SpinSafe: An unsupervised smartphone-based wheelchair path monitoring system

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    3 Citations (Scopus)
    82 Downloads (Pure)

    Abstract

    Movement and social life of wheelchair users are constrained by their disability and suitability of paths they can move on. Modern electric wheelchairs offer them assisted drive, making their movement easier and longer. They, however, do not prevent accidents, injuries, and inconveniences caused by path roughness and ramp slopes. Providing information about suitability and accessibility of paths and buildings for wheelchair users will enable them to beforehand plan their trip to not to be caught by surprises or not to take a trip all together. The recent emergence of smartphones equipped with inertial sensors offers new opportunities for provision of information regarding quality and accessibility of paths and buildings for wheelchair users. To this end, we propose a smartphone-based participatory system incorporating a hybrid unsupervised machine learning technique based on Self Organized Maps (SOM) to identify path conditions and to create clusters of similar path types. Our solution provides useful information about the angle of the ramp and curb slopes as well as pavement quality and roughness and path types.
    Original languageUndefined
    Title of host publicationProceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Pages1-6
    Number of pages6
    ISBN (Print)978-1-5090-1941-0
    DOIs
    Publication statusPublished - 14 Mar 2016
    EventIEEE International Conference on Pervasive Computing and Communication, PerCom 2016 - Sydney, Australia
    Duration: 14 Mar 201618 Mar 2016
    http://www.percom.org/Previous/ST2016/

    Publication series

    Name
    PublisherIEEE Computer Society

    Conference

    ConferenceIEEE International Conference on Pervasive Computing and Communication, PerCom 2016
    Abbreviated titlePerCom
    CountryAustralia
    CitySydney
    Period14/03/1618/03/16
    Internet address

    Keywords

    • CAES-PS: Pervasive Systems
    • wavelet
    • EWI-27001
    • IR-100649
    • Anomaly Detection
    • METIS-317198
    • Decomposition
    • Signal processing
    • Unsupervised machine learning
    • Visualization
    • Data Analysis

    Cite this