Abnormal Activity Detection Based On Received Signal Strengths Of Radio Tomographic Networks

Radio tomographic networks based imaging is bringing significant impact in activity sensing. In this article, we proposed an abnormal activity detection method without any computed recovery imaging. By organizing a vertically arranged profile-aware network, the critical state feature of abnormal activity is encoded into data stream of received signal strengths (RSSs). Then, the new coming sensor data is compared with the instantaneous state feature already recorded, and abnormal detection is performed according to similarity. To validate the efficacy of our method, we defined walking as normal activity and fall as abnormal activity in indoor environments. Experiments give the encouraging results.