Applications of the PowerGlove

J.C. van den Noort, N. van Beek, D.F. Stegeman, T. van der Kraan, H.E.J. Veeger, H. Maas, K. van Dijk, R. Verhagen, L.J. Bour, T. Heida, H.G. Kortier, P.H. Veltink

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

    2 Citations (Scopus)
    65 Downloads (Pure)


    The hand is important in many daily life activities. During aging, quality of fine motor control of hand and fingers is decreasing. Also motor symptoms of the hand are important to define for instance the neurological state of a Parkinson’s disease patient. Although objective and reliable measurement of hand and finger dynamics is of interest, current measurement systems are limited. This paper describes the application of the PowerGlove, a new measurement system based on miniature inertial and magnetic sensors, to study the finger interdependency in healthy elderly and objectively quantify hand motor symptoms in Parkinson’s disease. Results of pilot experiments in young healthy subjects are shown to evaluate the feasibility of the applications.
    Original languageEnglish
    Title of host publication13th International Symposium on 3D Analysis of Human Movement, 3D-AHM 2014
    EditorsK. Aminian
    Place of PublicationLausanne
    PublisherEcole Polytechnique Fédérale de Lausanne
    Number of pages4
    ISBN (Print)9782880748562
    Publication statusPublished - 14 Jul 2014
    Event13th International Symposium on 3D Analysis of Human Movement, 3D-AHM 2014 - Lausanne, Switzerland
    Duration: 14 Jul 201417 Jul 2014
    Conference number: 13

    Publication series

    NameInternation Symposium on 3D Analysis of Human Movement
    PublisherEcole Polytechnique Federale de Lausanne


    Conference13th International Symposium on 3D Analysis of Human Movement, 3D-AHM 2014
    Abbreviated title3D-AHM
    Internet address


    • EWI-24909
    • BSS-Biomechatronics and rehabilitation technology
    • instrumented glove
    • healthy aging
    • METIS-305945
    • Hand kinematics
    • Parkinson’s disease
    • IR-91440
    • Inertial movement sensing

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