Using accelerometers for physical actions recognition by a neural fuzzy network

Telemedicine Journal and E-health : the Official Journal of the American Telemedicine Association
Shing-Hong Liu, Yuan-Jen Chang

Abstract

Triaxial accelerometers were employed to monitor human actions under various conditions. This study aimed to determine an optimum classification scheme and sensor placement positions for recognizing different types of physical action. Three triaxial accelerometers were placed on the chest, waist, and thigh, and their abilities to recognize the three actions of walking, sitting down, and falling were determined. The features of the resultant triaxial signals from each accelerometer were extracted by an autoregression (AR) model. A self-constructing neural fuzzy inference network (SONFIN) was used to recognize the three actions. The performance of the SONFIN was assessed based on statistical parameters, sensitivity, specificity, and total classification accuracy. The results show that the SONFIN provided a stability total classification accuracy of 96.3% and 88.7% for the training and testing data, when the parameters of the 60-order AR model were used as the input feature vector, and the accelerometer was placed anywhere on the abdomen. Seven elderly individuals performing the three basic actions had 80.4% confirmation for the testing data.

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Citations

May 31, 2011·Archives of Physical Medicine and Rehabilitation·Vivian H CheungMohanraj Karunanithi
Nov 16, 2013·IEEE Journal of Biomedical and Health Informatics·Wen-Chang Cheng, Ding-Mao Jhan
Jul 22, 2016·Computers in Biology and Medicine·Laetitia Fradet, Frederic Marin
Feb 10, 2018·The International Journal of Behavioral Nutrition and Physical Activity·Kieran P DowdAlan E Donnelly
Mar 31, 2011·Medicine and Science in Sports and Exercise·Sanne I de VriesFrancisca Galindo Garre
Oct 24, 2014·Physiological Measurement·Michael B Del RosarioStephen J Redmond
Sep 27, 2019··Wenqiang CaiWanyue Li

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