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— The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient's motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A(More)
The World Health Organization warns that, in 2000, as many as 33 million Europeans suffered from diabetes, approximately 15% will likely develop foot ulcers, and approximately 15-20% of these patients will face lower-extremity amputation. Changes in some gait parameters that appear to be specific in diabetes have been identified in the literature: shorter(More)
This paper evaluates the use of Gaussian Mixture Model (GMM) trained through Electromyography (EMG) signals to online estimate the bending angle of a single human joint. The parameters involved in the evaluation are the number of Gaussian components, the channel used for model, the feature extraction method, and the size of the training set. The feature(More)
Diabetic peripheral neuropathy (DPN) causes motor control alterations during daily life activities. Tripping during walking or stair climbing is the predominant cause of falls in the elderly subjects with DPN and without (NoDPN). Surface Electromyography (sEMG) has been shown to be a valid tool for detecting alterations of motor functions in subjects with(More)
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