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Article

Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
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Author to whom correspondence should be addressed.
Academic Editor: Paolo Napoletano
Sensors 2021, 21(13), 4335; https://doi.org/10.3390/s21134335
Received: 22 May 2021 / Revised: 16 June 2021 / Accepted: 23 June 2021 / Published: 24 June 2021
(This article belongs to the Special Issue Intelligent Biosignal Analysis Methods)
Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s. View Full-Text
Keywords: fall detection; event-centered data segmentation; wearable sensors; accelerometer; window duration fall detection; event-centered data segmentation; wearable sensors; accelerometer; window duration
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MDPI and ACS Style

Šeketa, G.; Pavlaković, L.; Džaja, D.; Lacković, I.; Magjarević, R. Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms. Sensors 2021, 21, 4335. https://doi.org/10.3390/s21134335

AMA Style

Šeketa G, Pavlaković L, Džaja D, Lacković I, Magjarević R. Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms. Sensors. 2021; 21(13):4335. https://doi.org/10.3390/s21134335

Chicago/Turabian Style

Šeketa, Goran, Lovro Pavlaković, Dominik Džaja, Igor Lacković, and Ratko Magjarević. 2021. "Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms" Sensors 21, no. 13: 4335. https://doi.org/10.3390/s21134335

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