Sitsen: Passive sitting posture sensing based on wireless devices. (July 2021)

• Record Type: Journal Article
• Authors: Li, Miaoyu; Jiang, Zhuohan; Liu, Yutong; Chen, Shuheng; Wozniak, Marcin; Scherer, Rafal; Damasevicius, Robertas; Wei, Wei; Li, Ziyi; Li, Zuxin
• Is Part Of: International journal of distributed sensor networks. Volume 17:Number 7(2021)
• Publication Date: 2021-07
• Subjects: Sitting posture recognition -- radio frequency identification -- back propagation net -- principal component analysis -- device-free; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/15501477211024846
• Languages: English
• ISSNs: 1550-1329

Abstract

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.