Rehabilitation robotics after stroke: a bibliometric literature review

Robotic exoskeleton-assisted walking rehabilitation for stroke patients: a bibliometric and visual analysis

Objective

This study aimed to conduct a bibliometric analysis of the literature on exoskeleton robot assisted walking rehabilitation for stroke patients in the Web of Science Core Collection over the past decade.

Method

Retrieved literature on exoskeleton robot assisted gait training for stroke hemiplegic patients from the Web of Science Core Collection from 1 January 2014 to 31 January 2024. The search method was topic search, and the types of documents were "article, meeting abstract, review article, early access." CiteSpace was used to analyze the search results from countries, institutions, keywords, cited references and cited authors.

Result

A total of 1,349 articles were retrieved, and 1,034 were ultimately included for visualization analysis. The annual publication volume showed an upward trend, with countries, institutions, and authors from Europe and America in a leading position. The core literature was also published by authors from European and American countries. The keywords were divided into 8 clusters: # 0 soft robotic exit, # 1 robot assisted gain training, # 2 multiple scales, # 3 magnetic rheological brake, # 4 test retest reliability, # 5 electromechanical assisted training, # 6 cerebra salary, and # 7 slow gain. The early research direction focused on the development of exoskeleton robots, verifying their reliability and feasibility. Later, the focus was on the combination of exoskeleton robot with machine learning and other technologies, rehabilitation costs, and patient quality of life.

Conclusion

This study provides a visual display of the research status, development trends, and research hotspots, which helps researchers in this field to grasp the research hotspots and choose future research directions.

Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation-A Systematic Review

This review aims to provide a systematic analysis of the literature focused on the use of intelligent control systems in robotics for physical rehabilitation, identifying trends in recent research and comparing the effectiveness of intelligence used in control, with the aim of determining important factors in robot-assisted rehabilitation and how intelligent controller design can improve them. Seven electronic research databases were searched for articles published in the years 2015 - 2022 with articles selected based on relevance to the subject area of intelligent control systems in rehabilitation robotics. It was found that the most common use of intelligent algorithms for control is improving traditional control strategies with optimization and learning techniques. Intelligent algorithms are also commonly used in sensor output mapping, model construction, and for various data learning purposes. Experimental results show that intelligent controllers consistently outperform non-intelligent controllers in terms of transparency, tracking accuracy, and adaptability. Active participation of the patients and lowered interaction forces are consistently mentioned as important factors in improving the rehabilitation outcome as well as the patient experience. However, there are limited examples of studies presenting experimental results with impaired participants suffering limited range of motion, so the effectiveness of therapy provided by these systems is often difficult to quantify. A lack of universal evaluation criteria also makes it difficult to compare control systems outside of articles which use their own comparison criteria.

Outlook on Industry-Academia-Government Collaborations Impacting Medical Device Innovation

The nature of collaborations between industry, academic, and government entities are discussed by the authors who together have significant experience in all three of these sectors. This article examines the intricacies and coordination needed between different stakeholder environments toward successful medical device innovation. The value of different types of collaboration models is illustrated through examples and the author's perspectives on current opportunities, challenges, and future outlook.

Design and analysis of a compatible exoskeleton rehabilitation robot system based on upper limb movement mechanism

Rehabilitation robots are used to promote structural and functional recovery of the nervous system with repetitive, task-oriented training and have been gradually applied to clinical rehabilitation training. This paper proposes an upper limb exoskeleton rehabilitation robot system that could realize shoulder-elbow-wrist joint rehabilitation training. Firstly, a motion equivalent model was established based on the upper limb movement mechanism, the robot mechanism configuration was designed, and the optimization algorithm and spatial mechanism theory were used to optimize and analyze the structural parameters and human-machine compatibility of the robot, which will guide the design of the robot's model. Then, the robot kinematics were solved, and its maximum motion range, dexterity distribution, and daily motion trajectory were simulated. Finally, a system prototype was built to test the maximum range of robot-assisted human upper limb training by laser tracker, while the pressure of human-machine interaction during training was captured and analyzed by flexible sensors. The results show that the proposed rehabilitation robot could nearly completely cover the range of motion of upper limb joints and meet the needs of trajectory training, and the linear velocity dexterity and angular velocity dexterity in the motion space are maximum 0.55 and 0.89, and the human-machine interaction pressures during the training process are all less than 10 kPa. Besides, this paper also conducted a system evaluation based on the fuzzy comprehensive evaluation model, and the evaluation result was 0.39, with an excellent evaluation grade, it indirectly indicates that the robot's overall performance was good.

Adaptive-Robust Controller for Smart Exoskeleton Robot

Rehabilitation robotics has seen growing popularity in recent years due to its immense potential for improving the lives of people with disabilities. However, the complex, uncertain dynamics of these systems present significant control challenges, requiring advanced techniques. This paper introduces a novel adaptive control framework integrating modified function approximation (MFAT) and double-integral non-singular terminal sliding mode control (DINTSMC). The goal is to achieve precise tracking performance, high robustness, a fast response, a finite convergence time, reduced chattering, and effective handling of unknown system dynamics. A key feature is the incorporation of a higher-order sliding mode observer, eliminating the need for velocity feedback. This provides a new solution for overcoming the inherent variations and uncertainties in robot manipulators, enabling improved accuracy within fixed convergence times. The efficacy of the proposed approach was validated through simulations and experiments on an exoskeleton robot. The results successfully demonstrated the controller's effectiveness. Stability analysis using Lyapunov theory proved the closed-loop system's uniform ultimate boundedness. This contribution is expected to enable enhanced control for rehabilitation robots and improved patient outcomes.

Comparison of theoretical and experimental models for knee flexion/extension using a cable-driven robot

To support and facilitate the rehabilitation of patients with physical limitations and aid the therapist, several robotic structures are being studied. Among the structures, the cable-driven robots stand out. The cable-driven robots are structures actuated by cables and have the advantages of being flexible and reconfigurable for each patient. The objective of this paper is to develop a theoretical model for knee flexion/extension force and moment using a cable-driven robot. The proposed model is necessary for elaborating a referential to which diagnosis can be made and the improvement of the patient evaluated. The presented theoretical model was validated through experiments with twelve sedentary and healthy volunteers. The first procedure tested ten subjects in three thigh angles for knee flexion motion; the second procedure tested two subjects in flexion and extension for the same thigh angle. The results show the validity of the model for 88.58% of the tests in an ANOVA analysis with a 99% confidence interval. The similarity of data for different gender, ages, and intrinsic factors was noted, implying that the model is representative and independent of the subject’s individuality. Differences between flexion and extension values were observed, which need to be studied in the future.

Planar Model for Vibration Analysis of Cable Rehabilitation Robots

Cable robots are widely used in the field of rehabilitation. These robots differ from other cable robots because the cables are rather short and are usually equipped with magnetic hooks to improve the ease of use. The vibrations of rehabilitation robots are dominated by the effects of the hooks and payloads, whereas the cables behave as massless springs. In this paper, a 2D model of the cables of a robot that simulates both longitudinal and transverse vibrations is developed and experimentally validated. Then the model is extended to simulate the vibrations of an actual 3D robot in the symmetry planes. Finally, the calculated modal properties (natural frequencies and modes of vibration) are compared with the typical spectrum of excitation due to the cable’s motion. Only the first transverse mode can be excited during the rehabilitation exercise.