Focusing on upper limb rehabilitation of weak stroke patients, this pilot study explores how motion intent can be detected using force sensitive resistors (FSR). This is part of a bigger project which will see the actuation and control of an intent-driven exoskeleton. The limited time stroke survivors have with their therapists means that they can not often get enough training. During active-assisted training, therapists guide the paralysed limb through a movement only after detecting visual or haptic cues of the motion intent from the patient. Aiming to replicate therapist practices of recognising patients’ intention to move, a pilot study of a tactile system is performed. The system will perform consistently even with patients who have low muscle strength and control ability. Currently available devices for detecting muscle activity do not offer the robustness and performance necessary; Electromyography (EMG) sensors, a well-established method, is affected by factors like skin moisture and BCI (Brain Computer Interface) has a slow response time. The proposed tactile sensing system is a simple yet robust solution both from a sensing as well as a usability point of view. Pilot experiments have been performed with a healthy subject emulating low muscle activation conditions. An overall accuracy of 80.45% is achieved when detecting forearm and arm muscle contractions and hence motion intent.


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    Title :

    Upper Limb Motion Intent Recognition Using Tactile Sensing


    Contributors:

    Publication date :

    2018-02-01


    Remarks:

    Stefanou , T , Turton , A J , Lenz , A & Dogramadzi , S 2018 , Upper Limb Motion Intent Recognition Using Tactile Sensing . in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) : Proceedings of a meeting held 24-28 September 2017, Vancouver, British Columbia, Canada . , 8206573 , Institute of Electrical and Electronics Engineers (IEEE) , pp. 6601-6608 . https://doi.org/10.1109/IROS.2017.8206573



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629



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