Pre-impact Alarm System for Fall Detection Using MEMS Sensors and HMM-based SVM Classifier

Accidental fall can cause physical injury, fracture and other health complication, especially for elderly people living alone. Aimed to provide timely assistance after the occurrence of falling down, a pre-fall alarm system was proposed. In order to test the reliability of pre-fall alarm system, eighteen subjects who worn this device on the waist were required to participate in a series of experiments. The acceleration and angular velocity time series extracted from human motion processes were used to described human motion features. HMM-based SVM classifier was used to determine the maximum separation boundary between fall and Activities of Daily Living (ADLs). The fall detection results showed 94.91% accuracy, 97.22% Sensitivity and 93.75% Specificity. The proposed device can accurately recognize fall event, achieve additional functions, and have advantages of small size and low power consumption. Based on the findings, this pre-impact fall alarm system with detection algorithm could potentially be useful for monitoring the state of physical function in elderly population.