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Falling Angel - a Wrist Worn Fall Detection System Using K-NN Algorithm
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1547-4386
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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%.

Place, publisher, year, edition, pages
2016. Vol. 187, p. 148-151
Series
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211
Keywords [en]
Fall Detection, Angel Device, K-Nearest Neighbor
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-33803DOI: 10.1007/978-3-319-51234-1_25ISI: 000428954100025Scopus ID: 2-s2.0-85011312016ISBN: 9783319512334 (print)OAI: oai:DiVA.org:mdh-33803DiVA, id: diva2:1048564
Conference
The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 18 Oct 2016, Västerås, Sweden
Projects
SafeDriver: A Real Time Driver's State Monitoring and Prediction SystemAvailable from: 2016-11-21 Created: 2016-11-21 Last updated: 2018-07-27Bibliographically approved

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Begum, ShahinaLindén, MariaAhmed, Mobyen Uddin

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Rahman, HamidurSandberg, JohanEriksson, LennartHeidari, MohammadBegum, ShahinaLindén, MariaAhmed, Mobyen Uddin
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Embedded Systems
Electrical Engineering, Electronic Engineering, Information Engineering

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