Research Article
Falling Angel – A Wrist Worn Fall Detection System Using K-NN Algorithm
265 downloads
@INPROCEEDINGS{10.1007/978-3-319-51234-1_25, author={Hamidur Rahman and Johan Sandberg and Lennart Eriksson and Mohammad Heidari and Jan Arwald and Peter Eriksson and Shahina Begum and Maria Lind\^{e}n and Mobyen Ahmed}, title={Falling Angel -- A Wrist Worn Fall Detection System Using K-NN Algorithm}, proceedings={Internet of Things Technologies for HealthCare. Third International Conference, HealthyIoT 2016, V\aa{}ster\ae{}s, Sweden, October 18-19, 2016, Revised Selected Papers}, proceedings_a={HEALTHYIOT}, year={2017}, month={1}, keywords={Fall detection Angel device k-Nearest Neighbor}, doi={10.1007/978-3-319-51234-1_25} }- Hamidur Rahman
Johan Sandberg
Lennart Eriksson
Mohammad Heidari
Jan Arwald
Peter Eriksson
Shahina Begum
Maria Lindén
Mobyen Ahmed
Year: 2017
Falling Angel – A Wrist Worn Fall Detection System Using K-NN Algorithm
HEALTHYIOT
Springer
DOI: 10.1007/978-3-319-51234-1_25
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%.
Copyright © 2016–2024 EAI
