TY  - JOUR
PY  - 2021//
TI  - Event-centered data segmentation in accelerometer-based fall detection algorithms
JO  - Sensors (Basel)
A1  - Šeketa, Goran
A1  - Pavlaković, Lovro
A1  - Džaja, Dominik
A1  - Lacković, Igor
A1  - Magjarević, Ratko
SP  - s21134335
EP  - s21134335
VL  - 21
IS  - 13
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s21134335
ID  - ref1
ER  -