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Sensors 2018, 18(1), 20;

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

School of Engineering, Macquarie University, Sydney 2109, Australia
Faculty of Engineering, Environment & Computing, Coventry University, CV1 5FB Coventry, UK
Author to whom correspondence should be addressed.
Received: 13 November 2017 / Revised: 15 December 2017 / Accepted: 18 December 2017 / Published: 22 December 2017
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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. View Full-Text
Keywords: fall detection; accelerometer sensors; segmentation technique; fall stages; machine learning; computational cost fall detection; accelerometer sensors; segmentation technique; fall stages; machine learning; computational cost

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Putra, I.P.E.S.; Brusey, J.; Gaura, E.; Vesilo, R. An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection. Sensors 2018, 18, 20.

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