Monitoring the activities of daily living of the elderly at home is widely recognized as useful for the detection of new or deteriorating health conditions. However, the accuracy of existing indoor location tracking systems remains unsatisfactory. The aim of this study was therefore to develop a localization system that can identify a patient's real-time location in a home environment with maximum estimation error of two meters at a 95% confidence level. A proof-of-concept system based on a sensor fusion approach was built with considerations for lower cost, reduced intrusiveness, and higher mobility, deployability, and portability. This involved the development of both a step detector using the accelerometer and compass of an iPhone 5, and a radio-based localization subsystem using a Kalman filter and received signal strength indication (RSSI) to tackle issues that had been identified as limiting accuracy. The results of our experiments were promising with an average estimation error of 0.47 meters. We are confident that with the proposed future work our design can be adapted to a home-like environment with a more robust localization solution.
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 November 2015.
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