A Novel Heuristic Fall-Detection Algorithm Based on Double Thresholding, Fuzzy Logic, and Wearable Motion Sensor Data
Billur Barshan, Mustafa Şahin Turan
Abstract
We present a novel heuristic fall-detection algorithm based on combining double thresholding of two simple features with fuzzy logic techniques. We extract the features from the acceleration and gyroscopic data recorded from a waist-worn motion sensor unit. We compare the proposed algorithm to 15 state-of-the-art heuristic fall-detection algorithms in terms of five performance metrics and runtime on a vast benchmarking fall data set that is publicly available. The data set comprises recordings from 2880 short experiments (1600 fall and 1280 non-fall trials) with 16 participants. The proposed algorithm exhibits superior average accuracy (98.45%), sensitivity (98.31%), and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> -measure (98.59%) performance metrics with a runtime that allows real-time operation. Besides proposing a novel heuristic fall-detection algorithm, this work has comparative value in that it provides a fair comparison on the relative performances of a considerably large number of existing heuristic algorithms with the proposed one, based on the same data set. The results of this research are encouraging in the development of fall-detection systems that can function in the real world for reliable and rapid fall detection.