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Pregled bibliografske jedinice broj: 1134619

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


Šeketa, Goran; Pavlaković, Lovro; Džaja, Dominik; Lacković, Igor; Magjarević, Ratko
Event-Centered Data Segmentation in Accelerometer- Based Fall Detection Algorithms // Sensors, 21 (2021), 13; 4335, 18 doi:10.3390/s21134335 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1134619 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

Autori
Šeketa, Goran ; Pavlaković, Lovro ; Džaja, Dominik ; Lacković, Igor ; Magjarević, Ratko

Izvornik
Sensors (1424-8220) 21 (2021), 13; 4335, 18

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
fall detection ; event-centered data segmentation ; wearable sensors ; accelerometer ; window duration

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Ratko Magjarević (autor)

Avatar Url Dominik Džaja (autor)

Avatar Url Igor Lacković (autor)

Avatar Url Goran Šeketa (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi www.mdpi.com

Citiraj ovu publikaciju:

Šeketa, Goran; Pavlaković, Lovro; Džaja, Dominik; Lacković, Igor; Magjarević, Ratko
Event-Centered Data Segmentation in Accelerometer- Based Fall Detection Algorithms // Sensors, 21 (2021), 13; 4335, 18 doi:10.3390/s21134335 (međunarodna recenzija, članak, znanstveni)
Šeketa, G., Pavlaković, L., Džaja, D., Lacković, I. & Magjarević, R. (2021) Event-Centered Data Segmentation in Accelerometer- Based Fall Detection Algorithms. Sensors, 21 (13), 4335, 18 doi:10.3390/s21134335.
@article{article, author = {\v{S}eketa, Goran and Pavlakovi\'{c}, Lovro and D\v{z}aja, Dominik and Lackovi\'{c}, Igor and Magjarevi\'{c}, Ratko}, year = {2021}, pages = {18}, DOI = {10.3390/s21134335}, chapter = {4335}, keywords = {fall detection, event-centered data segmentation, wearable sensors, accelerometer, window duration}, journal = {Sensors}, doi = {10.3390/s21134335}, volume = {21}, number = {13}, issn = {1424-8220}, title = {Event-Centered Data Segmentation in Accelerometer- Based Fall Detection Algorithms}, keyword = {fall detection, event-centered data segmentation, wearable sensors, accelerometer, window duration}, chapternumber = {4335} }
@article{article, author = {\v{S}eketa, Goran and Pavlakovi\'{c}, Lovro and D\v{z}aja, Dominik and Lackovi\'{c}, Igor and Magjarevi\'{c}, Ratko}, year = {2021}, pages = {18}, DOI = {10.3390/s21134335}, chapter = {4335}, keywords = {fall detection, event-centered data segmentation, wearable sensors, accelerometer, window duration}, journal = {Sensors}, doi = {10.3390/s21134335}, volume = {21}, number = {13}, issn = {1424-8220}, title = {Event-Centered Data Segmentation in Accelerometer- Based Fall Detection Algorithms}, keyword = {fall detection, event-centered data segmentation, wearable sensors, accelerometer, window duration}, chapternumber = {4335} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


Citati:





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