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• W4289731029 abstract "Therapeutic strategies capable of inducing and enhancing prosthesis embodiment are a key point for better adaptation to and acceptance of prosthetic limbs. In this study, we developed a training protocol using an EMG-based human-machine interface (HMI) that was applied in the preprosthetic rehabilitation phase of people with amputation. This is a case series with the objective of evaluating the induction and enhancement of the embodiment of a virtual prosthesis. Six men and a woman with unilateral transfemoral traumatic amputation without previous use of prostheses participated in the study. Participants performed a training protocol with the EMG-based HMI, composed of six sessions held twice a week, each lasting 30 mins. This system consisted of myoelectric control of the movements of a virtual prosthesis immersed in a 3D virtual environment. Additionally, vibrotactile stimuli were provided on the participant's back corresponding to the movements performed. Embodiment was investigated from the following set of measurements: skin conductance response (affective measurement), crossmodal congruency effect (spatial perception measurement), ability to control the virtual prosthesis (motor measurement), and reports before and after the training. The increase in the skin conductance response in conditions where the virtual prosthesis was threatened, recalibration of the peripersonal space perception identified by the crossmodal congruency effect, ability to control the virtual prosthesis, and participant reports consistently showed the induction and enhancement of virtual prosthesis embodiment. Therefore, this protocol using EMG-based HMI was shown to be a viable option to achieve and enhance the embodiment of a virtual prosthetic limb." @default.
• W4289731029 created "2022-08-04" @default.
• W4289731029 creator A5012639325 @default.
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• W4289731029 date "2022-08-04" @default.
• W4289731029 modified "2023-09-30" @default.
• W4289731029 title "Embodiment of a virtual prosthesis through training using an EMG-based human-machine interface: Case series" @default.
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• W4289731029 doi "https://doi.org/10.3389/fnhum.2022.870103" @default.
• W4289731029 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35992955" @default.
• W4289731029 hasPublicationYear "2022" @default.
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