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

Kuang-Hsuan Chen, Jing-Jung Yang,
Published: 11 December 2015

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.
Keywords: Accelerometer / fall detection / support vector machine

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