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.

Recognition of Finger Motions Based on Surface Electromyographic Signals and Artificial Neural Network Surface electromyographic (sEMG) signals can be used in medicine, rehabilitation, robotics and industrial fields. This paper proposes a recognition method of finger motions for dexterous control of prosthetic hands. In this work, we record the corresponding sEMG signals from five subjects, and then set up the model by data preprocessing, feature extraction and classification. The results show that high recognition accuracy can be achieved by using time domain feature extraction and artificial neural network. It means that finger motions can be decoded accurately. To acquire the tradeoff between the number of channels and the recognition accuracy, we apply channel number reduction and it is found that the number of channels is at least 7 with recognition accuracy of 90.52%.