Accelerometer-based fall detection using feature extraction and support vector machine algorithms

Abstract

Falls by the elderly may result in hip fractures, paraplegia, and even death. Hence, over the past few decades, considerable research has been conducted on fall detection. Here, an accelerometer-based fall detector is reported that is fastened to a person's waist and includes an accelerometer, a multiplexer, a fifth-order low-pass Butterworth filter, and a microcontroller. Acceleration sensing, noise filtering, and analog-to-digital conversion were performed by the circuitry. The processed signal was sent to a personal computer through Bluetooth and analyzed by customized software. The fall detection algorithm included feature extraction and a support vector machine algorithm for classifying the features. Twenty volunteers performed 12 trials of 6 daily activities and 6 fall events. The results show that the algorithm had high sensitivity (95%) and specificity (96.7%). Thus, this device is expected to have significant application for fall detection.