Falling Angel a Wrist Worn Fall Detection System Using K-NN Algorithm
2016 (English)Conference paper (Refereed)
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, 148-151 p.
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211
Fall Detection, Angel Device, K-Nearest Neighbor
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:mdh:diva-33803DOI: 10.1007/978-3-319-51234-1_25ScopusID: 2-s2.0-85011312016OAI: oai:DiVA.org:mdh-33803DiVA: diva2:1048564
The 3rd EAI International Conference on IoT Technologies for HealthCare HealthyIoT'16, 18 Oct 2016, Västerås, Sweden
ProjectsSafeDriver: A Real Time Driver's State Monitoring and Prediction System