To examine the validity of 5 robot-based assessments of arm motor function poststroke.
An interactive real arm movements for application in rehabilitation is designed and
developed. The aim is to encourage hand paralysis patients performing their physical therapy by introducing games application in replacing conventional hand therapy module and methods. In this project, the accelerometer is used for tracking the orientation of the arm. As the arm moves, the values from x, y and z axis from the accelerometer changes and are being read by the Analog Inputs of the Arduino Board. After being read by the Analog Inputs of the Arduino Board, the 3D model moves as well. Solidworks software was used to modeled the hand in which the data is then transferred to Matlab/Simulink using SimMechanicalLink from Mathworks. Lastly, the sensor glove was programmed to work as a controller of games application in hand rehabilitation thus makes it an enjoyable therapy process. […]
To examine the validity of 5 robot-based assessments of arm motor function poststroke.
Outpatient clinical research center.
Volunteer sample of participants (N=40; age, >18y; 3–6mo poststroke) with arm motor deficits that had reached a stable plateau.
Clinical standards included the arm motor domain of the Fugl-Meyer Assessment (FMA) and 5 secondary motor outcomes: hand/wrist subsection of the arm motor domain of the FMA, Action Research Arm Test, Box and Block test (BBT), hand motor subscale of the Stroke Impact Scale Version 2.0, and Barthel Index. Robot-based assessments included wrist targeting, finger targeting, finger movement speed, reaction time, and a robotic version of the BBT. Anatomical measures included percent injury to the corticospinal tract (CST) and extent of injury of the hand region of the primary motor cortex obtained from magnetic resonance imaging.
Participants had moderate to severe impairment (arm motor domain of the FMA scores, 35.6±14.4; range, 13.5–60). Performance on the robot-based tests, including speed (r=.82; P<.0001), wrist targeting (r=.72; P<.0001), and finger targeting (r=.67; P<.0001), correlated significantly with the arm motor domain of the FMA scores. Wrist targeting (r=.57–.82) and finger targeting (r=.49–.68) correlated significantly with all 5 secondary motor outcomes and with percent CST injury. The robotic version of the BBT correlated significantly with the clinical BBT but was less prone to floor effects. Robot-based assessments were comparable to the arm motor domain of the FMA score in relation to percent CST injury and superior in relation to extent of injury to the hand region of the primary motor cortex.
The present findings support using a battery of robot-based methods for assessing the upper extremity motor function in participants with chronic stroke.
Impairment of the upper limbs is quite frequent after stroke, making rehabilitation an essential step towards clinical recovery and patient empowerment. This review aimed to synthetize existing evidence regarding interventions for upper limb function improvement after Stroke and to assess which would bring some benefit. The Cochrane Database of Systematic Reviews, the Database of Reviews of Effects and PROSPERO databases were searched until June 2013 and 40 reviews have been included, covering 503 studies, 18 078 participants and 18 interventions, as well asdifferent doses and settings of interventions. The main results were:
Currently available evidence is insufficient and of low quality, not supporting clear clinical decisions. High-quality studies are still needed.
The impact of transcranial direct current stimulation (tDCS) is controversial in the neurorehabilitation literature. It has been suggested that tDCS should be combined with other therapy to improve their efficacy.
To assess the effectiveness of a single session of upper limb robotic-assisted therapy (RAT) combined with real or sham-tDCS in chronic stroke patients. Twenty-one hemiparetic chronic stroke patients were included in a randomized, controlled, cross-over double-blind study.
Each patient underwent two sessions 7 days apart in a randomized order: (a) 20 min of real dual-tDCS associated with RAT (REAL+RAT) and (b) 20 min of sham dual-tDCS associated with RAT (SHAM+RAT). Patient dexterity (Box and Block and Purdue Pegboard tests) and upper limb kinematics were evaluated before and just after each intervention. The assistance provided by the robot during the intervention was also recorded. Gross manual dexterity (1.8±0.7 blocks, P=0.008) and straightness of movement (0.01±0.03, P<0.05) improved slightly after REAL+RAT compared with before the intervention. There was no improvement after SHAM+RAT. The post-hoc analyses did not indicate any difference between interventions: REAL+RAT and SHAM+RAT (P>0.05). The assistance provided by the robot was similar during both interventions (P>0.05).
The results showed a slight improvement in hand dexterity and arm movement after the REAL+RAT tDCS intervention. The observed effect after a single session was small and not clinically relevant. Repetitive sessions could increase the benefits of this combined approach.
OBJECTIVE: To assess the feasibility of conducting a randomised controlled trial of a home-based virtual reality system for rehabilitation of the arm following stroke.
DESIGN: Two group feasibility randomised controlled trial of intervention versus usual care.
SETTING: Patients’ homes.
PARTICIPANTS: Patients aged 18 or over, with residual arm dysfunction following stroke and no longer receiving any other intensive rehabilitation.
INTERVENTIONS: Eight weeks’ use of a low cost home-based virtual reality system employing infra-red capture to translate the position of the hand into game play or usual care.
MAIN MEASURES: The primary objective was to collect information on the feasibility of a trial, including recruitment, collection of outcome measures and staff support required. Patients were assessed at three time points using the Wolf Motor Function Test, Nine-Hole Peg Test, Motor Activity Log and Nottingham Extended Activities of Daily Living.
RESULTS: Over 15 months only 47 people were referred to the team. Twenty seven were randomised and 18 (67%) of those completed final outcome measures. Sample size calculation based on data from the Wolf Motor Function Test indicated a requirement for 38 per group. There was a significantly greater change from baseline in the intervention group on midpoint Wolf Grip strength and two subscales of the final Motor Activity Log. Training in the use of the equipment took a median of 230 minutes per patient.
CONCLUSIONS: To achieve the required sample size, a definitive home-based trial would require additional strategies to boost recruitment rates and adequate resources for patient support.
Due to an increased population of stroke patients and subsequent demand on health providers, there is an urgent need for effective stroke rehabilitation technology that can be used in patients’ own homes. Over recent years, systems employing functional electrical stimulation (FES) have shown the ability to provide effective therapy. However, there is currently no low-cost therapeutic system available which simultaneously supplies FES to muscles in the patient’s shoulder, arm and wrist to provide co-ordinated functional movement. This restricts the effectiveness of treatment, and hence the ability to support activities of daily living.
In this thesis a home-based low cost rehabilitation system is developed which substantially extends the current state of art in terms of sensing and control methodologies. In particular, it embeds novel non-contact sensing approaches; the first use of an electrode array within a closed-loop model based control scheme; an interactive task display system; and an integrated learning-based controller for multiple muscles within the upper-limb (UL), which supports co-ordinated tasks. The thesis then focuses on compacting the prototype by upgrading the depth sensor and using embedded systems to transfer it to the home
Currently available home-based systems employing FES for UL rehabilitation are first reviewed in terms of their underlying technology, operation, scope and clinical evidence. Motivated by this, a detailed examination of a prototype system is carried out that combines low cost non-contact sensors with closed-loop FES controllers. Then potential avenues to extend the technology are highlighted, with specific focus given to low-cost non-contact based sensors for the hand and wrist. Sensing approaches are then reviewed and evaluated in terms of their scope to support the intended system requirements. Electrode array hardware is developed in order to provide accurate movement capability. Biomechanical models of the combined stimulated arm and mechanical support are then formulated. Using these, model-based iterative learning control methodologies are then designed and implemented.
The system is evaluated with both unimpaired participants and stroke patients undergoing a course of treatment. Finally, a home-based prototype is developed which integrates and extends the aforementioned components. Results conrm the system’s scope to provide more effective stroke rehabilitation. Based on the achieved results, courses of future work necessary to continue this development are outlined.
Led by Professor Jane Burridge, the team will create a wireless sleeve, which will provide automatic, intelligent information about muscle movement and strength while patients practice every-day tasks at home.
The data will be available on a computer tablet to enable patients to review their progress as well as to allow therapists to tailor their rehabilitation programme.
The two-year project has been funded with a grant of just under £1 million from the National Institute for Health Research (NIHR) through its Invention for Innovation (i4i) programme and is a collaboration between the University of Southampton and Imperial College London, two medical technology consultancies; Maddison and Tactiq and NHS Trusts in Bristol and Portsmouth.
Jane Burridge, Professor of Restorative Neuroscience at Southampton, comments: “About 150,000 people in the UK have a stroke each year and, despite improvements in acute care that results in better survival rates, about 60 per cent of people with moderate to severe strokes fail to recover useful function of their arm and hand.
“Stroke rehabilitation is increasingly home-based, as patients are often discharged from hospital after only a few days. This policy encourages independence and avoids problems associated with prolonged hospital stays. However, some patients struggle to carry out the exercises and they may question whether what they are doing is correct. Similarly therapists don’t have objective measurements about their patients’ muscle activity or ability to move. Rehabilitation technologies like our sleeve will address problems faced by both patients and therapists.”
The wearable technology is the first to incorporate mechanomyography (MMG) microphone-like sensors that detect the vibration of a muscle when it contracts, and inertial measurement units (IMU), comprising tri-axial accelerometers, gyroscopes and magnetometers that detect movement. Data from the two types of sensors will be put together and then data that is not needed, for example outside noise, will then be removed from the muscle signal.
The feedback to patients will be presented on a user-friendly computer interface as an accurate representation of their movement, showing them how much they have improved.
The same sleeve and computer tablet technology, but using different software and user-interfaces, will provide therapists with information to help them diagnose specific movement problems, and inform their clinical decision-making, monitor progress and therefore increase efficiency and effectiveness of therapy.
Professor Burridge adds: “We hope that our sleeve will help stroke patients regain the use of their arm and hand, reduce time spent with therapists and allow them to have the recommended 45 minutes daily therapy more flexibly.. It will also be used to assess patients’ problems accurately as well as more cheaply and practically than using laboratory-based technologies.”
The team, which includes members who themselves have suffered strokes, are working with medical device consultancies, Maddison and Tactiq to develop wearable prototypes and graphical user interfaces which can then be trialled with patients from two NHS sites. They will test the user interfaces, wireless connectivity and examine how easy the sleeve is to wear. The potential cost savings to the NHS will also be examined.
Purpose: To investigate the effectiveness and feasibility of Kinect-based upper
extremity rehabilitation on functional performance in chronic stroke survivors.
Methods: This was a single cohort pre-post test study. Participants (N=10; mean age =
62.5 ± 9.06) engaged in Kinect-based training three times a week for four to five weeks
in a university laboratory. To simulate a clinic to home transfer condition,
individualized guidance was given to participants at the initial three sessions followed
by independent usage. Outcomes included Fugl-Meyer assessment of upper extremity,
Wolf Motor Function Test, Stroke Impact Scale, Confidence of Arm and Hand
Movement and Active Range of Motion. Participant experience was assessed using a
structured questionnaire and a semi-structured interview.
Results. Improvement was found in Fugl-Meyer assessment scores (p=0.001), Wolf
Motor Function Test, (p=0.008), Active Range of Motion (p<0.05) and Stroke Impact
Scale-Hand function (p=0.016). Clinically important differences were found in FuglMeyer
assessment scores (Δ= 5.70 ± 3.47) and Wolf Motor Function Test (Δ Time= –
4.45 ± 6.02; ∆ Functional Ability Scores= 0.29 ± 0.31). All participants could use the
system independently and recognized the importance of exercise individualization by
Conclusions. The Kinect-based UE rehabilitation provided clinically important
functional improvements to our study participants.
Stroke is the leading cause of long-term adult disability in the United States .
More than a half of survivors continue suffering from upper-limb hemiparesis poststroke with only 5% of people recovering their full arm function . The persistent
upper-limb dysfunction significantly impairs motor performance, and results in a
serious decline in functional ability as well as quality of life . Intensive and repeated
practice with the paretic arm appears necessary to enhance arm recovery and facilitate
neural reorganization [4-7]. Nevertheless, the healthcare system provides limited
amounts and duration of therapy, making it difficult for stroke survivors to achieve
maximal arm recovery before discharge from outpatient rehabilitation or home care
[8,9]. Therefore, identifying novel modalities that are accessible and affordable to the
general public while allowing continued practice of the arm is imperative for improving
long-term upper-limb outcomes after stroke.
One potential approach is the use of low-cost virtual reality (VR)-based systems,
for example, the Microsoft Kinect system. The Kinect is a vision-based motion
capturing system that can detect gesture and movements of the body through its RGA
camera and depth sensors. It allows users to interact with the VR-based system without
holding or wearing specialized equipment or markers for tracking. Users can play
games or practice exercises using natural movements while observing the performance of their virtual avatars shown in real-time on the computer screen. Through this interactive observation and feedback, stroke survivors can correct their movements towards more normal patterns. Furthermore, the Kinect is small and portable, thus enabling stroke survivors to practice exercises in a familiar and private environment. […]
Fifty percent of all stroke survivors remain with functional impairments of their upper limb. While there is a need to improve the effectiveness of rehabilitative training, so far no new training approach has proven to be clearly superior to conventional therapy. As training with rewarding feedback has been shown to improve motor learning in humans, it is hypothesized that rehabilitative arm training could be enhanced by rewarding feedback. In this paper, we propose a trial protocol investigating rewards in the form of performance feedback and monetary gains as ways to improve effectiveness of rehabilitative training.
This multicentric, assessor-blinded, randomized controlled trial uses the ArmeoSenso virtual reality rehabilitation system to train 74 first-ever stroke patients (< 100 days post stroke) to lift their impaired upper limb against gravity and to improve the workspace of the paretic arm. Three sensors are attached to forearm, upper arm, and trunk to track arm movements in three-dimensional space while controlling for trunk compensation. Whole-arm movements serve as input for a therapy game. The reward group (n = 37) will train with performance feedback and contingent monetary reward. The control group (n = 37) uses the same system but without monetary reward and with reduced performance feedback. Primary outcome is the change in the hand workspace in the transversal plane. Standard clinical assessments are used as secondary outcome measures.
This randomized controlled trial will be the first to directly evaluate the effect of rewarding feedback, including monetary rewards, on the recovery process of the upper limb following stroke. This could pave the way for novel types of interventions with significantly improved treatment benefits, e.g., for conditions that impair reward processing (stroke, Parkinson’s disease).
After stroke, 50% of survivors are left with impairments in arm function [1, 2], which is associated with reduced health-related quality of life . While there is evidence for a positive correlation between therapy dose and functional recovery [4, 5, 6], a higher therapy dose is challenging to implement, as it usually leads to an increase in costs commonly not covered by health insurances. However, when dose is matched, most randomized controlled trials introducing new types of rehabilitative interventions (e.g., robot-assisted therapy ) failed to show a superior effect compared to standard therapy. Thus, the need for improving therapy effectiveness remains. In search for elements of effective therapy, we hypothesize that performance feedback and monetary rewards can improve effectiveness.
It has been shown that reward enhances procedural  and motor-skill learning [9, 10] and has a positive effect on motor adaptation . Rewards mainly improve retention of motor skills and motor adaptations [9, 10, 11]. This effect was not explained by training duration (dose) as rewarded and non-rewarded groups underwent similar training schedules [8, 9, 10, 11]. In a functional magnetic resonance imaging (fMRI) study, Widmer et al. reported that adding monetary rewards after good performance leads to better consolidation and higher ventral striatum activation than knowledge of performance alone . The striatum is a key locus of reward processing , and its activity was shown to be increased by both intrinsic and extrinsic reward . Being a brain structure that receives substantial dopaminergic input from the midbrain, ventral striatal activity can be seen as a surrogate marker for dopaminergic activity in the substantia nigra/ventral tegmental area . In rodents, Hosp et al. found that dopaminergic projections from the midbrain also terminate directly in the primary motor cortex (M1) . Dopamine in M1 is necessary for long-term potentiation of certain cortico-cortical connections and successful motor-skill learning . As mechanisms of motor learning are also thought to play a role in motor recovery , rehabilitative interventions may benefit from neuroplasticity enhanced by reward.
Here, we describe a trial protocol to test the effect of enhanced feedback and reward on arm rehabilitation after stroke at matched training dose (time and intensity). We use the ArmeoSenso, a standardized virtual reality (VR)-based training system  that is delivered in two versions for two different study groups, one version with and one without reward and enhanced performance feedback. […]
Brain Computer Interfaces (BCI), is a modern technology which is currently revolutionizing the field of signal processing. BCI helped in the evolution of a new world where man and computer had never been so close. Advancements in cognitive neuro-sciences facilitated us with better brain imaging techniques and thus interfaces between machines and the human brain became a reality. Electroencephalography (EEG), which is the measurement and recording of electric signals using sensors arrayed across the scalp can be used for applications like prosthetic devices, applications in warfare, gaming, virtual reality and robotics upon signal conditioning and processing.
This paper is entirely based on Brain-Computer Interface with an objective of actuating a robotic arm with the help of device commands derived from EEG signals. This system unlike any other existing technology is purely non-invasive in nature, cost effective and is one of its kinds that can serve various requirements such as prosthesis. This paper suggests a low cost system implementation that can even serve as a reliable substitute for the existing technologies of prosthesis like BIONICS. […]