Browsing by Author "Carrozza, Maria Chiara"

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    An Adaptive Prosthetic Hand with Compliant Joints and EMG-based Control
    (2005) Carrozza, Maria Chiara; Zaccone, Franco; Micera, Silvestro; Cappiello, Giovanni; Stellin, Giovanni; Vecchi, Fabrizio; Dario, Paolo
    In this paper some recent results about the experimental trials we are performing on a functional prosthetic hand characterized by an EMG-control and by a simple and low cost fabrication technology are shown. A compliant under-actuated prosthetic hand has been designed and fabricated. The five-fingered hand (both palm and fingers) is moulded as a soft polymeric single part with compliant joints and embedded tendon driven under-actuated mechanism for providing adaptive grasp. The maximum measured cylindrical grasping force is 30 N. The one DoF prosthetic hand is controlled using two pre-amplified EMG electrodes. The proposed EMG-based control is a Finite State Machine (FSM). A particular attention has been given to the calibration phase. In order to identify the end of the grasp, the intensity of the current is monitored. Moreover, the microcontroller stops the motor when the average current overcomes the value imposed. Compared to other EMG based controllers, the approach proposed is very simple but it presents a good robustness and needs a minimum computational cost.
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    Preliminary Study On The Influence Of Inertia And Weight Of The Prosthesis On The Emg Pattern Recognition Robustness
    (2011) Cipriani, Christian; Sassu, Rossella; Controzzi, Marco; Kanitz, Gunter; Carrozza, Maria Chiara
    For transradial amputees, the muscles in the residual forearm naturally employed by unimpaired subjects for flexing/extending the hand fingers, are the most appropriate targets, for multi-fingered prostheses control. However, once the prosthetic socket is manufactured and fitted on the residual forearm, the recorded EMG might not be originated only by the intention of performing finger movements, but also by the muscular activity needed to sustain the prosthesis itself. In this work, we preliminary show –on healthy subjects wearing a prosthetic socket emulator– that (i) variations in the weight of the prosthesis, and (ii) upper arm movements significantly influence the robustness of a traditional classifier based on k-nn algorithm. We show in simulated conditions that traditional pattern recognition systems do not allow to separate the effects of the weight of the prosthesis because a surface recorded EMG pattern due only to the lifting or moving of the prosthesis is misclassified into a hand control movement. This suggests that a robust classifier should add to myoelectric signals, inertial transducers like multi-axes position, acceleration sensors or sensors able to monitor the interaction forces between the socket and the end-effector.