Evaluation of the Android-Based Fall Detection System with Physiological Data Monitoring

Aging population is considered to be major problem in modern healthcare. At the same time, fall incidents often occur among elderly and cause serious injuries affecting their independent living. This paper proposes a framework which uses mobile phone technology together with physiological data monitoring in order to detect falls. The system carries out collecting, storing and processing of acceleration data with further alarm generating and transferring all the measurements to remote caregiver. To perform evaluation, an experimental setup involving novice ice-skaters were carried out to obtain realistic fall data and examine the effects of falling on physiological parameters. A fall detection algorithm has been designed therefore to cope with large variations of movement in the torso. The on-line algorithm operating showed performance results of 90% specificity, 100% sensitivity and 94% accuracy.