Adaptive Robot Mediated Upper Limb Training Using Electromyogram Based Muscle Fatigue Indicators

Azeemsha Thacham-Poyil, Volker Steuber, Farshid Amirabdollahian

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Studies on improving the adaptability of upper limb rehabilitation training do not often consider the implications of muscle fatigue sufficiently. In this study, electromyogram features were used as fatigue indicators in a context of human-robot interaction, and were utilised for auto-adaptation of the task difficulty, which resulted in a prolonged training interaction.The electromyogram data was collected from three gross-muscles of the upper limb in 30 healthy participants.The experiment followed a protocol for increasing the muscle strength by progressive strength training, that was an implementation of a known method in sports science for muscle training, in a new domain of robotic adaptation in muscle training.The study also compared how the change in task difficulty levels was perceived by the participants, when the robot adjusted the difficulty, when the difficulty was manually adjusted, and also when there was no difficulty adjustment at all.Three experimental conditions were chosen, one benefiting from robotic adaptation (Intervention group) and the other two presenting control groups 1 and 2.The results indicated that the participants could perform a prolonged progressive strength training exercise with more repetitions with the help of a fatigue-based robotic adaptation, compared to the training interactions, which were based on manual/no adaptation.This study showed that using fatigue indicators, it is possible to alter the level of challenge, and thus, increase the interaction time.The results of the study are expected to be extended to stroke patients in the future by utilizing the potential for adapting the training difficulty according to the patient's muscular state, and also to have large number repetitions in a robot-assisted training environment.
Original languageEnglish
Article numbere0233545
Number of pages33
JournalPLoS ONE
Issue number5
Publication statusPublished - 29 May 2020


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