Design on a wearable armband device for assessing the motion function of upper limbs Abstract   With the improvement of peoplexe2x80x99s living standards, peoplexe2x80x99s need for daily health monitoring is increasingly urgent, making human activity assessment based on wearable devices a new research hotspot in the field of pattern recognition and machine learning. In order to meet the needs of the rapid development of mobile medicine, the development of new armbandtype wearable upper limb motor function evaluation systems has become increasingly important. This paper designs a human motion signal acquisition system composed of terminal nodes and gateway nodes. The terminal node is worn on the arm part of the human body, collects movement data of the corresponding part, and receives commands from the gateway node wirelessly. Multiple terminal nodes work in an orderly manner under the coordination of the gateway nodes, and the data of each node in the same experiment maintains high synchronization. In addition, the gateway node is connected to the upper computer through the USB interface, and is responsible for the construction of the wireless communication network of the entire system and the forwarding of the commands from the upper computer. In the case of hardware circuit and software design, power consumption and performance tests were performed on the hardware nodes of the evaluation system, and upper limb movement functions were tested. The test results show that the system is safe and reliable.

RealTime Surface EMG Pattern Recognition for Hand Gestures Based on Support Vector Machine * In this paper, a realtime hand gesture recognition model based on surface electromyography signals is proposed. The Myo armband is applied to acquire surface electromyography (sEMG) signals generated by hand movement and we extract the features by using a sliding window to split the data. Then we select support vector machine (SVM) model for gesture recognition, which is trained by the training set and tested by testing set. We count the labels returned by classifier and assign the label whose times reach the threshold of recognition to the tested gesture. In order to evaluate this model, we set up an experiment with five target gestures which conducted by 12 subjects. Experimental results show that the average accuracy of proposed model can reach 97.8% and the recognition process is completed before the continuous action is finished, which means that this model can achieve realtime identification with high accuracy.