Are you sitting right?Sitting Posture Recognition Using RF Signals Today, sedentary behaviors and bad sitting postures are the main causes of modern health musculoskeletal disorders and illnesses. Previous works either used a camera to record the image or attached wearable sensors on human body to recognize sitting postures. However, videobase approaches may face privacy issue while the wearable sensorbased approaches may cause uncomfortable to the user. This paper introduces SitR, the first sitting posture recognition system using RF signals alone. We demonstrate that with just three tags pasted to onexe2x80x99s back, SitR can successfully recognize three habitual sitting postures. Our design exploits the correlation between the phase change of RFID tags and the sitting postures. By extracting effective features from the measured phase sequences and employing machine learning algorithm, SitR can achieve robust and high performance. We evaluated SitR through extensive experiments including 14 volunteers under 3 different scenarios. The experiment results show that SitR can recognize sitting postures with an average accuracy of 99.27%. Our system can further detect the abnormal respiration and provide sitting posture history for sedentary people.

RFcare: devicefree posture recognition for elderly people using a passive RFID tag array Activity recognition is a fundamental research topic for a wide range of important applications such as fall detection for elderly people. Existing techniques mainly rely on wearable sensors, which may not be reliable and practical in realworld situations since people often forget to wear these sensors. For this reason, devicefree activity recognition has gained the popularity in recent years. In this paper, we propose an RFID (radio frequency identification) based, devicefree posture recognition system. More specifically, we analyze Received Signal Strength Indicator (RSSI) signal patterns from an RFID tag array, and systematically examine the impact of tag configuration on system performance. On top of selected optimal subset of tags, we study the challenges on posture recognition. Apart from exploring posture classification, we specially propose to infer posture transitions via Dirichlet Process Gaussian Mixture Model (DPGMM) based Hidden Markov Model (HMM), which effectively captures the nature of uncertainty caused by signal strength varieties during posture transitions. We run a pilot study to evaluate our system with 12 orientationsensitive postures and a series of posture change sequences. We conduct extensive experiments in both lab and reallife home environments. The results demonstrate that our system achieves high accuracy in both environments, which holds the potential to support assisted living of elderly people.