RFARP: RFIDBased Activity Recognition and Prediction in Smart Home Smart Home is generally considered to be the final solution for human living problem, especially for health care of the elderly and disabled, power saving, etc. Human activity recognition in smart home is the key to achieve home automation, which enables smart services automatically run according to human mind. Recent researches have made several progresses in this field, however most of them can only recognize default activities which is probably not needed by smart home services. In addition, low scalability makes such researches infeasible out of laboratory. In this work, we unwrap this issue and propose a novel framework to not only recognize human activity, but also predict it. The framework contains three stages: recognition after the activity; recognition in progress and activity prediction in advance. With the help of RFID tags, the hardware cost of our framework is low enough to popularize. And the experiment result shows that our framework can realize good performance in activity recognition and prediction with high scalability.

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.