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Advisor(s)
Abstract(s)
Falls are one of the causes of severe hilliness among elders, and the COVID-19 pandemic increased the number of unattended cases because of the social distancing measures. This study aims to create a dataset that collects the data from a 3-axis acceleration sensor fixed on a hinged board apparatus that mimics a human fall event. The datalogging system uses off-the-shelf devices to measure, collect and store the data. The resulting dataset includes data from different angle positions and heights, corresponding to joints of the lower limbs of the human body (ankle, knee, and hip). We use the dataset with a threshold-based fall detection algorithm. The result from the Receiver Operating Characteristic curve shows a good behavior with a mean Area Under the Curve of 0.77 and allow to compute a best threshold value with False Positive Rate of 14.8% and True Positive rate of 89.1%. The optimal threshold value may vary depending on the specific population, activity patterns, and environmental conditions, which may require further customization and validation in real-world settings.
Description
Keywords
Dataset Fall detection algorithm
Citation
DIONÍSIO, R.P. ; ROSA, A.R. ; JESUS, C.S.S. (2024) - Nyon-data, a fall detection dataset from a hinged board apparatus. In: BEN-AHMED, M. [et al.] (eds.) - 8th International Conference on Smart Cities Applications : Proceedings SCA 2023 : Innovations in smart cities applications. [S.l.] : Springer, Cham. Vol. 7, p. 391-401. DOI: https://doi.org/10.1007/978-3-031-53824-7_36
Publisher
Springer, Cham