Indoor Localization, Tracking and Fall Detection for Assistive Healthcare Based on Spatial Sparsity and Wireless Sensor Network

Indoor localization and fall detection are two of the most paramount topics in assistive healthcare, where tracking the positions and actions of the patient or elderly is required for medical observation or accident prevention. Most of existing indoor localization methods are based on estimating one or more location-dependent signal parameters. However, some challenges caused by the complex scenarios within a closed space significantly limit the applicability of those existing approaches in an indoor environment, such as the severe multipath effect. In this paper, the authors propose a new one-stage, three-dimensional localization method based on the spatial sparsity in the x-y-z space. The proposed method is not only able to estimate and track the accurate positions of the patient, but also capable to detect the falls of the patient. In this method, the authors directly estimate the location of the emitter without going through the intermediate stage of TOA or signal strength estimation. The authors evaluate the performance of the proposed method using various Monte Carlo simulation settings. The results show that the proposed method is i very accurate even with a small number of sensors and ii very effective in addressing the multi-path issues.