A neural tracking and motor control approach to improve rehabilitation of upper limb movements

Convolutional neural network in upper limb functional motion analysis after stroke

In this work, implementation of Convolutional Neural Network (CNN) for the purpose of analysis of functional upper limb movement pattern was applied. The main aim of the study was to compare motion of selected activities of daily living of participants after stroke with the healthy ones (in similar age). The optical, marker-based motion capture system was applied for the purpose of data acquisition. There were some attempts made in order to find the existing differences in the motion pattern of the upper limb. For this purpose, the motion features of dominant and non-dominant upper limb of healthy participants were compared with motion features of paresis and non-paresis upper limbs of participants after stroke. On the basis of the newly collected data set, a new CNN application was presented to the classification of motion data in two different class label configurations. Analyzing individual segments of the upper body, it turned out that the arm was the most sensitive segment for capturing changes in the trajectory of the lifting movements of objects.

Mobile Game Induces Active Engagement on Neuromuscular Electrical Stimulation Training in Patients with Stroke

This study aimed to investigate the effectiveness of the mobile game-based neuromuscular electrical stimulation (MG-NMES) with assessing usability issues, such as attention and curiosity, and intrinsically interesting issues, which is necessary for successful poststroke rehabilitation. With the conventional NMES (C-NMES) system, the subjects underwent active repetitive cyclic NMES training. For assessment of usability issues, 20 hemiplegic stroke subjects were randomly divided into two groups. The subjects in the MG-NMES group (n = 9) and C-NMES group (n = 11) underwent 20 minutes of training each day for 5 days. We assessed the subjects' attention, curiosity, and intrinsically interesting issues; and using questionnaires they answered questions regarding their expectations of the training outcome after each training session. We found that the subjects in the MG-NMES group maintained their attention and interest for the 5 days, and their curiosity and expectation of a positive training outcome gradually increased as the training proceeded. In contrast, the C-NMES group reported no change in their attention or curiosity, but it was lower than the subjects in the MG-NMES group. In addition, their interest gradually decreased, which may have reduced their expectations of a positive outcome as the sessions progressed. There were no side effects during the training sessions in either group. The MG-NMES training paradigm developed is a new, readily available, and highly motivating MG-NMES training system. Based on the usability test, the reported advantages of the system were improved attention and flow experience during NMES training.

Robotics in Lower-Limb Rehabilitation after Stroke

With the increase in the elderly, stroke has become a common disease, often leading to motor dysfunction and even permanent disability. Lower-limb rehabilitation robots can help patients to carry out reasonable and effective training to improve the motor function of paralyzed extremity. In this paper, the developments of lower-limb rehabilitation robots in the past decades are reviewed. Specifically, we provide a classification, a comparison, and a design overview of the driving modes, training paradigm, and control strategy of the lower-limb rehabilitation robots in the reviewed literature. A brief review on the gait detection technology of lower-limb rehabilitation robots is also presented. Finally, we discuss the future directions of the lower-limb rehabilitation robots.

Robot-assisted post-stroke motion rehabilitation in upper extremities: a survey

Recent neurological research indicates that the impaired motor skills of post-stroke patients can be enhanced and possibly restored through task-oriented repetitive training. This is due to neuroplasticity – the ability of the brain to change through adulthood. Various rehabilitation processes have been developed to take advantage of neuroplasticity to retrain neural pathways and restore or improve motor skills lost as a result of stroke or spinal cord injuries (SCI). Research in this area over the last few decades has resulted in a better understanding of the dynamics of rehabilitation in post-stroke patients and development of auxiliary devices and tools to induce repeated targeted body movements. With the growing number of stroke rehabilitation therapies, the application of robotics within the rehabilitation process has received much attention. As such, numerous mechanical and robot-assisted upper limb and hand function training devices have been proposed. A systematic review of robotic-assisted upper extremity (UE) motion rehabilitation therapies was carried out in this study. The strengths and limitations of each method and its effectiveness in arm and hand function recovery were evaluated. The study provides a comparative analysis of the latest developments and trends in this field, and assists in identifying research gaps and potential future work.

Inertial Sensing Based Assessment Methods to Quantify the Effectiveness of Post-Stroke Rehabilitation

In clinical settings, traditional stroke rehabilitation evaluation methods are subjectively scored by occupational therapists, and the assessment results vary individually. To address this issue, this study aims to develop a stroke rehabilitation assessment system by using inertial measurement units. The inertial signals from the upper extremities were acquired, from which three quantitative indicators were extracted to reflect rehabilitation performance during stroke patients’ movement examination, i.e., shoulder flexion. Both healthy adults and stroke patients were recruited to correlate the proposed quantitative evaluation indices and traditional rehab assessment scales. Especially, as a unique feature of the study the weight for each of three evaluation indicators was estimated by the least squares method. The quantitative results demonstrate the proposed method accurately reflects patients’ recovery from pre-rehabilitation, and confirm the feasibility of applying inertial signals to evaluate rehab performance through feature extraction. The implemented assessment scheme appears to have the potential to overcome some shortcomings of traditional assessment methods and indicates rehab performance correctly.

Gait disorder rehabilitation using vision and non-vision based sensors: a systematic review

Even though the amount of rehabilitation guidelines has never been greater, uncertainty continues to arise regarding the efficiency and effectiveness of the rehabilitation of gait disorders. This question has been hindered by the lack of information on accurate measurements of gait disorders. Thus, this article reviews the rehabilitation systems for gait disorder using vision and non-vision sensor technologies, as well as the combination of these. All papers published in the English language between 1990 and June, 2012 that had the phrases "gait disorder", "rehabilitation", "vision sensor", or "non vision sensor" in the title, abstract, or keywords were identified from the SpringerLink, ELSEVIER, PubMed, and IEEE databases. Some synonyms of these phrases and the logical words "and", "or", and "not" were also used in the article searching procedure. Out of the 91 published articles found, this review identified 84 articles that described the rehabilitation of gait disorders using different types of sensor technologies. This literature set presented strong evidence for the development of rehabilitation systems using a markerless vision-based sensor technology. We therefore believe that the information contained in this review paper will assist the progress of the development of rehabilitation systems for human gait disorders.

Pattern recognition based forearm motion classification for patients with chronic hemiparesis

To make full use of electromyography (EMG) that contains rich information of muscular activities in active rehabilitation for motor hemiparetic patients, a couple of recent studies have explored the feasibility of applying pattern recognition technique to the classification of multiple motion classes for stroke survivors and reported some promising results. However, it still remains unclear if kinematics signals could also bring good motion classification performance, particularly for patients after traumatic brain damage. In this study, the kinematics signals was used for motion classification analysis in three stroke survivors and two patients after traumatic brain injury, and compared with EMG. The results showed that an average classification error of 7.9 ± 6.8% for the affected arm over all subjects could be achieved with a linear classifier when they performed multiple fine movements, 7.9% lower than that when using EMG. With either kind of signals, the motor control ability of the affected arm degraded significantly in comparison to the intact side. The preliminary results suggested the great promise of kinematics information as well as the biological signals in detecting user's conscious effort for robot-aided active rehabilitation.

Role of Robotics in Neurorehabilitation

Over the past decade, rehabilitation hospitals have begun to incorporate robotics technologies into the daily treatment schedule of many patients. These interventions hold greater promise than simply replicating traditional therapy, because they allow therapists an unprecedented ability to specify and monitor movement features such as speed, direction, amplitude, and joint coordination patterns and to introduce controlled perturbations into therapy. We argue that to fully realize the potential of robotic devices in neurorehabilitation, it is necessary to better understand the specific aspects of movement that should be facilitated in rehabilitation. In this article, we first discuss neurorecovery in the context of motor control and learning principles that can provide guidelines to rehabilitation professionals for enhancing recovery of motor function. We then discuss how robotic devices can be used to support such activities.

Biologically inspired modelling for the control of upper limb movements: from concept studies to future applications

Modelling is continuously being deployed to gain knowledge on the mechanisms of motor control. Computational models, simulating the behaviour of complex systems, have often been used in combination with soft computing strategies, thus shifting the rationale of modelling from the description of a behaviour to the understanding of the mechanisms behind it. In this context, computational models are preferred to deterministic schemes because they deal better with complex systems. The literature offers some striking examples of biologically inspired modelling, which perform better than traditional approaches when dealing with both learning and adaptivity mechanisms. Can these theoretical studies be transferred into an application framework? That is, can biologically inspired models be used to implement rehabilitative devices? Some evidences, even if preliminary, are presented here, and support an affirmative answer to the previous question, thus opening new perspectives.