Detection of the intention to grasp during reach movements

J.C. de Vries, A.L. van Ommeren (Corresponding Author), G.P. Prange-Lasonder, J.S. Rietman, P.H. Veltink

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    Abstract

    IntroductionSoft-robotic gloves have been developed to enhance grip to support stroke patients during daily life tasks. Studies showed that users perform tasks faster without the glove as compared to with the glove. It was investigated whether it is possible to detect grasp intention earlier than using force sensors to enhance the performance of the glove.MethodsThis was studied by distinguishing reach-to-grasp movements from reach movements without the intention to grasp, using minimal inertial sensing and machine learning. Both single-user and multi-user support vector machine classifiers were investigated. Data were gathered during an experiment with healthy subjects, in which they were asked to perform grasp and reach movements.ResultsExperimental results show a mean accuracy of 98.2% for single-user and of 91.4% for multi-user classification, both using only two sensors: one on the hand and one on the middle finger. Furthermore, it was found that using only 40% of the trial length, an accuracy of 85....
    Original languageEnglish
    JournalJournal of rehabilitation and assistive technologies
    Volume5
    DOIs
    Publication statusPublished - 18 Jan 2018

    Keywords

    • UT-Hybrid-D
    • grasp intention detection
    • inertial sensing
    • machine learning
    • soft-robotic glove
    • stroke
    • support vector machine
    • Assistive technology

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