Abstract
A wrist worn fall detection system has been developed where the accelerometer data from an angel sensor is analyzed by a two-layered algorithm in an android phone. Here, the first layer uses a threshold to find potential falls and if the thresholds are met, then in the second layer a machine learning i.e., k-Nearest Neighbor (k-NN) algorithm analyses the data to differentiate it from Activities of Daily Living (ADL) in order to filter out false positives. The final result of this project using the k-NN algorithm provides a classification sensitivity of 96.4%. Here, the acquired sensitivity is 88.1% for the fall detection and the specificity for ADL is 98.1%.
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“MATLAB Computer Vision Toolbox,” R2013a ed: The MathWorks Inc., pp. Natick, Massachusetts, United States.
References
Fallolyckor. Myndigheter för sammh ällskydd och beredskap, statistic och analys, MSB752. https://www.msb.se/RibData/Filer/pdf/27442.pdf
W. H. Organization: Who global report on falls prevention in older age. http://www.who.int/ageing/publications/Falls_prevention7March.pdf
Fahmi, P.N.A., Viet, V., Deok-Jai, C.: Semi-supervised fall detection algorithm using fall indicators in smartphone. In: 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2012, pp. 122:1–122:9 (2012)
Fudickar, S., Karth, C., Mahr, P., Schnor, B.: Fall-detection simulator for accelerometers with in-hardware preprocessing. In: 5th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2012, New York, NY, USA, pp. 41:1–41:7 (2012)
Vilarinho, T., Farshchian, B., Bajer, D.G., Dahl, O.H., Egge, I., Hegdal, S.S., et al.: A combined smartphone and smartwatch fall detection system. In: IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp. 1443–1448, October 2015
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46, 175–185 (1992)
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We would like to express our gratitude to all the participants, who give their time and data.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Rahman, H. et al. (2016). Falling Angel – A Wrist Worn Fall Detection System Using K-NN Algorithm. In: Ahmed, M., Begum, S., Raad, W. (eds) Internet of Things Technologies for HealthCare. HealthyIoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-319-51234-1_25
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DOI: https://doi.org/10.1007/978-3-319-51234-1_25
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