Intelligent wearable rehabilitation robot control system based on mobile communication network Abstract   A large number of disabled people are caused by major diseases and accidents. Because disabled patients cannot exercise freely on their own, in order to prevent muscle atrophy, the most effective treatment is to exercise the patientsxe2x80x99 limbs. Therefore, it is necessary to choose a more suitable rehabilitation training method to replace the traditional artificial rehabilitation training method, and the use of intelligent wearable rehabilitation robots to treat hemiplegia patients is particularly important for the rehabilitation treatment of hemiplegia patients. Rehabilitation robots can promote the development of medical technology and medical equipment, and have important practical significance for patients to overcome disease and recover health and build a harmonious family. This paper mainly studies the EMG research of the control system of intelligent wearable rehabilitation robot based on mobile communication network. This paper studies the way of stimulating signals and the processing of signal feedback in the process of electromyographic stimulation of rehabilitation robots to improve the rehabilitation effect of robots after stimulation; explores the problem of using biofeedback and fuzzy control rules to control patients for rehabilitation training, which urges patients actively participate in rehabilitation treatment, effectively guide the recovery of patientsxe2x80x99 selfconsciousness, and build a variety of training modes for rehabilitation robots to enhance the robotxe2x80x99s ability to adapt to different groups of people. In the experiments of this paper, the time window for data processing is 256ms, and the delay is also 256ms. It can be seen that the original SEMG signal of the control signal has some delay compared with the filtered SEMG signal.

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.