Surface ElectromyographyControlled Automobile Steering Assistance Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)controlled steering assistance with pathfollowing accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMGcontrolled steering assistance by validating the Myo armband, a massproduced sEMGbased interface, with respect to the pathfollowing accuracy of a commercially available automobile. It was hypothesized that onehanded remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of lowspeed field testing indicate that the Myo armband had lower pathfollowing accuracy than the steering wheel during a 90xc2xb0 turn and wide Uturn at twice the minimum turning radius, the Myo armband had superior pathfollowing accuracy for a narrow Uturn at the minimum turning radius and a 45xc2xb0 turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.

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.