A Passive Wearable-Free Human Activity Pattern Tracking

Accurate indoor person localization is essential for several services, such as assisted living. We introduce a tagless indoor person localization system based on capacitive sensing and localization algorithms that can determine the location of the user with less than 0.6 m average error during a long-term deployment and 0.2 m during a short-term deployment scenario in a 3 m × 3 m room. In the case of the short-term deployment of the localization system we achieve the recall and precision better than 70%. We present the results of the capacitance measurement using a set of simulation models and compare them with the results of capacitance measurement based on the data gathered during a realistic deployment of the system. We also discuss the effects of various noise types on the measurements and ways to reduce them using filters suitable for on-sensor implementation to lower communication energy consumption. In the end, we compare the performance of several standard localization algorithms in terms of localization error, recall, precision, and accuracy of detection of the movement trajectory.