Prof. Mario Cesarelli, Prof. Paolo Bifulco, Prof. Gianni D’Addio, Prof. Paolo Gargiulo, Prof. Dario Gargiulo.

Goals

• Gait analysis’ parameters to classify Parkinsonism;
• Design and assessment of a low-cost, electromyographically controlled prosthetic hand;
• Regent Suit-based rehabilitation on gait EMG patterns in hemiparetic subjects;
• Analysis of the Power Spectrum of Motor Unit Action Potential Trains.

Results

• The implemented algorithms obtained an accuracy greater than 80%, with one of them, namely the Gradient Boosted Tree, achieving the best accuracy (93.5%) and specificity (93.5%) in predicting the presence of Freezing of Gait;
• Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods;
• The proprioceptive stimulation of the RS-based treatment induces higher and remarkable restoration of the normal muscle activation timing;
• EMG rectification significantly alters the spectrum characteristics.

Keywords

Electromyography, gait analysis, rehabilitation, robot assisted exercises, foetal heart rate processing, prosthesis.

Collaborations

Fondazione Salvatore Maugeri, BMC, Western Sydney University.