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%.
CITATION STYLE
Rahman, H., Sandberg, J., Eriksson, L., Heidari, M., Arwald, J., Eriksson, P., … Ahmed, M. U. (2016). Falling angel – A wrist worn fall detection system using K-NN algorithm. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 187, pp. 148–151). Springer Verlag. https://doi.org/10.1007/978-3-319-51234-1_25
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