Location-Aware Fall Detection System for Medical Care Quality Improvement

Huang, Chih-Ning; Chiang, Chifa; Chang, J. W.; Chou, Yi-Chieh; Hong, Ya-Xuan; Hsu, Steen J.; Chu, Woei-Chyn; Chan, Chia-Tai

doi:10.1109/mue.2009.84

Cited by 13 publications

(1 citation statement)

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“…This vulnerable demographic faces hindrances in seeking aid due to a lack of technology access in rural areas and physical limitations [3]. Automatic fall detection systems emerge as pivotal solutions, enhancing the quality of life for older individuals and providing crucial living assistance [4]. The statistics reveal that 25% of individuals over 65 experience falls annually, a notable increase to 32%-42% for those over 70 [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Performance of ML Models for Fall Detection in Older People

Sefidi Esfahani,

Fattahian

2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%