The Novel Method of Pedestrian Fall Detection Based on PSO and RF Using Accelerometer Data

Abstract

Pedestrian fall detection attracting a lot of research due to its importance in building a warning system to avoid negative consequences. There are many techniques for fall detection that have using different devices such as cameras, environmental sensors, wearable sensors, etc. However, the popularity of smartphones with many embedded sensors has motivated research on detecting abnormal activity such as falling based on sensor data. High accuracy of fall detection in daily life activities is always a practical challenge. This paper put forward a new feature set that pulls out from time, frequency and Hjorth domains, which calculate from the smartphone’s accelerometer data. This study also employs Particle Swarm Optimization (PSO) to optimize the parameters of the Random Forest classification, which aims to improve the accuracy of the fall detection system. Experimental results on the MobiAct 2.0 dataset have shown that the put forward system has improved detection efficiency from 96.9% to 98.5% in F-measure. In addition, the detection efficiency in falls is 17% to 26.7% higher on the F-measure than the methods proposed by the authors C. Chatzaki et al.