Wearable-sensor-based pre-impact fall detection system with a hierarchical classifier

Fall is a major threat to elder health, and fall detection has attracted considerable research attention recently. In our study, a novel method is proposed to detect falls prior to impact during walking. Angle and angular-velocity data from the waist and thigh are collected using two wearable sensors. By extracting and selecting distinctive features, we aim to identify falls at an early stage. To improve detection accuracy and reduce false alarms, a hierarchical classifier based on Fisher discriminant analysis is developed. With the hierarchical classifier, human activities are classified into three categories: non-fall, backward fall and forward fall. It can achieve average lead times of 376 ms for backward fall and 404 ms for forward fall. Meanwhile, it can achieve a sensitivity of 95.5% and specificity of 97.3%. The method can achieve a high accuracy for classifier, and a long lead time for pre-impact fall detection. Compared with single-sensor-based methods, the multi-sensor-based method achieves a better performance. The preliminary results indicate that our study has potential application in a fall-injury prevention system.