Timely calls for help can really make a difference for elders who suffer from falls, particularly in private locations. Considering privacy protection and convenience for the users, in this paper, we approach the problem by using impulse–radio ultra-wideband (IR-UWB) monostatic radar and propose a learning model that combines convolutional layers and convolutional long short term memory (ConvLSTM) to extract robust spatiotemporal features for fall detection. The performance of the proposed scheme was evaluated in terms of accuracy, sensitivity, and specificity. The results show that the proposed method outperforms convolutional neural network (CNN)-based methods. Of the six activities we investigated, the proposed method can achieve a sensitivity of 95% and a specificity of 92.6% at a range of 8 meters. Further tests in a heavily furnished lounge environment showed that the model can detect falls with more than 90% sensitivity, even without re-training effort. The proposed method can detect falls without exposing the identity of the users. Thus, the proposed method is ideal for room-level fall detection in privacy-prioritized scenarios.

Passive indoor localization has gained much attention recently. Numerous applications including storage management and somatosensory systems would benefit from accurate passive indoor localization. As a potential technology for the internet of things, radio frequency identification (RFID) has many advantages for indoor localization, such as low cost, parallel processing, and low complexity. In this paper, a passive indoor localization system, named Lobain, was proposed based on interference of ultrahigh frequency RFID tags. Using commercial off-the-shelf reader, antenna, and tags, this system, functions for tagged and tag-free item localization in 2-D and 3-D indoor environments. Small numbers of reference tags were used as sensors to improve performance and reduce cost. Unlike traditional methods, an improved propagation model and an innovative method were presented to extract a target's channel information from a complicated received signal. A relative phase wrapping integer method was presented to solve the phase wrapping problem of reference tags, and the least-squares method was used to optimize the tradeoff between number of reference tags and accuracy. A tag and tag-free items including shrimp chips, cola, and instant noodles were localized as single target in the evaluation experiments. The prototype achieved mean 2-D median localization errors of 5.9 (x-axis) and 8.2 cm (y-axis), and achieved mean 3-D median errors of 9 (x-axis), 6.7 (y-axis), and 13.3 cm (z-axis).

To achieve device-free indoor localization without the active participation of the users, this paper presents WallSense, a device-free indoor localization system based on off-the-shelf Radio RFID (Radio-Frequency Identification) equipment. The system deploys two orthogonal tag arrays in adjoining walls and uses the RSSI and phase information measured by RFID readers to localize the target. By differentiating the backscattered signal between adjacent tag pairs, WallSense is able to eliminate most undesirable factors and extract information directly related to the location of the target. By applying Particle Swarm Optimization (PSO) with a novel weighted fitness function and combining the localization result of two orthogonal tag arrays, the system is able to localize the target with high accuracy. Experiments show that the system is able to localize human target with 0.24 m median error. Also, WallSense has low deployment overhead and do not require the users to carry any devices.