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A ST-GCN based Motion recognition scheme for upper limb rehabilitation exoskeleton

Published: 05 April 2024 Publication History

Abstract

Stroke and its related complications such as hemiplegia and disability have placed a huge burden on human society in the 21st century, and different disability leads to personalized needs for rehabilitation and assistance in daily living. To addresses this issue, human-computer interaction (HMI) technology strives to detect and recognize the user's intention, to meet the user's needs through physical responses. In this work, the designed pressure film sensor array on the upper limb rehabilitation exoskeleton combined with our ST-GCN network is used to predict the patient's motion intention. Experimental results show that compared with the conventional method, the ST-GCN model meets the requirements in terms of accuracy, response time and model size, and the proposed method can be used as an optional scheme for the application of rehabilitation exoskeleton.

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    ISAIMS '23: Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science
    October 2023
    1394 pages
    ISBN:9798400708138
    DOI:10.1145/3644116
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 05 April 2024

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