Background: Location tracking of a wearable radio frequency (RF) transmitter in a wireless network is a potentially useful tool for the home monitoring of patients in clinical applications. However, the problem of converting RF signals into accurate estimates of transmitter location remains a significant challenge.
Objectives: We wish to demonstrate that long-term home monitoring using RF transmitters is feasible. Additionally, we conjecture that human motion within familiar environments is confined to relatively small regions of high occupancy. Hence, human motion can be modelled as movement along a network of such high occupancy regions.
Methods and materials: Our methodology uses a signal processing technique developed by one of the authors (Almudevar). The technique converts longitudinal RF data into an estimated trajectory which does not depend on explicit location estimates. This approach eliminates the need for a location-signal calibration procedure. Given a long-term trajectory, Gaussian mixture models are used to identify high occupancy regions. The methodology was evaluated using data collected under a study funded by an Everyday Technologies for Alzheimer Care (ETAC) research grant from the Alzheimer's Association. A home monitoring system provided by Home Free Systems was used.
Results: The proposed methodology was able to reliably reconstruct trajectories using study data. Regions of high occupancy were identified, and the observed transitions between these regions were found to be spatially and serially coherent. In addition, the trajectory was compared to output from a parallel home sensor network, and a high degree a conformity was evident.
Conclusion: Long-term home monitoring of human motion is feasible using readily available and easily installable technology. Furthermore, by using suitable signal processing algorithms, the often difficult location-signal calibration process can be bypassed.