Accelerometer-based fall detection using feature extraction and support vector machine algorithms. Issue 4 (3rd July 2016)

• Record Type: Journal Article
• Authors: Chen, Kuang-Hsuan; Yang, Jing-Jung; Jaw, Fu-Shan
• Is Part Of: Instrumentation science & technology. Volume 44:Issue 4(2016)
• Page Start: 333
• Page End: 342
• Publication Date: 2016-07-03
• Subjects: Accelerometer -- fall detection -- support vector machine; Scientific apparatus and instruments -- Periodicals; 681.2
• DOI: 10.1080/10739149.2015.1123161
• Languages: English
• ISSNs: 1073-9149

Abstract

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.