Fall simulator for supporting supervised Machine Learning techniques in wearable devices

Abstract

Falls are the predominant cause of injury for older people. How to detect them is being in the last decade the focus of attention of many projects and researchers. This paper presents a simulator for recreating triaxial accelerometer measures of people falling in two circumstances: as a consequence of a loss of conscience or due to bumping into an obstacle. The objective of the simulator is to generate falling instances to train Machine Learning algorithms that can be incorporated into wearable devices. The developed simulator can generate triaxial accelerometer measures that exhibit similar patterns compared to falls recorded with real people and mannequins using a commercial device.