SitR: Sitting Posture Recognition Using RF Signals

Abstract

Sitting posture has a close relationship with our health, and keeping a healthy sitting posture is critical to each of us. Poor sitting postures often inevitably increase the risk of modern health musculoskeletal disorders. Previous works either used a camera to record the image or attached wearable sensors on the human body to recognize sitting postures. However, video-based approaches may face privacy issues while the wearable sensor-based approaches may cause uncomfortable to the user. In this work, we propose SitR, the first sitting posture recognition system using RF signals alone, which neither compromises the privacy nor requires wearing various sensors on the human body. We demonstrate that SitR can successfully recognize seven habitual sitting postures with just three lightweight and low-cost RFID tags pasted to the user's back. Our design exploits the correlation between the phase change of RFID tags and the sitting postures. By extracting effective features of the measured phase sequences and employing appropriate machine learning algorithm, SitR can achieve robust and high performance. We evaluate the performance of SitR with ten volunteers in two different scenarios. Extensive experiments show SitR can recognize seven sitting postures with a high accuracy across different scenarios and various conditions. SitR can further detect the abnormal respiration, stand up, and sit down and provide sitting posture history for sedentary people.