Phone based fall detection by genetic programming

Publication Year: 2014 Publication Type : ConferenceProceeding


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.


    author = {Anh Hoang Dau and Flora Dilys Salim and Andy Song and Lachlan Hedin and Margaret Hamilton},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, Melbourne, VIC, Australia, November 25-28, 2014},
    doi = {10.1145/2677972.2678010},
    editor = {Arkady B. Zaslavsky and Seng W. Loke and Lars Kulik and Evaggelia Pitoura},
    pages = {256--257},
    publisher = {ACM},
    timestamp = {Fri, 27 Mar 2020 00:00:00 +0100},
    title = {Phone based fall detection by genetic programming},
    url = {},
    year = {2014}


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