Article thumbnail

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

By I Putu Edy Suardiyana Putra, James Brusey, Elena Gaura and Rein Vesilo

Abstract

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

Topics: fall detection, accelerometer sensors, segmentation technique, fall stages, machine learning, computational cost, Chemical technology, TP1-1185
Publisher: MDPI AG
Year: 2017
DOI identifier: 10.3390/s18010020
OAI identifier: oai:doaj.org/article:71e942b5eb9943af87f07fdb9677a0c7
Journal:
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • https://doaj.org/toc/1424-8220 (external link)
  • https://doaj.org/article/71e94... (external link)
  • https://www.mdpi.com/1424-8220... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.