Version 1
: Received: 26 December 2023 / Approved: 26 December 2023 / Online: 27 December 2023 (09:34:46 CET)
How to cite:
Sefidi Esfahani, M.; Fattahian, M. Comparative Study on Performance of ML Models for Fall Detection in Older People. Preprints2023, 2023122027. https://doi.org/10.20944/preprints202312.2027.v1
Sefidi Esfahani, M.; Fattahian, M. Comparative Study on Performance of ML Models for Fall Detection in Older People. Preprints 2023, 2023122027. https://doi.org/10.20944/preprints202312.2027.v1
Sefidi Esfahani, M.; Fattahian, M. Comparative Study on Performance of ML Models for Fall Detection in Older People. Preprints2023, 2023122027. https://doi.org/10.20944/preprints202312.2027.v1
APA Style
Sefidi Esfahani, M., & Fattahian, M. (2023). Comparative Study on Performance of ML Models for Fall Detection in Older People. Preprints. https://doi.org/10.20944/preprints202312.2027.v1
Chicago/Turabian Style
Sefidi Esfahani, M. and Mohammad Fattahian. 2023 "Comparative Study on Performance of ML Models for Fall Detection in Older People" Preprints. https://doi.org/10.20944/preprints202312.2027.v1
Abstract
Fall detection systems play a crucial role in addressing the significant health concern of elderly falls, a leading cause of health deterioration and mortality. As the aging population grows and life expectancy increases, the development of accessible tools becomes vital for predicting and preventing falls, offering a practical and widely applicable solution in contrast to costly and expertise-dependent assessment tools. In contrast, due to the formidable challenges encountered, a comprehensive investigation into the comparative performance of standard ML models within this field still needs to be explored. This paper proposes a standard pipeline for pre-processing, training, and evaluating ML models for fall detection on the SisFall dataset. We conducted extensive experiments to evaluate the performance of various ML models for fall detection. The results validate the efficiency of the deep model in identifying the time windows in which a fall occurred. Among the deep models, the architecture, including a combination of convolutional neural networks and fully connected layers, outperforms the others by macro-averaged Precision, macro-averaged Recall, and macro-averaged F1-Score of 87.03\%, 86.83\%, and 86.93\%, respectively.
Keywords
Fall Detection; Fall Detection System; Applied ML in Healthcare
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
