Limb Rehabilitation System Research Articles

Modified orthopedic traction system for cervical and lower limb rehabilitation.

Orthopedic tractions can be employed in the rehabilitation of patients suffering from problems affecting the spine, as well as the upper and lower extremities but the high costs of using tractions due to prolonged hospital stays is a major disadvantage particularly in low-income economies. The objective of this study was to design and develop a two-way adjustable traction system that accommodates both cervical and lower limb rehabilitation and improves limb and neck positioning. The production process involved the use of computer-aided design (CAD) as well as other manufacturing procedures like material selection, welding, and drilling. The system was tested for stability to be sure it does not fail under large loads. A functional and easy-to-install two-way orthopedic traction system for both hospital and home use was developed, installed and tested. The dimensions and adjustability would ensure that it can be used for children and adults. This study describes a device that can be used in hospitals. When used at homes, it can reduce the cost of medical bills, and provide patients with the emotional/psychological benefits of being cared for in a familiar environment.

Optimal Linear Quadratic Control for Knee-Ankle Orthosis System

The control technique for an exoskeleton system for lower limb rehabilitation is complicated, and numerous internal and external elements must be taken into account, in addition to the uncertainties in the system model. In this paper, through the analysis of the lower extremity exoskeleton is utilized to obtain the corresponding equation and its linearized form. The nonlinear differential equations have been linearized by using Jacobean’s method in order to facilitate the controller design. Considering the interior and external factors of the connecting rod, the uncertain elements are introduced and therefore the optimal control technique is applied to regulate the system. An optimal state feedback control strategy of Linear Quadratic Regulator (LQR), and LQR-Servo have been implemented in this work. Finally, the physical parameters of the Knee-Ankle Orthosis (KAO) exoskeleton are used, and the simulation results show the advantage and applicability of the proposed controller’s design of the Knee-Ankle orthosis system. Index Terms— Rehabilitation, Knee-Ankle orthosis (KAO), Optimal control, LQR-Servo controller.

Manipulability Optimization of a Rehabilitative Collaborative Robotic System

The use of collaborative robots (or cobots) in rehabilitation therapies is aimed at assisting and shortening the patient’s recovery after neurological injuries. Cobots are inherently safe when interacting with humans and can be programmed in different working modalities based on the patient’s needs and the level of the injury. This study presents a design optimization of a robotic system for upper limb rehabilitation based on the manipulability ellipsoid method. The human–robot system is modeled as a closed kinematic chain in which the human hand grasps a handle attached to the robot’s end effector. The manipulability ellipsoids are determined for both the human and the robotic arm and compared by calculating an index that quantifies the alignment of the principal axes. The optimal position of the robot base with respect to the patient is identified by a first global optimization and by a further local refinement, seeking the best alignment of the manipulability ellipsoids in a series of points uniformly distributed within the shared workspace.

Robotic control system for lower limb rehabilitation with force feedback

The paper proposes the structure of the robotic system (RS) control system with and without the patient’s force feedback, describes the conditions for using each of them. The method for improving data reliability on applied forces using strain gauge readings in the absence of patient activity was developed. The method of motivating a patient to perform “correct” movements thus “assisting” the mechanism, which consists in changing the speed of movement to create an impression that the mechanism moves due to patient’s efforts was developed. Mathematical RS model was constructed, graphs reflecting the accuracy of the specified trajectory were obtained.

Artificial Intelligence Limb Rehabilitation System on Account of Virtual Reality Technology on Long-Term Health Management of Stroke Patients in the Context of the Internet.

This study was aimed at discussing artificial intelligence (AI) limb rehabilitation system on account of virtual reality (VR) on long-term health management of stroke patients. In the study, AI limb rehabilitation system on account of VR technology was compared with traditional drug therapy, and effects of the two therapies on long-term health management of stroke patients were compared. Fifty patients with stroke in the hospital were randomly divided into experimental group and control group with 25 patients in each group. Patients in experimental group were treated with AI limb rehabilitation system on account of VR technology, and those in control group were treated with traditional drug therapy. To compare and judge the recovery of patients' physical ability, patients in the two groups were compared in physical movement ability and daily living activity ability after 10-week treatment. After 10-week treatment, Fugl-Meyer assessment-upper extremity (FMA-UE), Fugl-Meyer assessment-lower extremity (FMA-LE), the Hong Kong version of functional test for the hemiplegic upper extremity (FTHUE-HK), Barthel index (BI) daily living (ADL) activities, and Berg balance scale (BBS) in control group were lower than those in experimental group, but the score of MWS was higher than that of experimental group (P < 0.05). AI limb rehabilitation system on account of VR technology could effectively recover the daily health management of stroke patients, and its effect was significantly higher than that of traditional drug therapy. This method could be popularized in clinic.

Upper limb rehabilitation system based on virtual reality for breast cancer patients: Development and usability study.

BackgroundFunctional exercise is crucial for breast cancer patients after surgery, and the use of virtual reality technology to assist patients with postoperative upper limb functional rehabilitation has gradually attracted the attention of researchers. However, the usability of the developed rehabilitation system is still unknown to a large extent. The purpose of this study was to develop a virtual reality upper limb rehabilitation system for patients after breast cancer surgery and to explore its usability.MethodsWe built a multidisciplinary team based on virtual reality and human-computer interaction technology and designed and developed an upper limb function rehabilitation system for breast cancer patients after surgery. Breast cancer patients were recruited from a grade III-a general hospital in Changchun city for the experiment. We used the System Usability Scale to evaluate the system availability, the Presence Questionnaire scale to measure the immersive virtual reality scene, and the Simulator Sickness Questionnaire subjective measurement scale for simulator sickness symptoms.ResultsThis upper limb rehabilitation system hardware consisted of Head-mounted Display, a control handle and notebook computers. The software consisted of rehabilitation exercises and game modules. A total of 15 patients were tested on this system, all of whom were female. The mean age was 54.73±7.78 years, and no patients were excluded from the experiment because of adverse reactions such as dizziness and vomiting. The System Usability Scale score was 90.50±5.69, the Presence Questionnaire score was 113.40±9.58, the Simulator Sickness Questionnaire-nausea score was 0.93±1.16, the Simulator Sickness Questionnaire-oculomotor score was 0.80±1.27, the Simulator Sickness Questionnaire-disorientation score was 0.80±1.27, and the Simulator Sickness Questionnaire total score was 2.53±3.40.ConclusionsThis study fills in the blanks regarding the upper limb rehabilitation of breast cancer patients based on virtual reality technology system usability research. As the starting point of research in the future, we will improve the system’s function and design strictly randomized controlled trials, using larger samples in the promotion, to evaluate its application in breast cancer patients with upper limbs and other physiological functions and the feasibility and effects of rehabilitation.

Gaze-controlled Robot-assisted Painting in Virtual Reality for Upper-limb Rehabilitation.

Stroke is the leading cause of adult disability. Robot-assisted rehabilitation systems show great promise for motor recovery after a stroke. In this work, we present a gazecontrolled robotic system for upper limb rehabilitation. Subjects perform a painting task in virtual reality. We designed a novel and challenging painting task to encourage motivation and engagement, as these are critical factors in treatment efficacy. Because the robotic system can be programmed to provide varying amounts of assistance or resistance to the subject, it can be applied to a wide range of patients at different phases of recovery. We describe here the system configured in two modes: resistive control and hierarchical control. The former is designed for later stages of recovery, where the patient's impaired limb has recovered some function. It can be configured to provide varying degrees of resistance by adjusting the properties of an admittance controller. The latter targets patients in more acute phases, where the impaired limb is less responsive. It provides a combination of assistive and corrective control. We pilot tested our system on 10 able-bodied subjects. Our results show that the system can provide varying degrees of resistive control, and that the integration of high level control modulated by gaze can improve engagement. These results suggest that the system may provide a more engaging environment for a wide range of rehabilitative therapies than currently available.

Volitional EMG Controlled Wearable FES System for Lower Limb Rehabilitation.

Muscle rehabilitation by functional electrical stimulation (FES) is one of the effective treatments for the patients with neuromuscular diseases. The conventional FES applications, however, have limitations that utilize predetermined or repetitive stimulation patterns with the help of experts such as physical therapists. Therefore, we propose a wearable FES system in which the stimulus intensity is dynamically controlled by the motion intention of user in this paper. The proposed FES system utilizes electromyography (EMG) and inertial measurement unit (IMU) sensors for estimating the motion intention regardless of electrical stimulation, and is designed for the lower limb rehabilitation. The overall system configurations including hardware and software are presented in this paper, and the system performance was tested by lower limb exercises, e.g., squat, heel lift, and gait.

User-Centered Design and Evaluation of an Upper Limb Rehabilitation System with a Virtual Environment

Virtual environments (VEs) and haptic devices increase patients’ motivation. Furthermore, they observe their performance during rehabilitation. However, some of these technologies present disadvantages because they do not consider therapists’ needs and experience. This research presents the development and usability evaluation of an upper limb rehabilitation system based on a user-centered design approach for patients with moderate or mild stroke that can perform active rehabilitation. The system consists of a virtual environment with four virtual scenarios and a developed haptic device with vibrotactile feedback, and it can be visualized using a monitor or a Head-Mounted Display (HMD). Two evaluations were carried out; in the first one, five therapists evaluated the system’s usability using a monitor through the System Usability Scale, the user experience with the AttrakDiff questionnaire, and the functionality with customized items. As a result of these tests, improvements were made to the system. The second evaluation was carried out by ten volunteers who evaluated the usability, user experience, and performance with a monitor and HMD. A comparison of the therapist and volunteer scores has shown an increase in the usability evaluation (from 78 to &gt;85), the hedonic score rose from 0.6 to 2.23, the pragmatic qualities from 1.25 to 2.20, and the attractiveness from 1.3 to 2.95. Additionally, the haptic device and the VE showed no relevant difference between their performance when using a monitor or HMD. The results show that the proposed system has the characteristics to be a helpful tool for therapists and upper limb rehabilitation.

Clinical Effect of Virtual Reality Technology on Rehabilitation Training of Sports Injury.

In order to make most patients recover most of their limb functions after rehabilitation training, virtual reality technology is an emerging human-computer interaction technology, which uses the computer and the corresponding application software to build the virtual reality environment. Completing the training tasks in the virtual environment attracts the patients to conduct repeated training in the game and task-based training mode and gradually realizes the rehabilitation training goals. For the rehabilitation population with certain exercise ability, the kinematics of human upper limbs is mainly analyzed, and the virtual reality system based on HTC VIVE is developed. The feasibility and work efficiency of the upper limb rehabilitation training system were verified by experiments. Adult volunteers who are healthy and need rehabilitation training to participate in the experiment were recruited, and experimental data were recorded. The virtual reality upper limb rehabilitation system was a questionnaire. By extracting the motion data, the system application effect is analyzed and evaluated by the simulation diagram. Follow-up results of rehabilitation training showed that the average score of healthy subjects was more than 4 points and 3.8 points per question. Therefore, it is feasible to perform upper limb rehabilitation training using the HTC VIVE virtual reality rehabilitation system.

Design and Preliminary Evaluation of a Tongue-Operated Exoskeleton System for Upper Limb Rehabilitation.

Stroke is a devastating condition that may cause upper limb paralysis. Robotic rehabilitation with self-initiated and assisted movements is a promising technology that could help restore upper limb function. Previous studies have established that the tongue motion can be used to communicate human intent and control a rehabilitation robot/assistive device. The goal of this study was to evaluate a tongue-operated exoskeleton system (TDS-KA), which we have developed for upper limb rehabilitation. We adopted a tongue-operated assistive technology, called the tongue drive system (TDS), and interfaced it with the exoskeleton KINARM. We also developed arm reaching and tracking tasks, controlled by different tongue operation modes, for training and evaluation of arm motor function. Arm reaching and tracking tasks were tested in 10 healthy participants (seven males and three females, 23–60 years) and two female stroke survivors with upper extremity impairment (32 and 58 years). All healthy and two stroke participants successfully performed the tasks. One stroke subject demonstrated a clinically significant improvement in Fugl-Meyer upper extremity score after practicing the tasks in six 3-h sessions. We conclude that the TDS-KA system can accurately translate tongue commands to exoskeleton arm movements, quantify the function of the arm, and perform rehabilitation training.

Evaluating Power Rehabilitation Actions Using a Fuzzy Inference Method

The older population faces a high probability of experiencing age-related problems, such as osteoporosis, immobility, gait disturbances, stroke, Parkinson’s disease, and cognitive behavioral functional difficulties. Such problems negatively affect their lives. Thus, access to long-term care is a critical issue for older adults. In response to the aforementioned serious health issues, society must strive to provide a supportive and effective rehabilitation environment for older adults. This study designed an intelligent active and passive limb rehabilitation system to track and quantify the effectiveness of joint movements in patients automatically. The proposed method uses a camera and PoseNet to capture key feature information regarding human skeleton nodes and identify rehabilitation actions through joint movements. In addition, to solve the problem of joint occlusion during joint angle measurement, the designed system is equipped with a self-designed inertial measurement unit with GY-85 nine-axis sensors, which are mounted on the occluding part of the joints. A fuzzy inference system was developed to provide scores, suggestions, and encouragement for each rehabilitation session. This system also provides an interactive interface for users to monitor each rehabilitation session and examine whether rehabilitation is performed accurately.

Research on fNIRS Recognition Method of Upper Limb Movement Intention

This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement intention in the cerebral blood oxygen signal to the actual brain-computer interface system. Fifty subjects completed four upper limb movement paradigms: Lifting-up, putting down, pulling back, and pushing forward. Then, their near-infrared data and movement trigger signals were collected. In terms of the recognition algorithm for detecting the initial intention of upper limb movements, gradient boosting tree (GBDT) and random forest (RF) were selected for classification experiments. Finally, RF classifier with better comprehensive indicators was selected as the final classification algorithm. The best offline recognition rate was 94.4% (151/160). The ReliefF algorithm based on distance measurement and the genetic algorithm proposed in the genetic theory were used to select features. In terms of upper limb motion state recognition algorithms, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and linear discriminant analysis (LDA) were selected for experiments. Kappa coefficient was used as the classification index to evaluate the performance of the classifier. Finally, SVM classification got the best performance, and the four-class recognition accuracy rate was 84.4%. The results show that RF and SVM can achieve high recognition accuracy in motion intentions and the upper limb rehabilitation system designed in this paper has great application significance.

Wireless Motion Capture System for Upper Limb Rehabilitation

This work is devoted to the presentation of a Wireless Sensor System implementation for upper limb rehabilitation to function as a complementary system for a patient’s progress supervision during rehabilitation exercises. A cost effective motion capture sensor node composed by a 9 Degrees-of-Freedom (DoF) Inertial Measurement Unit (IMU) is mounted on the patient’s upper limb segments and sends wirelessly the corresponding measured signals to a base station. The sensor orientation and the upper limb individual segments movement in 3-Dimensional (3D) space are derived by processing the sensors’ raw data. For the latter purpose, a biomechanical model which resembles that of a kinematic model of a robotic arm based on the Denavit-Hartenberg (DH) configuration is used to approximate in real time the upper limb movements. The joint angles of the upper limb model are estimated from the extracted sensor node’s orientation angles. The experimental results of a human performing common rehabilitation exercises using the proposed motion capture sensor node are compared with the ones using an off-the-shelf sensor. This comparison results to very low error rates with the root mean square error (RMSE) being about 0.02 m.

Lower Limb Joint Nursing and Rehabilitation System Based on Intelligent Medical Treatment.

With the aggravation of the problem of aging population, all kinds of lower limb paralysis caused by various diseases occur frequently. People's demand for lower limb nursing and rehabilitation treatment is growing. In this paper, combined with intelligent medical technology and lower limb kinematics model, this paper proposes to build a lower limb joint nursing and rehabilitation system based on intelligent medical treatment. It is expected that, through the following limb joint rehabilitation robot as the main rehabilitation means, a smart nursing rehabilitation system which can quickly respond to users and realize remote rehabilitation nursing can be designed. First of all, it is clear that the main body of the lower limb joint rehabilitation system consists of the robot body and the state display system. Then, the sensor, amplifier, and data acquisition card are set in the data acquisition system, and the plantar balance force is detected using a FlexiForce film pressure sensor. The final control system mainly includes the main control module program and the lower limb action recognition program. The motor control software adopts PID regulation method, and the lower limb action recognition adopts SVM one-to-one classification method. After the construction of lower limb joint nursing and rehabilitation system, the accuracy rate of action recognition and classification was tested. In the third experiment, the accuracy of all the movements was 100%. Then, the joint displacement and angle changes of the experimenter assisted by the system were analyzed. The experimenter's knee joint and hip joint show a normal walking state, and the joint angle changes tend to be normal. Ten out of 55 rehabilitation system users were randomly selected for interview survey. The total scores of operation convenience, wearing comfort, intensity suitability, and movement science of the system were 90, 83, 84, and 91, respectively. This shows that the rehabilitation action designed by the system is scientific and easy to operate and can be put into use in rehabilitation training after improving the wearing comfort.

Upper Limb Rehabilitation System for Stroke Survivors Based on Multi-Modal Sensors and Machine Learning

Nowadays, rehabilitation training for stroke survivors is mainly completed under the guidance of the physician. There are various treatment ways, however, most of them are affected by various factors such as experience of physician and training intensity. The treatment effect cannot be fed back in time, and objective evaluation data is lacking. In addition, the treatment method is complicated, costly, and highly dependent on physicians. Moreover, stroke survivors' compliance is poor, which leads to various limitations. This paper combines the Internet-of-Things, machine learning, and intelligence system technologies to design a smartphone-based intelligence system to help stroke survivors to improve upper limb rehabilitation. With the built-in multi-modal sensors of the smart phone, training action data of users can be obtained, and then transfer to the server through the Internet. This research presents a DTW-KNN joint algorithm to recognize accuracy of rehabilitation actions and classify to multiple training completion levels. The experimental results show that the DTW-KNN algorithm can evaluate the rehabilitation actions, the accuracy rates of the classification in excellent, good, and normal are 85.7%, 66.7%, and 80% respectively. The intelligence system presented in this paper can help stroke survivors to proceed rehabilitation training independently and remotely, which reduces medical costs and psychological burden.

Dynamic balancing of RECOVER robotic system

This paper presents a novel robotic system for the lower limb rehabilitation of post-stroke survivors. The model presented in this paper aims to demonstrate an innovative solution that helps in the mobilization of the hip and knee flexion/extension and the ankle plantar flexion/extension and eversion/inversion motions. The paper focuses on achieving the dynamic balancing of the hip-knee module, which due to high accelerations peak values and large size is prone to a certain degree of instability, leading to shaking forces and unwanted vibration that might reduce the safety feelings of the patient. A dynamically balanced mechanism will reduce noise, wear and fatigue and allow higher payload capacity.

Robotic mirror therapy system for lower limb rehabilitation

PurposeThis paper aims to develop a robotic mirror therapy system for lower limb rehabilitation, which is applicable for different patients with individual movement disability levels.Design/methodology/approachThis paper puts forward a novel system that includes a four-degree-of-freedom sitting/lying lower limb rehabilitation robot and a wireless motion data acquisition system based on mirror therapy principle. The magnetorheological (MR) actuators are designed and manufactured, whose characteristics are detected theoretically and experimentally. The passive training control strategy is proposed, and the trajectory tracking experiments verify its feasibility. Also, the active training controller that is adapt to the human motor ability is designed and evaluated by the comparison experiments.FindingsThe MR actuators produce continuously variable and compliant torque for robotic joints by adjusting excitation current. The reference limb joint position data collected by the wireless motion data acquisition system can be used as the motion trajectory of the robot to drive the affected limb. The passive training strategy based on proportional-integral control proves to have great trajectory tracking performance through experiments. In the active training mode, by comparing the real-time parameters adjustment in two phases, it is certified that the proposed fuzzy-based regulated impedance controller can adjust assistance torque according to the motor ability of the affected limb.Originality/valueThe system developed in this paper fulfills the needs of robot-assisted mirror therapy for hemiplegic patients.

Usability of RePlay: An Automated Upper Limb Rehabilitation System for Brain Injury Tele-rehabilitation

To introduce RePlay, an automated upper limb rehabilitation system, and assess its usability in delivering at least 60 minutes of therapy to brain injury participants in the in-clinic and at-home environments.

A Novel sEMG Triggered FES-Hybrid Robotic Lower Limb Rehabilitation System for Stroke Patients

Stroke is a leading cause of acquired disability among adults. Current rehabilitation programs result in only partial recovery of motor ability for the patients, which has resulted in an ongoing search for methods to improve the rehabilitation approaches. Therefore, this study presents a novel method for early onset of active rehabilitation by combining an end effector robot with surface electromyography (sEMG) triggered functional electrical stimulation (FES) of rectus femoris and tibialis anterior muscles. This rehabilitation system was demonstrated in 10 able-bodied experimental participants. Defining a successful exercise repetition as a fully completed exercise, from start point to end point followed by a return to start point, when FES onset is triggered by the EMG threshold, the results showed that 97% of the exercise repetitions were successful for a leg press exercise and 100% for a dorsiflexion exercise. Furthermore, an FES stimulation current amplitude of 20–53 mA was required for the leg press exercise and 10–30 mA for the dorsiflexion exercise. The resulting generated force was in the range of 43.0-141.2 [N] for the leg press exercise and 5.4-17.6 [N] for dorsiflexion.