Fractional Differential Equations

Background
Electromechanical and robot-assisted arm training devices are used in rehabilitation, and may help to improve arm function after stroke.

Objectives
To assess the effectiveness of electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength in people after stroke. We also assessed the acceptability and safety of the therapy.

Search methods
We searched the Cochrane Stroke Group's Trials Register (last searched February 2015), the Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library 2015, Issue 3), MEDLINE (1950 to March 2015), EMBASE (1980 to March 2015), CINAHL (1982 to March 2015), AMED (1985 to March 2015), SPORTDiscus (1949 to March 2015), PEDro (searched April 2015), Compendex (1972 to March 2015), and Inspec (1969 to March 2015). We also handsearched relevant conference proceedings, searched trials and research registers, checked reference lists, and contacted trialists, experts, and researchers in our field, as well as manufacturers of commercial devices.

Selection criteria
Randomised controlled trials comparing electromechanical and robot-assisted arm training for recovery of arm function with other rehabilitation or placebo interventions, or no treatment, for people after stroke.

Data collection and analysis
Two review authors independently selected trials for inclusion, assessed trial quality and risk of bias, and extracted data. We contacted trialists for additional information. We analysed the results as standardised mean differences (SMDs) for continuous variables and risk differences (RDs) for dichotomous variables.

Main results
We included 34 trials (involving 1160 participants) in this update of our review. Electromechanical and robot-assisted arm training improved activities of daily living scores (SMD 0.37, 95% confidence interval (CI) 0.11 to 0.64, P = 0.005, I² = 62%), arm function (SMD 0.35, 95% CI 0.18 to 0.51, P < 0.0001, I² = 36%), and arm muscle strength (SMD 0.36, 95% CI 0.01 to 0.70, P = 0.04, I² = 72%), but the quality of the evidence was low to very low. Electromechanical and robot-assisted arm training did not increase the risk of participant drop-out (RD 0.00, 95% CI -0.02 to 0.03, P = 0.84, I² = 0%) with moderate-quality evidence, and adverse events were rare.

Authors' conclusions
People who receive electromechanical and robot-assisted arm and hand training after stroke might improve their activities of daily living, arm and hand function, and arm and hand muscle strength. However, the results must be interpreted with caution because the quality of the evidence was low to very low, and there were variations between the trials in the intensity, duration, and amount of training; type of treatment; and participant characteristics.

/pdf/electromechanical-and-robot-assisted-arm-training-for-256itu8khs.pdf

Electromechanical and robot‐assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke

If a device can control human hands, the device can be useful for HCI and tangible application's output. To aid the controlling of finger movement, we present PossessedHand, a device with a forearm belt that can inform when and which fingers should be moved. PossessedHand controls the user's fingers by applying electrical stimulus to the muscles around the forearm. Each muscle is stimulated via 28 electrode pads. Muscles at different depths in the forearm can be selected for simulation by varying the stimulation level. PossessedHand can automatically calibrate the system for individuals. The automatic calibration system estimates relations between each electrode pad, stimulation level and muscle movement. Experiments show that PossessedHand can control the motion of 16 joints in the hand. Further, we also discuss an application based on this device to aid in playing a musical instrument.

PossessedHand: techniques for controlling human hands using electrical muscles stimuli

https://vbn.aau.dk/ws/files/337950041/robotics_09_00016.pdf

A review on design of upper limb exoskeletons

The recovery of hand function is one of the most challenging topics in stroke rehabilitation. Although the robot-assisted therapy has got some good results in the latest decades, the development of hand rehabilitation robotics is left behind. Existing reviews of hand rehabilitation robotics focus either on the mechanical design on designers’ view or on the training paradigms on the clinicians’ view, while these two parts are interconnected and both important for designers and clinicians. In this review, we explore the current literature surrounding hand rehabilitation robots, to help designers make better choices among varied components and thus promoting the application of hand rehabilitation robots. An overview of hand rehabilitation robotics is provided in this paper firstly, to give a general view of the relationship between subjects, rehabilitation theories, hand rehabilitation robots, and its evaluation. Secondly, the state of the art hand rehabilitation robotics is introduced in detail according to the classification of the hardware system and the training paradigm. As a result, the discussion gives available arguments behind the classification and comprehensive overview of hand rehabilitation robotics.

/pdf/hand-rehabilitation-robotics-on-poststroke-motor-recovery-4fbpq79z52.pdf

Hand Rehabilitation Robotics on Poststroke Motor Recovery

Motor imagery (MI) based brain-computer interfaces (BCI) extract commands in real-time and can be used to control a cursor, a robot or functional electrical stimulation (FES) devices. The control of FES devices is especially interesting for stroke rehabilitation, when a patient can use motor imagery to stimulate specific muscles in real-time. However, damage to motor areas resulting from stroke or other causes might impair control of a motor imagery BCI for rehabilitation. The current work presents a comparative evaluation of the MI-based BCI control accuracy between stroke patients and healthy subjects. Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10-24 sessions lasting about 1 h each with the recoveriX system. The participants' EEG data were classified while they imagined left or right hand movements, and real-time feedback was provided on a monitor. If the correct imagination was detected, the FES was also activated to move the left or right hand. The grand average mean accuracy was 87.4% for all patients and sessions. All patients were able to achieve at least one session with a maximum accuracy above 96%. Both the mean accuracy and the maximum accuracy were surprisingly high and above results seen with healthy controls in prior studies. Importantly, the study showed that stroke patients can control a MI BCI system with high accuracy relative to healthy persons. This may occur because these patients are highly motivated to participate in a study to improve their motor functions. Participants often reported early in the training of motor improvements and this caused additional motivation. However, it also reflects the efficacy of combining motor imagination, seeing continuous bar feedback, and real hand movement that also activates the tactile and proprioceptive systems. Results also suggested that motor function could improve even if classification accuracy did not, and suggest other new questions to explore in future work. Future studies will also be done with a first-person view 3D avatar to provide improved feedback and thereby increase each patients' sense of engagement.

/pdf/high-classification-accuracy-of-a-motor-imagery-based-brain-1hocspp7zz.pdf

High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training

In recent years, the assistive technologies and stroke rehabilitation methods have been empowered by the use of virtual reality environments and the facilities offered by brain computer interface systems and functional electrical stimulators. In this paper, a therapy system for stroke rehabilitation based on these revolutionary techniques is presented. Using a virtual reality Oculus Rift device, the proposed system ushers the patient in a virtual scenario where a virtual therapist coordinates the exercises aimed at restoring brain function. The electrical stimulator helps the patient to perform rehabilitation exercises and the brain computer interface system and an electrooculography device are used to determine if the exercises are executed properly. Laboratory tests on healthy people led to system validation from technical point of view. The clinical tests are in progress, but the preliminary results of the clinical tests have highlighted the good satisfaction degree of patients, the quick accommodation with the proposed therapy, and rapid progress for each user rehabilitation.

/pdf/bci-and-fes-based-therapy-for-stroke-rehabilitation-using-vr-zlol5heay7.pdf

BCI and FES Based Therapy for Stroke Rehabilitation Using VR Facilities

Brain-computer interface (BCI) systems have been used primarily to provide communication for persons with severe movement disabilities. This paper presents a new system that extends BCI technology to a new patient group: persons diagnosed with stroke. This system, called recoveriX, is designed to detect changes in motor imagery in real-time to help monitor compliance and provide closed-loop feedback during therapy. We describe recoveriX and present initial results from one patient.

recoveriX: A new BCI-based technology for persons with stroke

A new control strategy that supports the sitting down phase in FES-assisted standing in paraplegia is proposed. It is an adaptation of a well established closed-loop On/Off controller that uses trajectories within the state space of knee angle and knee angular velocity, but defines a zone between the On and Off sub-spaces for gradual change in the stimulation levels. This new controller has been experimentally tested on two paraplegic patients and it is concluded that compared to the conventional On/Off controller it better controls the lowering of the person toward the seat, by reducing knee-end velocity and handle reaction forces. It has a certain degree of interaction with the user's voluntary effort. Keeping the system simple in respect to the required number of sensors, only the knee angle measurements are required as feedback signal making the strategy simple and, thus, particularly useful for daily standing exercises in home environments.

A New Controller for FES-Assisted Sitting Down in Paraplegia

The life quality improvement issue is a problem of national and international interest. This acquires total different values when it is to refer to a series of disadvantaged categories, that is the persons with locomotor disabilities. It is an inevitable social responsibility to create equal opportunities for disabled people, to prevent any intentional or unintentional discrimination that they face and apply positive discrimination if necessary to improve their living standards and to let them have an equal share from social development as productive individuals of society. A disability is any kind of restriction or inability to accomplish a certain activity, which belongs to the interval considered as “normal” for a human being. Disabilities are characterized by deficiencies or excesses in the activity or behaviour that is normally expected from a person; they can be temporary or permanent, reversible or irreversible, progressive or regressive. The physical disabilities that require the use of wheelchairs imply the manufacturing of special clothing products that meet certain needs at a functional and/or therapeutic level. The study presents the main aspects involved in designing and developing textile products for this category of users. Therefore, the apparel that is specially designed for persons with disabilities has to: allow more freedom and independence when is being worn, correspond to the aesthetic point of view, offer psychological benefits and help to socially integrate the individual who wears it. In addition, clothes should not hinder the individual’s mobility and must allow the use of the chest and of the superior limbs to the maximum, they must be durable, have a low level of electrostatic charge, be absorbent and comfortable, present the so-called “rehabilitation functions”, and last but not least, be beneficial for the wearer both from a physical and psychological point of view .

/pdf/designing-functional-clothes-for-persons-with-locomotor-575mkle9v6.pdf

Marian Poboroniuc

Papers

High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training

BCI and FES Based Therapy for Stroke Rehabilitation Using VR Facilities

recoveriX: A new BCI-based technology for persons with stroke

A New Controller for FES-Assisted Sitting Down in Paraplegia

Designing Functional Clothes for Persons with Locomotor Disabilities