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Falling Angel – a Wrist Worn Fall Detection System Using K-NN Algorithm

Authors:

Hamidur Rahman, Johan Sandberg , Lennart Eriksson , Mohammad Heidari , Jan Arwald , Peter Eriksson , Shahina Begum, Maria Lindén, Mobyen Uddin Ahmed

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

The 3rd EAI International Conference on IoT Technologies for HealthCare

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%.

Bibtex

@inproceedings{Rahman4498,
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 Uddin Ahmed},
title = {Falling Angel – a Wrist Worn Fall Detection System Using K-NN Algorithm},
month = {October},
year = {2016},
booktitle = {The 3rd EAI International Conference on IoT Technologies for HealthCare},
url = {http://www.es.mdu.se/publications/4498-}
}