Location of threedimensional movement for a human using a wearable multinode instrument implemented by wireless body area networks Abstract   A wireless human body local area network is a wireless network composed of lightweight, wearable or implantable sensor nodes that sense various physiological parameters of the human body. This paper introduces the key technologies and characteristics of wireless sensor networks and the research status of node localization, analyzes the wireless sensor network localization algorithms and their performance evaluation indicators, and conducts indepth research on wearable 3D node localization algorithms. The 3D positioning algorithm uses static beacon nodes to achieve the 3D positioning of public nodes. The positioning accuracy and positioning rate depend on the number of beacon nodes, and the number of beacon nodes increases, which increases network costs and energy consumption. Aiming at the spatiotemporal correlation between motion data collected by multiple sensors in a wireless human local area network, an RSSIbased multinode 3D motion monitoring auxiliary algorithm is used, including the planning of wearable device motion trajectories. Throughout the positioning process, the workflow belongs to the public node and involves the format of the relevant information packet. The 3D wearable multinode positioning algorithm based on RSSI is a distancedependent distributed positioning algorithm. The public node uses the RSSI ranging method to obtain the distance during the twostage positioning process, and then uses the threedimensional multilateral positioning method to perform its own calculation.

RFID RSS Fingerprinting System for Wearable Human Activity Recognition Alternative healthcare solutions have been identified as a viable approach to ameliorate the increasing demand for telehealth and prompt healthcare delivery. Moreover, indoor localisation using different technologies and approaches have greatly contributed to alternative healthcare solutions. In this paper, a costeffective, radio frequency identification (RFID)based indoor location system that employs received signal strength (RSS) information of passive RFID tags is presented. The proposed system uses RFID tags placed at different positions on the target body. The mapping of the analysed data against a set of reference position datasets is used to accurately track the vertical and horizontal positioning of a patient within a confined space in realtime. The Euclidean distance model achieves an accuracy of 98% for all sampled activities. However, the accuracy of the activity recognition algorithm performs below the threshold performance for walking and standing, which is due to similarities in the target height, weight and body density for both activities. The obtained results from the proposed system indicate significant potentials to provide reliable health measurement tool for patients at risk.