Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

Overview of the role of robots in upper limb disabilities rehabilitation: a scoping review

BACKGROUND

Neuromotor rehabilitation and improvement of upper limb functions are necessary to improve the life quality of patients who have experienced injuries or have pathological outcomes. Modern approaches, such as robotic-assisted rehabilitation can help to improve rehabilitation processes and thus improve upper limb functions. Therefore, the aim of this study was to investigate the role of robots in upper limb disability improvement and rehabilitation.

METHODS

This scoping review was conducted by search in PubMed, Web of Science, Scopus, and IEEE (January 2012- February 2022). Articles related to upper limb rehabilitation robots were selected. The methodological quality of all the included studies will be appraised using the Mixed Methods Appraisal Tool (MMAT). We used an 18-field data extraction form to extract data from articles and extracted the information such as study year, country, type of study, purpose, illness or accident leading to disability, level of disability, assistive technologies, number of participants in the study, sex, age, rehabilitated part of the upper limb using a robot, duration and frequency of treatment, methods of performing rehabilitation exercises, type of evaluation, number of participants in the evaluation process, duration of intervention, study outcomes, and study conclusions. The selection of articles and data extraction was made by three authors based on inclusion and exclusion criteria. Disagreements were resolved through consultation with the fifth author. Inclusion criteria were articles involving upper limb rehabilitation robots, articles about upper limb disability caused by any illness or injury, and articles published in English. Also, articles involving other than upper limb rehabilitation robots, robots related to rehabilitation of diseases other than upper limb, systematic reviews, reviews, and meta-analyses, books, book chapters, letters to the editor, and conference papers were also excluded. Descriptive statistics methods (frequency and percentage) were used to analyses the data.

RESULTS

We finally included 55 relevant articles. Most of the studies were done in Italy (33.82%). Most robots were used to rehabilitate stroke patients (80%). About 60.52% of the studies used games and virtual reality rehabilitate the upper limb disabilities using robots. Among the 14 types of applied evaluation methods, "evaluation and measurement of upper limb function and dexterity" was the most applied evaluation method. "Improvement in musculoskeletal functions", "no adverse effect on patients", and "Safe and reliable treatment" were the most cited outcomes, respectively.

CONCLUSIONS

Our findings show that robots can improve musculoskeletal functions (musculoskeletal strength, sensation, perception, vibration, muscle coordination, less spasticity, flexibility, and range of motion) and empower people by providing a variety of rehabilitation capabilities.

Robustness and Tracking Performance Evaluation of PID Motion Control of 7 DoF Anthropomorphic Exoskeleton Robot Assisted Upper Limb Rehabilitation

Upper limb dysfunctions (ULD) are common following a stroke. Annually, more than 15 million people suffer a stroke worldwide. We have developed a 7 degrees of freedom (DoF) exoskeleton robot named the smart robotic exoskeleton (SREx) to provide upper limb rehabilitation therapy. The robot is designed for adults and has an extended range of motion compared to our previously designed ETS-MARSE robot. While providing rehabilitation therapy, the exoskeleton robot is always subject to random disturbance. Moreover, these types of robots manage various patients and different degrees of impairment, which are quite impossible to model and incorporate into the robot dynamics. We hypothesize that a model-independent controller, such as a PID controller, is most suitable for maneuvering a therapeutic exoskeleton robot to provide rehabilitation therapy. This research implemented a model-free proportional–integral–derivative (PID) controller to maneuver a complex 7 DoF anthropomorphic exoskeleton robot (i.e., SREx) to provide a wide variety of upper limb exercises to the different subjects. The robustness and trajectory tracking performance of the PID controller was evaluated with experiments. The results show that a PID controller can effectively control a highly nonlinear and complex exoskeleton-type robot.

CardioVR-ReTone—Robotic Exoskeleton for Upper Limb Rehabilitation following Open Heart Surgery: Design, Modelling, and Control

Following cardiac surgery, patients experience difficulties with the rehabilitation process, often finding it difficult, and therefore lack the motivation for rehabilitation activities. As the number of people aged 65 and over will rise by 207 percent globally by 2050, the need for cardiac rehabilitation will significantly increase, as this is the main population to experience heart problems. To address this challenge, this paper proposes a new robotic exoskeleton concept with 12 DoFs (6 DoFs on each arm), with a symmetrical structure for the upper limbs, to be used in the early rehabilitation of cardiac patients after open-heart surgery. The electromechanical design (geometric, kinematic, and dynamic model), the control architecture, and the VR-based operating module of the robotic exoskeleton are presented. To solve the problem of the high degree of complexity regarding the CardioVR-ReTone kinematic and dynamic model, the iterative algorithm, kinetic energy, and generalized forces were used. The results serve as a complete model of the exoskeleton, from a kinematic and dynamic point of view as well as to the selection of the electric motors, control system, and VR motivation model. The validation of the concept was achieved by evaluating the exoskeleton structure from an ergonomic point of view, emphasizing the movements that will be part of the cardiac rehabilitation.

Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees

In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (RMSE), and correlation coefficient (γ)) were calculated to verify the feasibility of the prediction method.

Current Trends and Challenges in Pediatric Access to Sensorless and Sensor-Based Upper Limb Exoskeletons

Sensorless and sensor-based upper limb exoskeletons that enhance or support daily motor function are limited for children. This review presents the different needs in pediatrics and the latest trends when developing an upper limb exoskeleton and discusses future prospects to improve accessibility. First, the principal diagnoses in pediatrics and their respective challenge are presented. A total of 14 upper limb exoskeletons aimed for pediatric use were identified in the literature. The exoskeletons were then classified as sensorless or sensor-based, and categorized with respect to the application domain, the motorization solution, the targeted population(s), and the supported movement(s). The relative absence of upper limb exoskeleton in pediatrics is mainly due to the additional complexity required in order to adapt to children's growth and answer their specific needs and usage. This review highlights that research should focus on sensor-based exoskeletons, which would benefit the majority of children by allowing easier adjustment to the children's needs. Sensor-based exoskeletons are often the best solution for children to improve their participation in activities of daily living and limit cognitive, social, and motor impairments during their development.

Shoulder Kinematics Assessment towards Exoskeleton Development

Neuromuscular and sensorimotor degeneration caused by stroke or any other disease significantly reduce the physical, cognitive, and social well-being across the life span. Mostly, therapeutic interventions are employed in order to restore the lost degrees-of-freedom (DOF) caused by such impairments and automating these therapeutic tasks through exoskeletons/robots is becoming a common practice. However, aligning these robotic devices with the complex anatomical and geometrical motions of the joints is very challenging. At the same time, a good alignment is required in order to establish a better synergy of human-exoskeleton system for an effective intervention procedure. In this paper, a case study of an exoskeleton and shoulder joint alignment were studied through different size and orientation impairment models through motion capture data and musculoskeletal modeling in OpenSim. A preliminary result indicates that shoulder elevation is very sensitive to misalignment and varies with shoulder joint axes orientation; this is partly due to drastic displacement of the upper arm axes with respect to the shoulder joint origin during elevation. Additional study and analysis is required to learn any possible restraint on shoulder elevation that could potentially help in the exoskeleton development.

Upper Limb Bionic Orthoses: General Overview and Forecasting Changes

Using robotics in modern medicine is slowly becoming a common practice. However, there are still important life science fields which are currently devoid of such advanced technology. A noteworthy example of a life sciences field which would benefit from process automation and advanced robotic technology is rehabilitation of the upper limb with the use of an orthosis. Here, we present the state-of-the-art and prospects for development of mechanical design, actuator technology, control systems, sensor systems, and machine learning methods in rehabilitation engineering. Moreover, current technical solutions, as well as forecasts on improvement, for exoskeletons are presented and reviewed. The overview presented might be the cornerstone for future research on advanced rehabilitation engineering technology, such as an upper limb bionic orthosis.

Assistive Grasping Based on Laser-point Detection with Application to Wheelchair-mounted Robotic Arms

As the aging of the population becomes more severe, wheelchair-mounted robotic arms (WMRAs) are gaining an increased amount of attention. Laser pointer interactions are an attractive method enabling humans to unambiguously point out objects and pick them up. In addition, they bring about a greater sense of participation in the interaction process as an intuitive interaction mode. However, the issue of human–robot interactions remains to be properly tackled, and traditional laser point interactions still suffer from poor real-time performance and low accuracy amid dynamic backgrounds. In this study, combined with an advanced laser point detection method and an improved pose estimation algorithm, a laser pointer is used to facilitate the interactions between humans and a WMRA in an indoor environment. Assistive grasping using a laser selection consists of two key steps. In the first step, the images captured using an RGB-D camera are pre-processed, and then fed to a convolutional neural network (CNN) to determine the 2D coordinates of the laser point and objects within the image. Meanwhile, the centroid coordinates of the selected object are also obtained using the depth information. In this way, the object to be picked up and its location are determined. The experimental results show that the laser point can be detected with almost 100% accuracy in a complex environment. In the second step, a compound pose-estimation algorithm aiming at a sparse use of multi-view templates is applied, which consists of both coarse- and precise-matching of the target to the template objects, greatly improving the grasping performance. The proposed algorithms were implemented on a Kinova Jaco robotic arm, and the experimental results demonstrate their effectiveness. Compared with commonly accepted methods, the time consumption of the pose generation can be reduced from 5.36 to 4.43 s, and synchronously, the pose estimation error is significantly improved from 21.31% to 3.91%.