Stroke is a leading cause of serious long-term disability. World Health Organization (WHO) published that the second leading of death is stroke accident and every year, 15 million people worldwide suffer from stroke attack, two-thirds of them have a permanent disability. Muscle impairment can be treated by intensive movements involving repetitive task, task-oriented and task-variegated. Conventional stroke rehabilitation is expensive, less engaging and at the same time need more time for the rehabilitation process and need more energy and time for the therapist to guide the stroke-survivor. Modern stroke rehabilitation is more promising and more effective with modern rehabilitation aids allowing the rehabilitation process to be faster, however, this therapist method can be obtained in the big cities. To cover the lack of rehabilitation process in this research will develop and improve post-stroke rehabilitation using games. This research using electromyography (EMG) device to analyze the muscle contraction during the rehabilitation process and using Kinect XBOX to record trajectory hands movements. Five games from movements sequence have designed and will be examined in this research. This games obtained two results, the first is the EMG signal and the second is trajectory data. EMG signal can recognize muscle contractions during playing game and the trajectory data can save the pattern of movements and showed the pattern to the monitor. EMG signal processing using time or frequency feature extractions is a good idea to obtain more information from muscle contractions, also velocity, similarities and error movements can be obtained by study the possible approaches.
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via Arm Games for Virtual Reality Based Post-stroke Rehabilitation | SpringerLink
Previous studies on robotic rehabilitation have shown that subjects’ active participation and effort involved in rehabilitation training can promote the performance of therapies. In order to improve the voluntary effort of participants during the rehabilitation training, assist-as-needed (AAN) control strategies regulating the robotic assistance according to subjects’ performance and conditions have been developed. Unfortunately, the heterogeneity of patients’ motor function capability in task space is not taken into account during the implementation of these controllers. In this paper, a new scheme called greedy AAN (GAAN) controller is designed for the upper limb rehabilitation training of neurologically impaired subjects. The proposed GAAN control paradigm includes a baseline controller and a Gaussian RBF network that is utilized to model the functional capability of subjects and to provide corresponding a task challenge for them. In order to avoid subjects’ slacking and encourage their active engagement, the weight vectors of RBF networks evaluating subjects’ impairment level are updated based on a greedy strategy that makes the networks progressively learn the maximum forces over time provided by subjects. Simultaneously, a challenge level modification algorithm is employed to adjust the task challenge according to the task performance of subjects. Experiments on 12 subjects with neurological impairment are conducted to validate the performance and feasibility of the GAAN controller. The results show that the proposed GAAN controller has significant potential to promote the subjects’ voluntary engagement during training exercises.
via A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation – IEEE Journals & Magazine
Previously, we reported a novel bilateral upper-limb rehabilitation system, an adaptive admittance controller and a related bilateral recovery strategy. In this study, we want to get a stronger evidence to verify the robustness of the proposed system, controller and recovery strategy as well as to further investigate the possibility of bilateral trainings for clinical applications. To this end, ten healthy subjects took part in a 60-minute experiment. Trajectories of robots and interaction force were recorded under the proposed bilateral recovery strategy which contained four exercise modes. For mode-l and mode-2, results showed that the trajectories of master and slave robots can catch the reference trajectory very well, and be changed with active interaction force applied by participants. For mode-3 and mode-4, participants finished tasks very well by drawing the ‘square-shaped’ trajectories through their own force. In conclusion, the experimental results were good enough to provide a strong and positive evidence for the proposed system and controller. Moreover, according to the feedbacks from participants, the bilateral recovery strategy can be treated as a new and interesting training as compared to the traditional unilateral training, and could be tested in clinical applications further.
Compared to the traditional manual therapy, the robot involved therapy can alleviate labor-intensive aspects of conventional rehabilitation trainings, and provide precise passive/active repetitive trainings in a sufficiently long timeframe , . In terms of upper-limb rehabilitation trainings, some robotic systems have been developed for bilateral exercises, and figured out a problem that performing most activities of daily living tasks with one-hand is awkward, difficult and time-consuming .
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via A Bilateral Training System for Upper-limb Rehabilitation: A Follow-up Study – IEEE Conference Publication
The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human-robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia.
via Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton – IEEE Journals & Magazine
21-23 Oct. 2015
In the last two decades, robot-aided rehabilitation has become widespread, particularly for upper limb movement rehabilitation. In this Doctoral Consortium I present a system for physical and cognitive rehabilitation that uses a combination of Serious Games to allow the monitoring and progress tracking of a person during physical therapy. The system records physical and cognitive states through the interaction with the advance robotic arm in order to assess the users hand-eye coordination, response interaction, working memory and concentration rates.
Source: IEEE Xplore Abstract (Abstract) – Robot-Assisted Rehabilitation for Smart Assessment and Training
In this paper we propose a full upper limb exoskeleton for motor rehabilitation of reaching, grasping and releasing in post-stroke patients. The presented system takes into account the hand pre-shaping for object affordability and it is driven by patient’s intentional control through a self-paced asynchronous Motor Imagery based Brain Computer Interface (MI-BCI). The developed antropomorphic eight DoFs exoskeleton (two DoFs for the hand, two for the wrist and four for the arm) allows full support of the manipulation activity at the level of single upper limb joint. In this study, we show the feasibility of the proposed system through experimental rehabilitation sessions conducted with three chronic post-stroke patients. Results show the potential of the proposed system for being introduced in a rehabilitation protocol.
Source: IEEE Xplore Abstract – A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI
Robot-assisted therapy has become an important technology used to restore and reinforce the motor functions of the patients with neuromuscular disorders.
In this paper, we proposed an upper-limb exoskeleton intended to assist the rehabilitation training of shoulder, elbow and wrist. The proposed therapeutic exoskeleton has an anthropomorphic structure able to match the upper-limb anatomy and enable natural human-robot interaction.
A modified sliding mode control (SMC) strategy consisting of a proportional-integral-derivative (PID) sliding surface and a fuzzy hitting control law is developed to guarantee robust tracking performance and reduce the chattering effect. The Lyapunov theorem is utilized to demonstrate the system stability. In order to evaluate the effectiveness of proposed algorithm, several trajectory tracking experiments were conducted based on a real-time control system.
Experimental results are presented to prove that, when compared to the conventional PID controller, the fuzzy SMC strategy can effectively reduce the tracking errors and achieve favorable control performance.
via IEEE Xplore Abstract (Abstract) – Fuzzy sliding mode control of an upper limb exoskeleton for robot-assisted rehabilitation