Phone based fall detection by genetic programming

Dau, H, Salim, F, Song, A, Hedin, L and Hamilton, M 2014, 'Phone based fall detection by genetic programming', in Seng W. Loke, Arkady Zaslavsky, Lars Kulik, Evaggelia Pitoura (ed.) Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, MUM 2014, Melbourne, Australia, 25-27 November 2014, pp. 256-257.


Document type: Conference Paper
Collection: Conference Papers

Title Phone based fall detection by genetic programming
Author(s) Dau, H
Salim, F
Song, A
Hedin, L
Hamilton, M
Year 2014
Conference name MUM 2014
Conference location Melbourne, Australia
Conference dates 25-27 November 2014
Proceedings title Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, MUM 2014
Editor(s) Seng W. Loke, Arkady Zaslavsky, Lars Kulik, Evaggelia Pitoura
Publisher Association for Computing Machinery (ACM)
Place of publication United States
Start page 256
End page 257
Total pages 2
Abstract Elderly people are prone to fall due to the high rate of risk factors associated with ageing. Existing fall detection sys- tems are mostly designed for a constrained environment, where various assumptions are applied. To overcome these drawbacks, we opt to use mobile phones with standard built- in sensors. Fall detection is performed on motion data col- lected by sensors in the phone alone. We use Genetic Pro- gramming (GP) to learn a classi er directly from raw sensor data. We compare the performance of GP with the popu- lar approach of using threshold-based algorithm. The result shows that GP-evolved classi ers perform consistently well across di erent fall types and overall more reliable than the threshold-based.
Subjects Pattern Recognition and Data Mining
Ubiquitous Computing
Keyword(s) Fall detection
genetic programming
mobile sensing
DOI - identifier 10.1145/2677972.2678010
Copyright notice © ACM 2014
ISBN 9781450333047
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