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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.
RESUMO
INTRODUCTION: The Oxfordshire Community Stroke Project (OCSP) proposed a clinical classification for Stroke patients. This classification has proved helpful to predict the risk of neurological complications. However, the OCSP was initially based on findings on the neurological assesment, which can pose difficulties for classifying patients. We aimed to describe the development and the validation step of a computer-based algorithm based on the OCSP classification. MATERIALS AND METHODS: A flow-chart was created which was reviewed by five board-certified vascular neurologists from which a computer-based algorithm (COMPACT) was developed. Neurology residents from 12 centers were invited to participate in a randomized trial to assess the effect of using COMPACT. They answered a 20-item questionnaire for classifying the vignettes according to the OCSP classification. Each correct answer has been attributed to 1-point for calculating the final score. RESULTS: Six-two participants agreed to participate and answered the questionnaire. Thirty-two were randomly allocated to use our algorithm, and thirty were allocated to adopt a list of symptoms alone. The group who adopted our algorithm had a median score of correct answers of 16.5[14.5, 17]/20 versus 15[13, 16]/20 points, p = 0.014. The use of our algorithm was associated with the overall rate of correct scores (p = 0.03). DISCUSSION: Our algorithm seemed a useful tool for any postgraduate year Neurology resident. A computer-based algorithm may save time and improve the accuracy to classify these patients. CONCLUSION: An easy-to-use computer-based algorithm improved the accuracy of the OCSP classification, with the possible benefit of further improvement of the prediction of neurological complications and prognostication.