An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

作     者：Putra, I. Putu Edy Suardiyana Brusey, James Gaura, Elena Vesilo, Rein 

作者机构：Macquarie Univ Sch Engn Sydney NSW 2109 Australia Coventry Univ Fac Engn Environm & Comp Coventry CV1 5FB W Midlands England 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2018年第18卷第1期

页      面：20-20页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：International Macquarie University Research Excellence Scholarship (iMQRES) Coventry University studentship, under a co-tutelle scheme 

主　　题：fall detection accelerometer sensors segmentation technique fall stages machine learning computational cost 

摘      要：The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW-and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.