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.INDEX TERMS Human-exoskeleton coupling dynamics, parameter identification, rehabilitation training.
BACKGROUND: As an emerging exoskeleton robot technology, flexible lower limb exoskeleton (FLLE) integrates flexible drive and wearable mechanism, effectively solving many problems of traditional rigid lower limb exoskeleton (RLLE) such as higher quality, poorer compliance and relatively poor portability, and has become one of the important development directions in the field of active rehabilitation. OBJECTIVE: This review focused on the development and innovation process in the field of FLLE in the past decade. METHOD: Related literature published from 2010 to 2021 were searched in EI, IEEE Xplore, PubMed and Web of Science databases. Seventy target research articles were further screened and sorted through inclusion and exclusion criteria. RESULTS: FLLE is classified according to different driving modes, and the advantages and disadvantages of passive flexible lower limb exoskeletons and active flexible lower limb exoskeletons are comprehensively summarized. CONCLUSION: At present, FLLE’s research is mainly based on cable drive, bionic pneumatic muscles followed and matured, and new exoskeleton designs based on smart material innovations also trend to diversify. In the future, the development direction of FLLE will be lightweight and drive compliance, and the multi-mode sensory feedback control theory, motion intention recognition theory and human-machine interaction theory will be combined to reduce the metabolic energy consumption of walking.
BACKGROUND: Upper limb rehabilitation robots have become an important piece of equipment in stroke rehabilitation. Human-robot coupling (HRC) dynamics play a key role in the control of rehabilitation robots to improve human-robot interaction. OBJECTIVE: This study aims to study the methods of modeling and analysis of HRC dynamics to realize more accurate dynamic control of upper limb rehabilitation robots. METHODS: By the analysis of force interaction between the human arm and the upper limb rehabilitation robot, the HRC torque is achieved by summing up the robot torque and the human arm torque. The HRC torque and robot torque of a 2-DOF upper limb rehabilitation robot (FLEXO-Arm) are solved by Lagrangian equation and step-by-step dynamic parameters identification method. RESULTS: The root mean square (RMS) is used to evaluate the accuracy of the HRC torque and the robot torque calculated by the parameter identification, and the error of both is about 10%. Moreover, the HRC torque and the robot torque are compared with the actual torque measured by torque sensors. The error of the robot torque is more than twice the HRC. Therefore, the HRC torque is more accurate than the actual torque. CONCLUSIONS: The proposed HRC dynamics effectively achieves more accurate dynamic control of upper limb rehabilitation robots.
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