Real-time indoor patient movement pattern telemonitoring with one-meter precision

Monitoring of the activities of daily living of the elderly at home is widely recognized as useful for detection of new or deteriorated 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 prototype based on a sensor fusion approach was built. 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 Kalman filter and received signal strength indication (RSSI). The results of our experiments were promising with average estimation error of 0.55 meters. We are confident that with more work our design can be adapted to a home-like environment with a more robust localization solution.