Human Balance Estimation Using a Wireless 3D Acceleration Sensor Network

Heidi Similä, Jouni Kaartinen, Mikko Lindholm, Ari Saarinen, Ibrahim Mahjneh

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

    Abstract

    Balance and gait are a consequence of complex coordination between muscles, nerves, and central nervous system structures. The impairment of these functions can pose serious threats to independent living, especially in the elderly. This study was carried out to evaluate the performance of a wireless acceleration sensor network and its capability in balance estimation. The test has been carried out in eight patients and seven healthy controls. The Patients group had larger values in lateral amplitudes of the sensor displacement and smaller values in vertical displacement amplitudes of the sensor. The step time variations for the Patients were larger than those for the Controls. A fuzzy logic and clustering classifiers were implemented, which gave promising results suggesting that a person with balance deficits can be recognized with this system. We conclude that a wireless system is easier to use than a wired one and more unobtrusive to the user.
    Original languageEnglish
    Title of host publication2006 International Conference of the IEEE Engineering in Medicine and Biology Society
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages1493-1496
    ISBN (Print)1-4244-0032-5
    DOIs
    Publication statusPublished - 2006
    MoE publication typeA4 Article in a conference publication
    Event28th International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006 - New York, United States
    Duration: 30 Aug 20063 Sep 2006

    Conference

    Conference28th International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006
    Abbreviated titleEMBS 2006
    CountryUnited States
    CityNew York
    Period30/08/063/09/06

    Keywords

    • wireless sensor network
    • humans
    • sampling methods
    • time division multiple

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