An event-triggered machine learning approach for accelerometer-based fall detection

I. Putu Edy Suardiyana Putra*, James Brusey, Elena Gaura, Rein Vesilo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)
17 Downloads (Pure)

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.

Original languageEnglish
Article number20
Pages (from-to)1-18
Number of pages18
JournalSensors
Volume18
Issue number1
DOIs
Publication statusPublished - Jan 2018

Bibliographical note

Copyright the Author(s)2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • fall detection
  • accelerometer sensors
  • segmentation technique
  • fall stages
  • machine learning
  • computational cost

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