Interactive mechatronic system for improving upper limb rehabilitation

In this paper, a new master-slave system for upper limb rehabilitation is proposed. Haptic device (PHANTOM Omni) is used as master robot and the slave robot has been designed to be portable and wearable. It is potential to be used in passive and active training which are prevailing approaches in upper limb rehabilitation and it is also suitable in home-rehabilitation through therapist's teleoperation. We describe the mechanical design of elbow joint module, force model and some preliminary results of passive experiment.

The paper presents the kinematics of an exoskeleton-based robotic system for upper limb rehabilitation of post-stroke patients. The targeted arm areas are the elbow and the wrist, while the targeted motions are flexion/extension, pronation/supination and adduction/abduction. The paper presents the (direct and inverse) kinematic analysis of the proposed solution, the generated workspace of the robot and simulations for a proposed exercise for post-stroke upper limb rehabilitation.

With the development of virtual reality technology, its application in the field of medical rehabilitation has become more and more extensive, and it has gradually penetrated into the medical research on the rehabilitation of stroke patients. This paper proposes a set of evaluation criteria and rehabilitation data analysis methods for the interactive design of the upper limb rehabilitation virtual reality training system for stroke patients. Firstly, the characteristics and needs of upper limb rehabilitation of stroke patients are analyzed. Secondly, according to the characteristics of virtual reality technology, the evaluation criteria of interaction design are proposed. Finally, the method of training system rehabilitation data analysis is proposed based on hypothesis testing. The aim is to evaluate whether the interaction design of the virtual reality training system meets the rehabilitation needs of stroke patients, and to provide a useful reference for the analysis of the experimental results.

Following stroke, rehabilitation exercises paired with medical treatment are important for the recovery and maintenance of upper limb function. We developed a 2-DOF passive haptic system for upper limb rehabilitation and software focusing on reaching exercises, known as the coin collecting program, which provides training and evaluation of motor function, particularly dexterity. Past studies have revealed quantitative, reproducible evaluation indices using this software. In this paper, we compared case studies of post-stroke patients with results from healthy participants, and confirmed the reliability of the evaluation indices in grading or scoring motor skills.

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.

This paper presents the analysis and the implementation of a robotic rehabilitation device which couples a Brain-Computer Interface (BCI) and a robotic FES (Functional Electrical Stimulation)-exoskeleton system for upper limb rehabilitation of persons with neuromotor disabilities. This system combines two types of therapies used in neurorehabilitation: one using functional electrical stimulation and one using movements driven by an electrically powered exoskeleton. FES can coordinate the activation of certain muscle groups having as result movements which are similar to the normal ones. The main goal of this system is to allow people with disabilities to reach and maintain an optimal physical function. The FES-exoskeleton device is controlled by a BCI system. The control strategy of the entire system was implemented in MATLAB&Simulink environment. BCI technology involves monitoring the brain electrical activity and detecting special characteristics of EEG (electroencephalogram) using advanced signal processing algorithms. The BCI principle used for controlling the developed system is the Motor Imagery (MI). By imagining the left or right arm movement, the user can control the FES-exoskeleton system to perform forearm flexion and extension and hand opening.

This paper presents a new robotic system for upper limb rehabilitation. It is designed to assist the upper limb in therapies for both sitting and supine position, helping patients to carry out the required movements when they could not perform them. In the first part of the paper, the mechanical design and the development of the first prototype is exposed in detail. In the second part, new control strategy that modify the behavior of the rehabilitation robot according to different potential and force fields has been presented. Then, some experimental results of the performance of the implemented control with healthy subjects are reported.

The virtual rehabilitation system combining virtual rehabilitation environment and upper limb rehabilitation technology is interactive and interesting, which can improve the enthusiasm and initiative of patients for rehabilitation training, improve the efficiency of rehabilitation training and improve the effect of rehabilitation treatment. This paper firstly conducts in-depth research and analysis on the research progress of the upper limb rehabilitation robot system, and deeply studies the principle and rehabilitation principle of the stroke caused by hemiplegic dyskinesia, and summarizes the goals and methods of the upper limb rehabilitation system design. Secondly, the hand motion tracking is realized by Kinect’s bone tracking, and the optimal tracking distance is determined experimentally, which verifies the stability and robustness of the tracking. Static gesture recognition adopts two gesture recognition schemes based on Kinect depth image and color space model respectively. Finally, using the rehabilitation robot and Kinect sensor as the hardware platform, the virtual rehabilitation training system experimental platform is constructed, and the horizontal rehabilitation exercise and the three-dimensional space rehabilitation exercise are respectively studied experimentally, and the exercise data obtained by using healthy subjects as the experimental object is analyzed. Based on this, the validity and feasibility of the Kinect-based upper limb exercise rehabilitation interactive system were verified.

Cerebral apoplexy, commonly known as is one of the most common cerebrovascular diseases in the world. It is a kind of disease of brain nerve function defect which occurs frequently in the elderly. After the onset of stroke, most of the patients lose the function of limb movement, which leads to severe hemiplegia. In this paper, an upper and lower limb rehabilitation system based on brain computer interface technology is proposed. In the proposed rehabilitation system, BCI technique is used to translate the patients’ thoughts into rehabilitation training action, therefore it can transform the traditional passive training into active training, which is helpful to promote the functional recovery of the injured brain area and the reconstruction of the neural pathway in the body. The corresponding experiment results are positive and demonstrate the feasibility and effectiveness of the proposed rehabilitation system.

準3次元上肢リハビリ支援システム「PLEMO(プレモ)」およびそのソフトウェアの研究開発(「ハプティックインタラクション」特集)

Stroke can cause acute damage to blood vessels in the brain, which often lead to hemiplegia and imbalances in mobility. It is a challenge to develop a motor recovery training and evaluation method with less help of therapists. In this paper, a motor recovery training and evaluation method for upper limb rehabilitation robotic system is proposed. This system has two rehabilitation unit, one is active, the other is passive. The developed rehabilitation robotic system includes exoskeleton rehabilitation robot and PHANTOM1.5. The patients do rehabilitation training with the help of robot in the passive unit, and virtual reality game is designed in active unit. Patients with mild motor impairments observe the virtual reality game interface while manipulating the PHANTOM to do rehabilitation training. Three experiments are proposed in this paper. The fuzzy neural network, spring-damper model, and the method to evaluate the training trajectory are designed and validated. The sEMG signals and grip force during rehabilitation training are collected to set up a fuzzy neural network and achieve evaluation. The accuracy of the network is 0.96 which is calculated in validation set. In chapter IV, the rehabilitation evaluation method is compared with the state of art on rehabilitation evaluation method. The method proposed in this paper can reach high accuracy. It is easy to use and understand for patients even without the help of therapists. The problem of lacking therapists can be solved to some extent by the proposed upper limb rehabilitation system.

Networked multiagent systems consist of interacting agents that locally exchange information. Since these systems do not in general have a centralized architecture to monitor the activity of each agent, resilient distributed control system design for networked multiagent systems is essential in providing high system performance, reliability, and operation in the presence of system uncertainties. An important set of classes of such system uncertainties that can significantly deteriorate the achievable closed-loop system performance is sensor, actuator, and plant uncertainties, with sensor and actuator uncertainty possibly arising due to adversarial sensor and actuator compromise. This paper presents a novel distributed robust adaptive control architecture for leader-follower networked multiagent systems with undirected communication graph topologies to mitigate the effects of time-varying sensor and actuator attacks, and timeinvariant plant parameter uncertainty. The proposed adaptive control architecture guarantees uniform ultimate boundedness of the controlled closed-loop system. Finally, two numerical examples are provided to illustrate the efficacy of the proposed distributed robust adaptive control architecture.

A new low-cost system for rehabilitation of the impaired upper limb for stroke survivors is presented. A computer game was developed specifically for this purpose and the user's impaired upper extremity is tracked using a downward-pointed Kinect, an inexpensive motion capture system commercially available from Microsoft. A Kalman filter was implemented to reduce data jittering. Patients are required to move their impaired arm, sliding it on top of a transparent support, in order to play the game. The game is personalized to the patient through specific settings that adapt to the patient's range of motion and motor control at the start of the game as well as performance during the game. The final score is proportional to the arm's movement speed. A feasibility study was carried out with one stroke survivor. The game was played for ten days and usability surveys were answered before and after the study. The patient was engaged with the game, found it easy to understand and reported willingness to use it in the home environment and enjoyment of the use in the clinic.

Development of virtual reality proprioceptive rehabilitation system for stroke patients

In the direct torque control (DTC) of induction motor (IM) drive systems utilizing model predictive torque control (MPTC) in the inner loop and classical proportional integral (PI) control in the outer loop is prone to weaknesses, such as uncertainties, external disturbances, parameter variations, and nonlinear dynamics. A high-performance control system for speed and torque is required to guarantee a smooth and robust control architecture that can withstand such unpredictable effects. In this study, we propose an integral super twisting sliding mode control (ISTSMC) to address the shortcomings of the classical PI used in the outer loop of DTC drive systems. A sliding mode speed observer is also designed and utilized to overcome problems associated with mechanical sensors. The robustness of the proposed control strategy and fast dynamic speed response are compared favorably with a benchmark PI controller and conventional sliding mode control (SMC). The ability of the proposed system to regulate both speed and torque was evaluated through simulations, under various fault perturbations, parameter variations, and load disturbances, conducted in MATLAB/ Simulink. Various performance indices were used to demonstrate the superior performance of the proposed strategy compared to a benchmark PI system and conventional SMC-based MPTC approaches.