The Global Positioning System (GPS) has been widely applied in outdoor positioning, but it cannot meet the accuracy requirements of indoor positioning. Comprising an important part of the Internet of Things perception layer, Radio Frequency Identification (RFID) plays an important role in indoor positioning. We propose a novel localization scheme aiming at the defects of existing RFID localization technology in localization accuracy and deployment cost, called ANTspin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna, which introduces a rotary table in the experiment. The reader antenna is fixed on the rotary table to continuously collect dynamic data. When compared with static acquisition, there is more information for localization. After that, the relative incident angle and distance between tags and the antenna can be analyzed for localization with characteristics of Received Signal Strength Indication (RSSI) data. We implement ANTspin using COTS RFID devices and the experimental results show that it achieves a mean accuracy of 9.34 cm in 2D and mean accuracy of 13.01 cm in three-dimensions (3D) with high efficiency and low deployment cost.

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Ultra-high frequency radio frequency identification (UHF RFID) technology has been widely used in many areas, and RFID localization becomes a research hotspot. There are many kinds of research on absolute localization; however, due to some disadvantages of absolute localization, relative localization is more effective in some situations. At present, there are some problems with relative localization: existing methods have low localization accuracy, and it is difficult for them to deal with high-density tags. Aiming at these problems, this paper proposes PRDL: relative localization method of RFID tags via phase and RSSI based on deep learning. By using deep learning, the variation characteristics of RFID phase and RSSI are extracted with limited data accuracy conditions. On this basis, we can infer the relative positional relationship of RFID tags with high accuracy, and design the corresponding sorting algorithm to obtain the sequence arrangement. PRDL has experimented with bare tags and actual books, and the experimental results show that PRDL can achieve better results than the traditional relative localization methods. A series of tests also showed that PRDL has good robustness and generalization ability.

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Accidental fall detection for the elderly who live alone can minimize the risk of death and injuries. In this article, we present a new fall detection method based on "deep learning and image, where a human body recognition model-DeeperCut is used. First, a camera is used to get the detection source data, and then the video is split into images which can be input into DeeperCut model. The human key point data in the output map and the label of the pictures are used as training data to input into the fall detection neural network. The output model then judges the fall of the subsequent pictures. In addition, the fall detection system is designed and implemented with using Raspberry Pi hardware in a local network environment. The presented method obtains a 100% fall detection rate in the experimental environment. The false positive rate on the test set is around 1.95% which is very low and can be ignored because this will be checked by using SMS, WeChat and other SNS tools to confirm falls. Experimental results show that the proposed fall behavior recognition is effective and feasible to be deployed in home environment.