Stroke and spinal cord injury are two common disease, leading to increasing morbidity and disability in recent years. Exercise therapy plays an irreplaceable role for the rehabilitation of patients with neurological injury. The implementation of exercise therapy is shifting from traditional therapists assist patients with exercise training to rehabilitation robots help patients perform exercise training. The clinical studies have demonstrated that active training have a more positive effect on the motor function recovery and neurological rehabilitation of patients. How to motivate the patients' voluntary participation in the rehabilitation training and obtain the patients' motion intention in real-time is the key to achieving human-machine coordinated control. This paper uses surface EMG as the primary means to explore how to establish an effective human-machine interface and motion control strategy on the rehabilitation robot platform. In addition, pilot studies on rehabilitation training effect and rehabilitation evaluation of robot applied to clinical practice are conducted. The main contributions of this dissertation are as follows:
1. The quantitative relationship between surface EMG and joint active torques are established based on neural network, and the robot-assisted active training for lower limb rehabilitation is achieved in order to fully stimulate the enthusiasm of patients with rehabilitation training. Since kinetic signals always lag behind the real motion intention, the method proposed in this study uses the normalized value of EMG, joint angle, joint angular velocity as inputs to the BP neural networks to estimate the active torques of hip and knee joints. Then the active torques are converted into angular deviations based on admittance control to correct the current trajectory. This kind of exercise training could improve patients' active participation in the rehabilitation training.
2. A simplified EMG-driven musculoskeletal model is proposed to simulate and predict the movement of knee joint, and two robot-assisted exercise training methods for knee rehabilitation are developed. The EMG-driven model approximates the knee joint as a single hinge joint with a center of rotation, which uses the simplified representation of musculoskeletal system to dynamically analyze the muscle forces and torque. The dual population genetic algorithm is applied to calibrate the model parameters to reduce risk of fallen into local minimum. Then the ``patient-driven'' voluntary exercise training methods are achieved based on admittance control.
3. An effective feature processing method based on the bag-of-words (BoW) model is proposed for improving the accuracy of EMG pattern recognition. The feature extracted from raw EMG is reformulated by the BoW model prior to classification, which extracts structural similarity information within the same class and make the characteristics between different classes more discriminative. Then the structural information is combined into a new feature vector and input to the classifier. The experimental results show that the feature processing method significantly improves the classification accuracies for 5-class natural dynamic motions of shoulder and elbow joints.
4. The effectiveness of robot-assisted therapy is verified by conducting the clinical trials about upper limb robot-assisted rehabilitation training of stroke patients. The robot-based rehabilitation evaluation is also conducted based on patient's motion parameters and surface EMG. The clinical results show that the robot-assisted therapy has a positive effect on improving the upper limb motor function and activities of daily living in stroke patients. Meanwhile, the sensorimotor recovery of upper limb is analyzed at the behavioral level. The Fugl-Meyer scale score could be approximately estimated by extracting motion parameters to establish linear regression models. The contraction timing and participation of each muscle are analyzed using surface EMG, and points out that surface EMG could provide objective and quantitative evaluation on limb functions in stroke patients.
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