Fall detection using smartwatch sensor data with accessor architecture

This paper proposes using a commodity-based smartwatch paired with a smartphone for developing a fall detection IoT application which is non-invasive and privacy preserving. The majority of current fall detection applications require specially designed hardware and software which make them expensive and inaccessible to the general public. We demonstrated that by collecting accelerometer data from a smartwatch and processing those data in a paired smartphone, it is possible to reliability detect (93.8% accuracy) whether a person has encountered a fall in real-time. By wearing a smartwatch as a piece of jewelry, the well-being of a person can be monitored in real-time at anytime and anywhere as contrasted to being confined in a particular facility installed with special sensors and cameras. Using simulated fall data acquired from volunteers, we trained a fall detection model off-line that can be composed with a data collection accessor to continuously analyze accelerometer data gathered from a smartwatch to detect minor or serious fall at anytime and anywhere. The accessor-based architecture allows easy composition of the fall-detection IoT application tailored to heterogeneity of devices and variation of user’s need.