Shape memory alloy (SMA) is a kind of active deformation material with a self-sensing and driving ability. It is very similar to the performance of human muscles, and through temperature changes to produce phase changes to output force and displacement, it has the ability to restore the initial shape and size. The combination of SMA and wearable robotic technology has the advantages of being light weight, energy-saving, and having great human–exoskeleton interaction. However, the existing flexible exoskeletons driven by SMA are only designed with bionic primary muscles, ignoring the role of antagonistic muscles. This study presents a novel soft bionic elbow exoskeleton based on SMA spring actuators (Sobee-SMA). The exoskeleton adopts a bionic design, combining active deformation material SMA and a high-elastic-material rubber band to simulate the contraction and relaxation of elbow skeletal muscles. Through a pulse width modulation (PWM) experiment, the driving voltage is selected as 12 V, the PWM duty cycle is 90 % during heating, and the PWM duty cycle is 18 % during heat preservation. In a relaxed state of healthy subjects, the range of motion of the elbow is about 0–80^∘, and the maximum temperature is about 60–70 ^∘C. During the circular movement of the elbow, the maximum temperature can be maintained within the SMA operating temperature without a high temperature. In conclusion, the exoskeleton provides elbow-assisted motion and ensures the safety of the heating process.

This paper presents the modeling design method for a novel hybrid-driven compliant hand exoskeleton based on the human-machine coupling model for the patients who have requirements on training and assisting. Firstly, the human-machine coupling model is established based on the kinematics characteristics of human fingers and the Bernoulli beam formula. On this basis, the variable stiffness flexible hinge (VSFH) is used to drive the finger extension and the cable-driven mechanism is used to implement the movement of the finger flexion. Here, a hand orthosis is designed in the proposed hand exoskeleton to act as the base and maintain the function position of the hand for patients with hand dysfunction. Then, a final design prototype is fabricated to evaluate the proposed modeling method. In the end, a series of experiments based on the prototype is proceeded to evaluate its capabilities on stretching force for extension, bio-imitability, finger flexion capability, and fingertip force. The results show that the prototype has a significant improvement in all aspects of the ability mentioned above, and has good bionics. The proposed design method can be utilized to implement the rapid design of the hybrid-driven compliant hand exoskeleton with the changed requirements. The novel modeling method can be easily applied in personalized design in rehabilitation engineering.

The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early intervention and optimal assistance for stroke patients. This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient's height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients.

To help hemiplegic patients with stroke to restore impaired or lost upper extremity functionalities efficiently, the design of upper limb rehabilitation robotics which can substitute human practice becomes more important. The aim of this work is to propose a powered exoskeleton for upper limb rehabilitation based on a wheelchair in order to increase the frequency of training and reduce the preparing time per training. This paper firstly analyzes the range of motion (ROM) of the flexion/extension, adduction/abduction, and internal/external of the shoulder joint, the flexion/extension of the elbow joint, the pronation/supination of the forearm, the flexion/extension and ulnar/radial of the wrist joint by measuring the normal people who are sitting on a wheelchair. Then, a six-degree-of-freedom exoskeleton based on a wheelchair is designed according to the defined range of motion. The kinematics model and workspace are analyzed to understand the position of the exoskeleton. In the end, the test of ROM of each joint has been done. The maximum error of measured and desired shoulder flexion and extension joint angle is 14.98%. The maximum error of measured and desired elbow flexion and extension joint angle is 14.56%. It is acceptable for rehabilitation training. Meanwhile, the movement of drinking water can be realized in accordance with the range of motion. It demonstrates that the proposed upper limb exoskeleton can also assist people with upper limb disorder to deal with activities of daily living. The feasibility of the proposed powered exoskeleton for upper limb rehabilitation training and function compensating based on a wheelchair is proved.