent years, the activity recognition based on channel state information (CSI) has been the focus of researchers. The rise and development of this technology stems from people’s unremitting efforts to the contactless, non-invasive smart space. These works presumably assume that the effects of human behavior on the wireless signal transmission path can be iden,tified by the characteristic changes of signals. A wide range of applications [6, 7] such as gait monitoring [13, 27], sleep monitoring [12, 18], smoking detection [38], fall monitoring [26] and
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