MobiSense

Abstract

This article introduces MobiSense, a novel mobile health monitoring system for ambulatory patients. MobiSense resides in a mobile device, communicates with a set of body sensor devices attached to the wearer, and processes data from these sensors. MobiSense is able to detect body postures such as lying, sitting, and standing, and walking speed, by utilizing our rule-based heuristic activity classification scheme based on the extended Kalman (EK) Filtering algorithm. Furthermore, the proposed system is capable of controlling each of the sensor devices, and performing resource reconfiguration and management schemes (sensor sleep/wake-up mode). The architecture of MobiSense is highlighted and discussed in depth. The system has been implemented, and its prototype is showcased. We have also carried out rigorous performance measurements of the system including real-time and query latency as well as the power consumption of the sensor nodes. The accuracy of our activity classifier scheme has been evaluated by involving several human subjects, and we found promising results.