WSEAS Transactions on Biology and Biomedicine

Print ISSN: 1109-9518
E-ISSN: 2224-2902

Volume 14, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Intensive and Repetitive Training with Patient Active Participation though EMG-Controlled Robotic Hand Rehabilitation Device: Healthy Controls and Patients Validation

AUTHORS: Marta Gandolla, Simona Ferrante, Franco Molteni, Eleonora Guanziroli, Cristina Petitti Di Roreto, Carlo Seneci, Michele Cotti Cottini, Alessandra Pedrocchi

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ABSTRACT: The objective of this work is to describe and test a hand rehabilitation device with particular attention to the key ingredients for a successful neuro-motor rehabilitation training, and in particular: i) adjunctive high duration and intensity therapy sessions; ii) functional orientation of the training; and iii) patient active involvement. The developed system is composed by a PC, the Gloreha hand rehabilitation glove along with its dedicated screen for visual feedback during movements execution, and the MYO armband for EMG signals recording. Two different control approaches have been designed and implemented taking into account the residual muscular activity of the users: EMG trigger controller, and EMG task-selection classifier. Multiple degrees-of-freedom hand functional movements were alternatively triggered (i.e., when the EMG activity overcomes a predefined threshold, the hand robotic rehabilitation device supports the patient-triggered task) or predicted (i.e., two cascaded artificial neural networks were exploited to detect the patient’s motion intention from the EMG signal window starting from the electrical activity onset up to the movement onset) depending on the selected approach by means of surface EMG signals. The proposed control approaches were tested on nine healthy control subjects (7 females; age range 16-93 years) and a pilot group of four chronic post-stroke patients. All participants, both from the control group and the patients pilot group successfully calibrated and triggered Gloreha during the testing session using the EMG trigger controller. The EMG task-selection classifier demonstrated an overall mean ± SD testing performance of 80% ± 13% and 67% ± 16%. for correctly predicting healthy users’ and pilot post-stroke patient motion respectively. In the control group, the classifier performance was negatively correlated with age, and the pilot patient behaved similarly to elder participants.

KEYWORDS: Electromyography (EMG), EMG controller, artificial neural networks, hand rehabilitation, movement prediction, electromechanical delay


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WSEAS Transactions on Biology and Biomedicine, ISSN / E-ISSN: 1109-9518 / 2224-2902, Volume 14, 2017, Art. #5, pp. 29-37

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