An EventDriven ClosedLoop System for RealTime FES Control Active functional rehabilitation procedures, exoskeleton application and prosthesis employment are essential tools for the society life reinstatement of subjects affected by neuromuscular disorders or victims of serious physical accident. In this paper we propose a closedloop system for the control of the Functional Electrical Stimulation (FES) based on the Average Threshold Crossing (ATC) eventdriven processing technique applied to the sEMG signal. It allows an energy saving, low complex, and realtime muscle activity monitoring suitable for biofeedback lines applications. Two functional modalities are implemented: selfadministered stimulation, in which a person is able to selfstimulate herselfhimself, and twosubjects stimulation, that covers the standard rehabilitation flow in which both the therapist and the patient are involved. The system has been tested on 19 healthy subjects to evaluate its performance in term of reproducibility between the voluntary movement and the stimulated one, obtaining a median value of the correlation coefficient, used as similarity measurement, above 0.9 across a wide range of benchmark movements. The promising results confirm the feasibility of controlling myoelectric exoskeleton and prosthesis with the ATC processing approach.

A LowCost Arm Robotic Platform based on Myoelectric Control for Rehabilitation Engineering Rehabilitation robotics is a recent kind of service robot that include devices such as robotic prosthesis and exoskeletons. These devices could help motor disabled people to rehabilitate their motor functions, and could provide functional compensation to accomplish motor activities. In order to control robotic prosthesis and exoskeletons it is required to identify human movement intention, to be converted into commands for the device. Motor impaired people may use surface electromyography (sEMG) signals to control these devices, taking into account that sEMG signals directly reflects the human motion intention. Myoelectric control is an advanced technique related with the detection, processing, classification, and application of sEMG signals to control humanassisting robots or rehabilitation devices. Despite recent advances with myoelectric control algorithms, currently there is still an important need to develop suitable methods involving usability, for controlling prosthesis and exoskeletons in a natural way. Traditionally, acquiring EMG signals and developing myoelectric control algorithms require expensive hardware. With the advent of lowcost technologies (i.e. sensors, actuators, controllers) and hardware support of simulation software packages as Matlab, affordable research tools could be used to develop novel myoelectric control algorithms. This work describes the implementation and validation of a Matlabbased robotic arm using lowcost technologies such as Arduino commanded using myoelectric control. The platform permits implementation of a variety of EMGbased algorithms. It was carried out a set of experiments aimed to evaluate the platform, through an application of pattern recognition based myoelectric control to identify and execute seven movements of the robotic upper limb: 1forearm pronation; 2 forearm supination; 3wrist flexion; 4wrist extension; 5 elbow flexion; 6 elbow extension; 7resting. The algorithm use a feature extraction stage based on a combination of time and frequency domain features (mean absolute value, waveform length, root mean square) and a widely used kNN classifier. Obtained mean classification errors were 5.9%. As future work, additional features in the myoelectric control algorithm will be evaluated, for realtime applications.