Posts Tagged proprioception

[Abstract] Use of mobile applications in hand therapy



Mobile devices can be incorporated into therapy as an engaging alternative to traditional therapy options. The use of mobile devices and smartphone applications can enhance the quality of care provided by health care professionals.


To find mobile apps that can be incorporated into hand therapy practice.


Hand therapy evaluation, interventions, proprioception, laterality, and home exercise program applications can be incorporated into practice. Patient education can also be provided via the use of mobile applications.


Smartphone applications can be a valuable intervention and impact performance in individuals with impaired hand function. Smartphone applications offer a client-centered, and potentially motivating, activity option that can be utilized to aid the hand therapist.

via Use of mobile applications in hand therapy – ScienceDirect

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[ARTICLE] Gamification in a Physical Rehabilitation Setting: Developing a Proprioceptive Training Exercise for a Wrist Robot – Full Text

Proprioception or body awareness is an essential sense that aids in the neural control of movement. Proprioceptive impairments are commonly found in people with neurological conditions such as stroke and Parkinson’s disease. Such impairments are known to impact the patient’s quality of life. Robot-aided proprioceptive training has been proposed and tested to improve sensorimotor performance. However, such robot-aided exercises are implemented similar to many physical rehabilitation exercises, requiring task-specific and repetitive movements from patients. Monotonous nature of such repetitive exercises can result in reduced patient motivation, thereby, impacting treatment adherence and therapy gains. Gamification of exercises can make physical rehabilitation more engaging and rewarding. In this work, we discuss our ongoing efforts to develop a game that can accompany a robot-aided wrist proprioceptive training exercise.


Figure 1: Left. WristBot being used by a participant. Right. Screenshot of the virtual environment showing an avatar controlled by user’s wrist movements.


Proprioception, the sense of body awareness, is essential for normal motor function. Proprioceptive deficits are common in neurological conditions [Coupar et al. 2012; Konczak et al. 2009]. Such deficits cause a decline in precision of goal-directed movements, and altered postural and spinal reflexes resulting in balance and gait problems [Rothwell et al. 1982]. Proprioceptive training is an intervention aiming to improve proprioceptive function [Aman et al. 2015]. Previous work has established the efficacy of a robot-aided proprioceptive training using WristBot [Elangovan et al. 201720182019]. The WristBot (Figure 1. Left) is a three degrees-of-freedom (3-DoF) exoskeleton robot that allows full range of motion (ROM), delivers precise haptic, position, and velocity stimuli at the wrist, and accurately encodes wrist position across time. Additional details about the WristBot can found in [Cappello et al. 2015].

Nevertheless, while the WristBot has demonstrated its efficacy, it shares a limitation that is often encountered in rehabilitation settings. In a clinical setting, patients are often required to perform task-specific and repetitive movements [Kwakkel et al. 1999]. Initial patient enthusiasm to complete such activities rapidly declines as a result of the monotonous nature of movements. Patient engagement can be improved by complementing therapy with a virtual environment (VE). Prior research has shown that users have favored exercises complemented with a VE rather than conventional approaches [Hoffman et al. 2014]. Thus, our project objective is to turn these tedious movements into an interactive VE experience.


Gamification process accounted for two key considerations: (1) the game should foster patient motivation and attention (2) and be clinically meaningful. To address these objectives, we reviewed the literature on game development [Bond 2014; Fullerton 2018] and identified four essential components: (1) Variability, (2) Feedback, (3) Rewards, and (4) a Compelling Purpose. The user will be gradually exposed to increasing levels of difficulty, which will likely reduce user frustrations. The user will receive meaningful feedback on concurrent metrics (e.g., Optimal ROM), as well as on previous treatment sessions. During game progress, the user will be alerted about deviations from the target movement requirements. Achievement badges will be rewarded to the user upon reaching therapy milestones, such as target ROM. Lastly, to encourage game completion, we establish an interesting backstory and a meaningful character arc for our virtual avatars. The developed game will be adaptable based on the user’s current clinical status, thus, making the game clinically meaningful. The clinician will have the ability to prescribe exercises based on user needs such as 1 DoF vs 3 DoF movements, continuous vs discrete movements, and strength training vs mobility training. WristBot will provide supportive forces aiding the user to achieve therapy milestones.

Gamified exercise is being developed using the Unity Game Engine, Python and libraries which interface with the WristBot. The game closely resembles an endless runner type game (Figure 1. Right) and utilizes the WrsitBot’s 3-DoF functionality to interact with the VE. Wrist flexion, extension, and abduction can be used to traverse their environment. The remaining 3 movements will allow interactions with their VE in unique ways, such as opening/closing doors, crouching, and pulling levers. In the VE, coins are strategically placed to maximize and improve the use of available ROM. Upon contact with either a wall or obstacle, visual feedback will be provided in the form of avatar damage and coin deduction. Consequently, users achieve improved mobility.

In Python, the connection between Unity and the WristBot library is managed through the use of a local WebSocket, a protocol for two-way communication over a single Transmission Control Protocol (TCP) connection [Fette and Melnikov 2011]. Through the WebSocket, reciprocal data are transferred between the WristBot and Unity. For example, wrist kinematic data will be streamed to the game while game progress is being relayed to the WristBot library. Game progress data will be utilized to compute and deliver haptic feedback to the user. Haptic feedback provided in the form of haptic assistance will aid users to improve their available ROM, while haptic resistance will improve muscle strength within the desired ROM. The clinical motive of the game is to transition the user from use of haptic assistance to resistance during game play. WristBot will adapt haptic feedback based on time spent and progress achieved in game play.


Usability testing will be conducted to ensure proper game usage by the clinical population and healthcare professionals. Specifically, the usability testing will evaluate areas such as 1) ease of game play, 2) game efficiency, and 3) user engagement. We will test the assumptions in each of these areas are accurately depicted in game development and met during game play. For example, we expect online visual feedback of deviations from target to help user focus on achieving the movement requirements. The users will be asked to verify the benefits of visual feedback in modifying their movements. Similarly, other assumptions such as performance badges and coins as rewards, and increase in difficulty levels will be evaluated. A common pitfall of usability studies involving physical rehabilitation setting is not recruiting from the representative population, most notably elderly population [Laver et al. 2017] as age has been shown to interfere with interactions in VE [Meldrum et al. 2012]. Therefore, to ensure our game is intuitive, we will recruit representative users from our patient populations.


This project was supported by National Science Foundation Partnerships For Innovation Technology Translation Award to Jürgen Konczak (1919036). Christopher Curry was supported by National Research Trainee-Understanding the Brain: Graduate Training Program in Sensory Science: Optimizing the Information Available for Mind and Brain (1734815).


  • Joshua E Aman, Naveen Elangovan, I Yeh, Jürgen Konczak, et al. 2015. The effectiveness of proprioceptive training for improving motor function: a systematic review. Frontiers in human neuroscience 8 (2015), 1075. 
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  • Jeremy Gibson Bond. 2014. Introduction to Game Design, Prototyping, and Development: From Concept to Playable Game with Unity and C. Addison-Wesley Professional. 
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  • Leonardo Cappello, Naveen Elangovan, Sara Contu, Sanaz Khosravani, Jürgen Konczak, and Lorenzo Masia. 2015. Robot-aided assessment of wrist proprioception. Frontiers in human neuroscience 9 (2015), 198. 
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  • Fiona Coupar, Alex Pollock, Phil Rowe, Christopher Weir, and Peter Langhorne. 2012. Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clinical rehabilitation 26, 4 (2012), 291–313. 
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  • Naveen Elangovan, Leonardo Cappello, Lorenzo Masia, Joshua Aman, and Jürgen Konczak. 2017. A robot-aided visuo-motor training that improves proprioception and spatial accuracy of untrained movement. Scientific reports 7, 1 (2017), 17054. 
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  • Naveen Elangovan, Paul Tuite, and Jürgen Konczak. 2018. Somatosensory training improves proprioception and untrained motor function in Parkinson’s disease. Frontiers in neurology 9(2018), 1053. 
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  • Naveen Elangovan, I-Ling Yeh, Jessica Holst-Wolf, and Jürgen Konczak. 2019. A robot-assisted sensorimotor training program can improve proprioception and motor function in stroke survivors. In 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, 660–664. 
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  • Jürgen Konczak, Daniel M Corcos, Fay Horak, Howard Poizner, Mark Shapiro, Paul Tuite, Jens Volkmann, and Matthias Maschke. 2009. Proprioception and motor control in Parkinson’s disease. Journal of motor behavior 41, 6 (2009), 543–552. 
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  • Gert Kwakkel, Boudewijn J Kollen, and Robert C Wagenaar. 1999. Therapy impact on functional recovery in stroke rehabilitation: a critical review of the literature. Physiotherapy 85, 7 (1999), 377–391. 
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  • Dara Meldrum, Aine Glennon, Susan Herdman, Deirdre Murray, and Rory McConn-Walsh. 2012. Virtual reality rehabilitation of balance: assessment of the usability of the Nintendo Wii® Fit Plus. Disability and rehabilitation: assistive technology 7, 3 (2012), 205–210. 
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[WEB SITE] Neurorehabilitation: Fighting strokes with robotics

Having a stroke can be a scary experience, but the long road to recovery might be getting shorter, thanks to research out of ECU.

Imagine suddenly losing control of a limb or your ability to communicate.

And while this happens, excruciating pain spreads across your head.

This was Joanna’s experience when she had a  at the age of 44.

“I was sick three days up to having my stroke,” Joanna explains. “I had vomiting, headaches and was not making much sense when talking.”

“Three days later, I was sitting down and then it felt like my head was being squeezed between two vices. Excruciating pain.”

Risk factor

In Australia, strokes affect around 55,000 people a year and are the third most common cause of death and a leading cause of disability.

There’s a range of factors that increase the risk of strokes, including diet, exercise and .

But one of the most telling  is, simply, age.

From the age of 45, the risk of a stroke in men is one in four, and for women, it’s one in five.

Fortunately, our knowledge of strokes and how to combat them has improved a lot in the past few decades.

A big part of the solution is getting help quickly, according to Edith Cowan University (ECU) Professor Dylan Edwards.

“If it’s the blockage of a blood vessel, it can be treated very well by anti-coagulant therapy that will break up the blood clot and restore the blood flow to the brain,” Dylan says.

“Typically, you notice somebody is having a stroke by them having issues with their speech or they have a weakness or funny sensation in one side of their body.”

But surviving a stroke is only part of the journey, and with 65% of stroke survivors suffering from some form of disability, restoring motor skills is a critical part of rehabilitation.

Road to recovery

Recovery from stroke can be a long and frustrating road for even the smallest paralysation.

For stroke survivor Joanna, the frustration she felt not being able to move normally made the recovery process even more challenging.

“The emotional side of having the stroke has affected me more than anything else,” Joanna says.

“You slowly get used to the fact that you can’t move your left side, and you know that you’ll get therapy. But when I had people come visit, when they left, I was in tears [out of frustration].”

Joanna eventually started to get some feeling back in her left side, just to her thumb at first.

“It was still a shock that I had lost all of that, so just a little bit of movement was enough to keep me going and stay motivated.”

Fighting back with technology

At ECU’s Lab for NeuroRehabilitation and Robotics, Dylan and his team have been researching how to help people recover their motor control after a brain or spinal cord injury.

Part of their research focuses on understanding the recovery of stroke survivors, using a robotic sensory platform called the Kinarm Exoskeleton Lab.

“The Kinarm looks like a fancy piece of gym equipment,” Dylan explains. “You sit inside the device and position your arms on top of movable handles, and you’re wheeled into this virtual reality environment.”

For the user in the chair, it feels like you’re playing a series of games, moving the chair’s arms to get a response on the screen—such as bouncing balls off paddles.

But the real work is happening behind the scenes.

“All of this information is acquired by these high-powered computers and analysed for how the person is performing,” Dylan says. “This [helps] identify the precise proprioceptive issue with an individual stroke survivor so we can prescribe therapy more effectively.”

In simplest terms, the Kinarm helps identify issues where the user is telling their arm to move but the resulting movement is not what they were trying to do.

This could be an arm not extending the full distance or slower reaction times.

With strokes usually affecting one side of the body more than the other, the unaffected side can provide a good baseline for what their normal reactions should be.

But what if both sides of the body have been affected? The Kinarm can pick up on that too, detecting deficits in what would be considered the unaffected side and showing this in the test results.

R&R—Robotics and Recovery

For Joanna, using the Kinarm has been a challenging experience, even three years after her stroke.

“It actually made you concentrate more in the game to hit the balls coming down,” she explains.

“I think that made you use the brain to try and keep up with your eye, which it didn’t, but I gave it my best shot. I also noticed my peripheral vision has gone.”

“It highlighted for me the improvements I have got since my stroke, which is nice for me three years on to see how it was then to what I could actually achieve on the Kinarm now.”

The data collected helps doctors prescribe the most beneficial treatment for their patients, based on the results of the tests.

Whether it’s heading towards recovering the function in a limb or something as simple as the mobility of a single joint, Dylan believes even small changes are worth pursuing.

“Some degree of independence—even though it might be apparent to an onlooker or a carer—can be very meaningful for a patient.”

“Small changes that we have made in the past through prescribing therapies effectively are things like being able to stabilise yourself on the train and send a text message.”

Recovering movement and lives

While full recovery from a stroke is not guaranteed, any improvement to quality of life can mean everything for survivors. Restoring simple movements can help patients build up their self-confidence to return to their everyday lives.

“Often stroke patients are in the older age bracket, and many of them are working,” Dylan says. “It’s very depressing to be disengaged from a functional work life, and going back to work might just be having the confidence of turning over a page of paper at your desk.”

As we learn more about how the body and brain recover after these , there’s hope we can find ways to better support those who have experienced extensive motor damage.

While there’s medication and training regimes to follow, at its core, it comes down to the drive to actively engage in recovering.

And even if it’s just through small victories, a spark from ECU’s Lab for NeuroRehabilitation and Robotics could help light the fire of determination in .

Explore further

Regulating blood supply to limbs improves stroke recovery


via Neurorehabilitation: Fighting strokes with robotics

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[ARTICLE] Robot-Assisted Stair Climbing Training on Postural Control and Sensory Integration Processes in Chronic Post-stroke Patients: A Randomized Controlled Clinical Trial – Full Text

Background: Postural control disturbances are one of the important causes of disability in stroke patients affecting balance and mobility. The impairment of sensory input integration from visual, somatosensory and vestibular systems contributes to postural control disorders in post-stroke patients. Robot-assisted gait training may be considered a valuable tool in improving gait and postural control abnormalities.

Objective: The primary aim of the study was to compare the effects of robot-assisted stair climbing training against sensory integration balance training on static and dynamic balance in chronic stroke patients. The secondary aims were to compare the training effects on sensory integration processes and mobility.

Methods: This single-blind, randomized, controlled trial involved 32 chronic stroke outpatients with postural instability. The experimental group (EG, n = 16) received robot-assisted stair climbing training. The control group (n = 16) received sensory integration balance training. Training protocols lasted for 5 weeks (50 min/session, two sessions/week). Before, after, and at 1-month follow-up, a blinded rater evaluated patients using a comprehensive test battery. Primary outcome: Berg Balance Scale (BBS). Secondary outcomes:10-meter walking test, 6-min walking test, Dynamic gait index (DGI), stair climbing test (SCT) up and down, the Time Up and Go, and length of sway and sway area of the Center of Pressure (CoP) assessed using the stabilometric assessment.

Results: There was a non-significant main effect of group on primary and secondary outcomes. A significant Time × Group interaction was measured on 6-min walking test (p = 0.013) and on posturographic outcomes (p = 0.005). Post hoc within-group analysis showed only in the EG a significant reduction of sway area and the CoP length on compliant surface in the eyes-closed and dome conditions.

Conclusion: Postural control disorders in patients with chronic stroke may be ameliorated by robot-assisted stair climbing training and sensory integration balance training. The robot-assisted stair climbing training contributed to improving sensorimotor integration processes on compliant surfaces. Clinical trial registration (NCT03566901).


Postural control disturbances are one of the leading causes of disability in stroke patients, leading to problems with transferring, maintaining body position, mobility, and walking (Bruni et al., 2018). Therefore, the recovery of postural control is one of the main goals of post-stroke patients. Various and mixed components (i.e., weakness, joint limitation, alteration of tone, loss of movement coordination and sensory organization components) can affect postural control. Indeed, the challenge is to determine the relative weight placed on each of these factors and their interaction to plan specific rehabilitation programs (Bonan et al., 2004).

The two functional goals of postural control are postural orientation and equilibrium. The former involves the active alignment of the trunk and head to gravity, the base of support, visual surround and an internal reference. The latter involves the coordination of movement strategies to stabilize the center of body mass during self-initiated and externally triggered stability perturbations. Postural control during static and dynamic conditions requires a complex interaction between musculoskeletal and neural systems (Horak, 2006). Musculoskeletal components include biomechanical constraints such as the joint range of motion, muscle properties and limits of stability (Horak, 2006). Neural components include sensory and perceptual processes, motor processes involved in organizing muscles into neuromuscular synergies, and higher-level processes essential to plan and execute actions requiring postural control (Shumway-Cook and Woollacott, 2012). A disorder in any of these systems may affect postural control during static (in quite stance) and dynamic (gait) tasks and increase the risk of falling (Horak, 2006).

Literature emphasized the role of impairments of sensory input integration from visual, somatosensory and vestibular systems in leading to postural control disorders in post-stroke patients (Bonan et al., 2004Smania et al., 2008). Healthy persons rely on somatosensory (70%), vision (10%) and vestibular (20%) information when standing on a firm base of support in a well-lit environment (Peterka, 2002). Conversely, in quite stance on an unstable surface, they increase sensory weighting to vestibular and vision information as they decrease their dependence on surface somatosensory inputs for postural orientation (Peterka, 2002). Bonan et al. (2004) investigate whether post-stroke postural control disturbances may be caused by the inability to select the pertinent somatosensory, vestibular or visual information. Forty patients with hemiplegia after a single hemisphere chronic stroke (at least 12 months) performed computerized dynamic posturography to assess the patient’s ability to use sensory inputs separately and to suppress inaccurate inputs in case of sensory conflict. Six sensory conditions were assessed by an equilibrium score, as a measure of body stability. Results show that patients with hemiplegia seem to rely mostly on visual input. In conditions of altered somatosensory information, with visual deprivation or visuo-vestibular conflict, the patient’s performance was significantly lower than healthy subjects. The mechanism of this excessive visual reliance remains unclear. However, higher-level inability to select the appropriate sensory input rather than to elementary sensory impairment has been advocated as a potential mechanism of action (Bonan et al., 2004).

Sensory strategies and sensory reweighting processes are essential to generate effective movement strategies (ankle, hip, and stepping strategies) which can be resolved through feed-back or feed-forward postural adjustments. The cerebral cortex shapes these postural responses both directly via corticospinal loops and indirectly via the brainstem centers (Jacobs and Horak, 2007). Moreover, the cerebellar- and basal ganglia-cortical loop is responsible for adapting postural responses according to prior experience and for optimizing postural responses, respectively (Jacobs and Horak, 2007).

Rehabilitation is the cornerstone in the management of postural control disorders in post-stroke patients (Pollock et al., 2014). To date, no one physical rehabilitation approach can be considered more effective than any other approach (Pollock et al., 2014). Specific treatments should be chosen according to the individual requirements and the evidence available for that specific treatment. Moreover, it appears to be most beneficial a mixture of different treatment for an individual patient (Pollock et al., 2014). Considering that, rehabilitation involving repetitive, high intensity, task-specific exercises is the pathway for restoring motor function after stroke (Mehrholz et al., 2013Lo et al., 2017) robotic assistive devices for gait training have been progressively being used in neurorehabilitation to Sung et al. (2017). In the current literature, three primary evidence have been reported.

Firstly, a recent literature review highlights that robot-assisted gait training is advantageous as add-on therapy in stroke rehabilitation, as it adds special therapeutic effects that could not be afforded by conventional therapy alone (Morone et al., 2017Sung et al., 2017). Specifically, robot-assisted gait training was beneficial for improving motor recovery, gait function, and postural control in post-stroke patients (Morone et al., 2017Sung et al., 2017). Stroke patients who received physiotherapy treatment in combination with robotic devices were more likely to reach better outcomes compared to patients who received conventional training alone (Bruni et al., 2018).

Second, the systematic review by Swinnen et al. (2014) supported the use of robot-assisted gait therapy to improve postural control in subacute and chronic stroke patients. A wide variability among studies was reported about the robotic-device system and the therapy doses (3–5 times per week, 3–10 weeks, 12–25 sessions). However, significant improvements (Cohen’s d = 0.01 to 3.01) in postural control scores measured with the Berg Balance Scale (BBS), the Tinetti test, postural sway tests, and the Timed Up and Go (TUG) test were found after robot-assisted gait training. Interestingly, in five studies an end-effector device (gait trainer) was used (Peurala et al., 2005Tong et al., 2006Dias et al., 2007Ng et al., 2008Conesa et al., 2012). In two study, the exoskeleton was used (Hidler et al., 2009Westlake and Patten, 2009). In one study, a single joint wearable knee orthosis was used (Wong et al., 2012). Because the limited number of studies available and methodological differences among them, more specific randomized controlled trial in specific populations are necessary to draw stronger conclusions (Swinnen et al., 2014).

Finally, technological and scientific development has led to the implementation of robotic devices specifically designed to overcome the motor limitation in different tasks. With this perspective, the robot-assisted end-effector-based stair climbing (RASC) is a promising approach to facilitate task-specific activity and cardiovascular stress (Hesse et al., 20102012Tomelleri et al., 2011Stoller et al., 20142016Mazzoleni et al., 2017).

To date, no studies have been performed on the effects of RASC training in improving postural control and sensory integration processes in chronic post-stroke patients.

The primary aim of the study was to compare the effects of robot-assisted stair climbing training against sensory integration balance training on static and dynamic balance in chronic stroke patients. The secondary aims were to compare the training effects on sensory integration processes and mobility. The hypothesis was that the task-specific and repetitive robot-assisted stairs climbing training might act as sensory integration balance training, improving postural control because sensorimotor integration processes are essential for balance and walking.[…]


Continue —->  Frontiers | Robot-Assisted Stair Climbing Training on Postural Control and Sensory Integration Processes in Chronic Post-stroke Patients: A Randomized Controlled Clinical Trial | Neuroscience

Figure 1. The G-EO system used in the Robot-Assisted Stair-Climbing Training (Written informed consent was obtained from the individual pictured, for the publication of this image).


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[Abstract] Neural Correlates of Passive Position Finger Sense After Stroke

Background. Proprioception of fingers is essential for motor control. Reduced proprioception is common after stroke and is associated with longer hospitalization and reduced quality of life. Neural correlates of proprioception deficits after stroke remain incompletely understood, partly because of weaknesses of clinical proprioception assessments.

Objective. To examine the neural basis of finger proprioception deficits after stroke. We hypothesized that a model incorporating both neural injury and neural function of the somatosensory system is necessary for delineating proprioception deficits poststroke.

Methods. Finger proprioception was measured using a robot in 27 individuals with chronic unilateral stroke; measures of neural injury (damage to gray and white matter, including corticospinal and thalamocortical sensory tracts), neural function (activation of and connectivity of cortical sensorimotor areas), and clinical status (demographics and behavioral measures) were also assessed.

Results. Impairment in finger proprioception was present contralesionally in 67% and bilaterally in 56%. Robotic measures of proprioception deficits were more sensitive than standard scales and were specific to proprioception. Multivariable modeling found that contralesional proprioception deficits were best explained (r2 = 0.63; P = .0006) by a combination of neural function (connectivity between ipsilesional secondary somatosensory cortex and ipsilesional primary motor cortex) and neural injury (total sensory system injury).

Conclusions. Impairment of finger proprioception occurs frequently after stroke and is best measured using a quantitative device such as a robot. A model containing a measure of neural function plus a measure of neural injury best explained proprioception performance. These measurements might be useful in the development of novel neurorehabilitation therapies.

via Neural Correlates of Passive Position Finger Sense After Stroke – Morgan L. Ingemanson, Justin R. Rowe, Vicky Chan, Jeff Riley, Eric T. Wolbrecht, David J. Reinkensmeyer, Steven C. Cramer, 2019

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[ARTICLE] Quantification of upper limb position sense using an exoskeleton and a virtual reality display – Full Text



Proprioceptive sense plays a significant role in the generation and correction of skilled movements and, consequently, in most activities of daily living. We developed a new proprioception assessment protocol that enables the quantification of elbow position sense without using the opposite arm, involving active movement of the evaluated limb or relying on working memory. The aims of this descriptive study were to validate this assessment protocol by quantifying the elbow position sense of healthy adults, before using it in individuals who sustained a stroke, and to investigate its test-retest reliability.


Elbow joint position sense was quantified using a robotic device and a virtual reality system. Two assessments were performed, by the same evaluator, with a one-week interval. While the participant’s arms and hands were occluded from vision, the exoskeleton passively moved the dominant arm from an initial to a target position. Then, a virtual arm representation was projected on a screen placed over the participant’s arm. This virtual representation and the real arm were not perfectly superimposed, however. Participants had to indicate verbally the relative position of their arm (more flexed or more extended; two-alternative forced choice paradigm) compared to the virtual representation. Each participant completed a total of 136 trials, distributed in three phases. The angular differences between the participant’s arm and the virtual representation ranged from 1° to 27° and changed pseudo-randomly across trials. No feedback about results was provided to the participants during the task. A discrimination threshold was statistically extracted from a sigmoid curve fit representing the relationship between the angular difference and the percentage of successful trials. Test-retest reliability was evaluated with 3 different complementary approaches, i.e. a Bland-Altman analysis, an intraclass correlation coefficient (ICC) and a standard error of measurement (SEm).


Thirty participants (24.6 years old; 17 males, 25 right-handed) completed both assessments. The mean discrimination thresholds were 7.0 ± 2.4 (mean ± standard deviation) and 5.9 ± 2.1 degrees for the first and the second assessment session, respectively. This small difference between assessments was significant (− 1.1 ± 2.2 degrees), however. The assessment protocol was characterized by a fair to good test-retest reliability (ICC = 0.47).


This study demonstrated the potential of this assessment protocol to objectively quantify elbow position sense in healthy individuals. Futures studies will validate this protocol in older adults and in individuals who sustained a stroke.



Proprioception is defined as the ability to perceive body segment positions and movements in space [1]. Sensory receptors involved in proprioception are mostly located in muscle [234], joint [56] and skin [37]. Proprioceptive sense is known to play a significant role in motor control [891011] and learning [812], particularly in the absence of vision. The importance of proprioceptive inputs has been demonstrated while studying individuals who presented lack of proprioception due to large-fiber sensory neuropathy [1112]. Despite an intact motor system, somatosensory deafferentation may lead to limitations in several activities involving motor skills, such as eating or dressing [12]. These disabilities may also be observed in individuals with proprioceptive impairments due to a stroke. Indeed, approximately half of the individuals who sustained a stroke present proprioceptive impairments in contralesional upper limb [13]. After a stroke, proprioception is known to be related to recovery of functional mobility and independence in activities of daily living (ADL; [14]). Fewer individuals with significant proprioceptive and motor losses (25%) were independent in ADL than individuals with motor deficits alone (78%). Moreover, fewer individuals with proprioceptive deficits (60%) after a stroke are discharged from the hospital directly to home compared to those without proprioceptive deficits (92%) [15].

Although the negative impact of proprioceptive impairments on motor and functional recovery is known, a large proportion of clinicians (70%) report not using standardised assessment to evaluate somatosensory deficits in patients with a stroke [16]. In clinical and research settings, proprioception is most frequently assessed with limb-matching tasks. Two types of matching tasks have commonly been used: the ipsilateral remembered matching task and the contralateral concurrent matching task [17]. In an ipsilateral remembered matching task, the evaluator or robotic device brings the patient’s limb to a target joint position, when the patient’s eyes are closed, keeps the limb in this position for several seconds, and then moves back the limb to the initial position. The patient needs to memorize the reference position and replicate it with the same (ipsilateral) limb. This task cannot, however, be used to evaluate proprioception in individuals with working memory issues, which represent around 25% of individuals who sustained a stroke [18]. In such cases, the matching error observed could reflect memory deficits, rather than proprioceptive impairments. Moreover, upper limb paresis affects 76% of individuals who sustained a stroke [19], making the task’s execution difficult or impossible. Assessing proprioception with the less affected arm as the indicator arm is therefore frequently considered in patients with hemiparesis. Indeed, in a contralateral concurrent matching task, the patient has to reproduce a mirror image of the evaluated limb position with the opposite (contralateral) limb [17]. However, considering that 20% of individuals who sustained a stroke also presents proprioceptive impairment on the ipsilateral side of the lesion [13], it would be difficult to ascertain whether the error is due to deficits in the evaluated arm, the opposite arm or both. In addition, interhemispheric communication is required in a contralateral concurrent matching task. Individuals with asymmetric stroke or with transcallosal degeneration would therefore be particularly disadvantaged while being assessed with a contralateral concurrent matching task [17].

In order to study proprioception in individuals who sustained a stroke, we developed an assessment protocol, that combines the use of an exoskeleton and a virtual reality system, enabling the quantification of position sense without using the opposite arm, involving active movement of the evaluated limb or relying on working memory. The primary objective of the present study was to validate the assessment protocol by quantifying the elbow joint position sense of healthy adults, before using this protocol with individuals who sustained a stroke. As a secondary objective, test-retest reliability of the assessment protocol was investigated.[…]


Continue —> Quantification of upper limb position sense using an exoskeleton and a virtual reality display | Journal of NeuroEngineering and Rehabilitation | Full Text


Fig. 1KINARM Exoskeleton Lab. a Modified wheelchair with each arm supported against gravity by exoskeletons; (b) Virtual reality display; (c) Virtual arm and real arm positions (blue line; non-visible for the participant) where ∆Θ represents the angular difference between the real and the virtual arm. The white circle corresponds to the center of rotation, i.e. the elbow joint

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[ARTICLE] Assessment of the correlations between gait speed in post-stroke patients and the time from stroke onset, the level of motor control in the paretic lower limb, proprioception, visual field impairment and functional independence – Full Text PDF


Introduction: Gait recovery is one of the main objectives in the rehabilitation of post-stroke patients. The study aim was to assess the correlations between gait speed in post-stroke hemiparetic patients and the level of motor control in the paretic lower limb, the time from stroke onset, the subjects’ age as well as the impairment of proprioception and visual field.

Materials and methods: This retrospective study was performed at the Clinical Rehabilitation Ward of the Regional Hospital No. 2 in Rzeszow. The study group consisted of 600 patients after a first stroke who walked independently. The measurements focused on gait speed assessed in a 10-meter walking test, motor control in the lower limb according to Brunnström recovery stages, proprioception in lower limbs, visual field as well as functional independence according to The Barthel Index.

Results: The study revealed a slight negative correlation between gait speed and the subjects’ age (r = − 0.25). No correlation was found between mean gait speed and the time from stroke onset. On the other hand, gait speed strongly correlated both with the level of motor control in the lower limb (p = 0.0008) and the incidence of impaired proprioception. Additionally, a strong statistically significant correlation between the patients’ gait speed and the level of functional independence was found with the use of The Barthel Index.

Conclusions: The level of motor control in the paretic lower limb and proprioception are vital factors affecting gait speed and functional independence. Patients with a higher level of functional independence demonstrated higher gait speed.


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[ARTICLE] A composite robotic-based measure of upper limb proprioception – Full Text



Proprioception is the sense of the position and movement of our limbs, and is vital for executing coordinated movements. Proprioceptive disorders are common following stroke, but clinical tests for measuring impairments in proprioception are simple ordinal scales that are unreliable and relatively crude. We developed and validated specific kinematic parameters to quantify proprioception and compared two common metrics, Euclidean and Mahalanobis distances, to combine these parameters into an overall summary score of proprioception.


We used the KINARM robotic exoskeleton to assess proprioception of the upper limb in subjects with stroke (N = 285. Mean days post-stroke = 12 ± 15). Two aspects of proprioception (position sense and kinesthetic sense) were tested using two mirror-matching tasks without vision. The tasks produced 12 parameters to quantify position sense and eight to quantify kinesthesia. The Euclidean and Mahalanobis distances of the z-scores for these parameters were computed each for position sense, kinesthetic sense, and overall proprioceptive function (average score of position and kinesthetic sense).


A high proportion of stroke subjects were impaired on position matching (57%), kinesthetic matching (65%), and overall proprioception (62%). Robotic tasks were significantly correlated with clinical measures of upper extremity proprioception, motor impairment, and overall functional independence. Composite scores derived from the Euclidean distance and Mahalanobis distance showed strong content validity as they were highly correlated (r = 0.97–0.99).


We have outlined a composite measure of upper extremity proprioception to provide a single continuous outcome measure of proprioceptive function for use in clinical trials of rehabilitation. Multiple aspects of proprioception including sense of position, direction, speed, and amplitude of movement were incorporated into this measure. Despite similarities in the scores obtained with these two distance metrics, the Mahalanobis distance was preferred.


Stroke is heterogeneous, affecting sensory, motor, and cognitive functions that are required for daily activities. While there are well validated tools to assess motor and speech functions (eg. Fugl-Meyer Assessment (FMA) [1], the National Institute of Health Stroke Scale (NIHSS) [2], Chedoke-McMaster Stroke Assessment Impairment Inventory (CMSA) [3]) the use of high quality, validated assessment tools for measuring sensory function post-stroke (proprioception in particular) is limited [4], and there is still a lack of a gold standard assessment. While the FMA and NIHSS have sensory components to the assessment, they are seldom used as a sole measure of sensory impairment in research studies focused on sensation as they are based on relatively coarse scales. Yet, sensory and proprioceptive impairments have a significant negative impact on functional recovery following stroke [56789]. Individuals with sensory and motor impairments, compared to those with just motor impairments, have longer lengths of hospitalization and fewer discharges home [101112]. Furthermore, it has recently been shown that motor and proprioceptive impairments can occur independently after stroke [13].

Some commonly used clinical assessments of proprioception post-stroke include: 1) simple passive limb movement detection test [14] in which an examiner moves a subject’s limb segment with their eyes closed, and subjects are asked to say which direction the limb was moved; 2) the Revised Nottingham Sensory Assessment [1516] in which the subject is asked to mirror match the movement of a passively moved limb by a therapist; and 3) the Thumb Localizing Test [17] which involves passive movement of a subject’s arm and hand to a random position overhead, and is followed by subjects reaching to grasp their thumb with the opposite (less affected) hand. These assessments are scored crudely as normal, slightly impaired, or absent, and lack the sensitivity to detect smaller changes in proprioceptive function in part due to poor inter- and intrarater reliability [1819]. Therefore, establishing an objective and reproducible method to assess proprioceptive impairments post-stroke is vital to evaluating the efficacy of different treatments.

Other more advanced methods to assess proprioception have been developed [20212223], with many using robotic technology to measure the kinematics of an individual’s movements. Assessment devices can now measure position sense and kinesthetic impairments after stroke using arm contralateral matching [13242526], in which a subject’s affected arm is passively moved by the robot to a position, and the subject mirror-matches the movement/position with their less affected limb. Another paradigm involves passive movement of a subject’s limb to a specified position, returning the limb to the starting position, and then having subjects actively move the same arm to this remembered position [2126]. This method has an advantage in that it does not require interhemispheric transfer of information, but has limited value in assessing people with concurrent motor deficits, or in assessing kinematic aspects of proprioception, such movement speed and amplitude perception. Further, results can be confounded by problems with spatial working memory. Threshold for detection of passive movement paradigms have also been used to assess proprioception [2728]. This paradigm eliminates confounds due to motor impairment and interhemispheric transfer of information but again, little information about the kinematics of movement perception (e.g. speed or direction) are gained from this task, and it typically takes much longer to complete than position/movement matching. Lastly, Carey et al. [20] have developed and validated a wrist position sense test, where a subject’s wrist is moved to a position (wrist flexion or extension) and without vision of the wrist the subject has to use their other arm to move a cursor to the direction the wrist is pointing. This method minimizes confounds due to interhemispheric information transfer and motor deficits, but again does not provide information about kinesthetic impairments.

Many of these assessments are reliable, reproducible, objective, and provide quantitative measures of proprioceptive function in the upper limbs. Dukelow et al. [1324], used a KINARM robot (BKIN Technologies, Kingston, ON), and detailed a contralateral position-matching task for the upper extremities that can measure various aspects of an individual’s position sense including: absolute error, variability in matching positions, systematic shifts in perceived workspace, and perceived contraction or expansion of the workspace. Similarly, Semrau et al. [25] recently detailed a kinesthetic matching task using the KINARM robot that can measure an individual’s ability to mirror-match the speed, direction, and amplitude of a robotically moved limb [825]. These tasks are reliable [24], and provide numerous parameters that describe an individual’s position or kinesthetic sense impairments and can be used to guide a rehabilitation program tailored to the individual. Furthermore, these studies have shown a strong relationship between proprioceptive impairments and functional independence post-stroke, yet proprioceptive impairments are often not addressed in day-to-day therapy. Reliable and quantitative assessment tools are therefore critical for testing the efficacy of rehabilitation treatments, as in clinical rehabilitation trials.

While multiple kinematic parameters can provide a level of exactness around the nature of an individual’s proprioceptive impairments and are helpful for rehabilitation planning, a summary measure is needed for clinical therapeutic trials in rehabilitation. Thus, a single continuous metric of upper limb proprioceptive function that combines all parameters from the position and kinesthetic matching robotic tasks was developed using two common measures of distance, Euclidean distance (EDist) and Mahalanobis distance (MDist) [29]. The EDist was chosen as it is an easily interpretable calculation and considers each parameter independently. It is the square root of the sum of squared distances between data points (i.e. the straight-line distance between two points in three-dimensional space). The MDist is the next measure we used to compare with the EDist. It was chosen because the calculation accounts for correlations between parameters (by using the inverse of the variance-covariance matrix of the data set of interest), therefore preventing the overweighting of correlated parameters in the calculation. It is the distance between a point and the center of a distribution, measured along the major axes of variation (i.e. the standard deviation of an object in more than one dimension) [3031].. Because the kinematic parameters derived from the robotic tasks may demonstrate some degree of correlation with one another [13], the MDist can account for this auto-correlation. Theoretically, it should perform better at identifying stroke subjects who perform abnormally on the tasks and those who have atypical patterns of behavior relative to controls. The MDist is generally preferred over the EDist for multivariable data since it can cope with different structures of data [31].

MDist (or variants of it) has recently been used in other studies when examining reaching movements after stroke [32].. Our primary aim was to examine differences and similarities between two summary scores (EDist and MDist) in their ability to differentiate proprioceptive impairment in individuals with stroke from controls in a large patient sample. We hypothesized that using a composite proprioception score calculated from the Mahalanobis distance would more accurately identify impaired proprioception in individuals with stroke compared to a proprioception score calculated from the Euclidean distance.[…]


Continue —>  A composite robotic-based measure of upper limb proprioception | Journal of NeuroEngineering and Rehabilitation | Full Text


Fig. 1a KINARM robotic exoskeleton (BKIN Technologies, Kingston, ON, Canda). Subjects are seated in the wheelchair base with arms supported by the arm troughs. b Top-down view of the position matching task. The stroke affected arm was positioned by the robot (black targets, green lines) and subjects were required to mirror-match the target positions with their opposite hand (open targets, blue lines). Nine targets were matched to six times each for a total of 54 trials, presented in pseudorandom order. c Top-down view of an exemplar subject performing one trial of the kinesthetic matching task. The stroke affected arm was moved by the robot between two targets (green lines) and subjects were required to mirror match the speed, direction, and amplitude of movement as soon as they felt the robot move their arm (blue lines). The speed versus time profile represents the temporal aspects of the task, by measuring the response latency (time to initiation of the active arm movement) and peak speed ratio (difference between peak speeds of the passive (green) and active (blue) hands)

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[Abstract] Effect of elastic bandage on postural control in subjects with chronic ankle instability: a randomised clinical trial

Purpose: To report the immediate and prolonged (one week) effects of elastic bandage (EB) on balance control in subjects with chronic ankle instability.
Material and methods: Twenty-eight individuals successfully completed the study protocol, of whom 14 were randomly assigned to the EB group (7 men, 7 women) and 14 were assigned to the non-standardised tape (NST) group (9 men, 5 women). To objectively measure postural sway we used computerised dynamic posturography (CDP) with sensory organisation test (SOT) and unilateral stance (US) test. We analysed the following SOT parameters: the composite SOT score, the composite SOT strategy and the SOT condition 2 and its strategy. In addition, we studied the centre of gravity (COG) sway velocity with open eyes and close eyes during the US test.
Results: Repeated measures ANOVA showed a significant effect for time in composite SOT score (F= 34.98; p= <0.01), composite SOT strategy (F= 12.082; p= 0.02), and COG sway with open eyes (F= 3.382; p= 0.039) in EB group and NST group. Therefore, there were improvements in balance control after bandage applications (defined as better scores in SOT parameters and decreased COG sway in US test). However, no differences between groups were observed in the most relevant parameters.
Conclusions: This study did not observe differences between EB and NST during the follow-up in the majority of measurements. Several outcome measures for SOT and US tests improved in both groups immediately after bandage applications and after one week of use. EB of the ankle joint has no advantage as compared to the non-standardised tape.

Implications for rehabilitation

  • Elastic bandage (EB) of the ankle joint has no advantage as compared to the non-standardised tape.
  • The effects of the bandages could be due to a greater subjective sense of security.
  • It is important to be prudent with the use of bandage, since a greater sense of safety could also bring with it a greater risk of injury.
  • The application of the bandage on subjects with chronic ankle instability (CAI) should be prolonged and used alongside other physiotherapy treatments.

Source: Effect of elastic bandage on postural control in subjects with chronic ankle instability: a randomised clinical trial

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[ARTICLE] Strength of ~20-Hz Rebound and Motor Recovery After Stroke – Full Text


Background. Stroke is a major cause of disability worldwide, and effective rehabilitation is crucial to regain skills for independent living. Recently, novel therapeutic approaches manipulating the excitatory-inhibitory balance of the motor cortex have been introduced to boost recovery after stroke. However, stroke-induced neurophysiological changes of the motor cortex may vary despite of similar clinical symptoms. Therefore, better understanding of excitability changes after stroke is essential when developing and targeting novel therapeutic approaches.

Objective and Methods. We identified recovery-related alterations in motor cortex excitability after stroke using magnetoencephalography. Dynamics (suppression and rebound) of the ~20-Hz motor cortex rhythm were monitored during passive movement of the index finger in 23 stroke patients with upper limb paresis at acute phase, 1 month, and 1 year after stroke.

Results. After stroke, the strength of the ~20-Hz rebound to stimulation of both impaired and healthy hand was decreased with respect to the controls in the affected (AH) and unaffected (UH) hemispheres, and increased during recovery. Importantly, the rebound strength was lower than that of the controls in the AH and UH also to healthy-hand stimulation despite of intact afferent input. In the AH, the rebound strength to impaired-hand stimulation correlated with hand motor recovery.

Conclusions. Motor cortex excitability is increased bilaterally after stroke and decreases concomitantly with recovery. Motor cortex excitability changes are related to both alterations in local excitatory-inhibitory circuits and changes in afferent input. Fluent sensorimotor integration, which is closely coupled with excitability changes, seems to be a key factor for motor recovery.

Approximately 75% of stroke survivors suffer from permanent disability; thus, stroke causes significant human suffering and poses a major economic burden on the society.1 Recovery from stroke is based on brain’s plasticity. Studies in both animals and humans have shown that a period of enhanced plasticity occurs 1-4 weeks after stroke.25 After this sensitive period, the effectiveness of poststroke rehabilitation diminishes dramatically. Recently, there have been promising attempts to prolong or enhance the sensitive period with pharmacological manipulations68 or with noninvasive brain stimulation,9,10 both aiming at changing the cortical excitation-inhibition balance. However, patients with initially similar clinical symptoms may recover differently, possibly because the underlying neurophysiological changes vary between these patients. Thus, understanding and monitoring recovery-related neurophysiological mechanisms and their temporal evolution is crucial for developing efficient, personalized rehabilitation.

Fluent upper limb motor function is important for independency in daily life. Integration of proprioceptive and tactile input with motor plans forms the basis of smooth and precise movements.11 Afferent input mediates its effect on motor functions by modulating the motor cortex excitability.12 Accordingly, our previous study in healthy subjects indicated that proprioceptive input strongly modulates the ~20-Hz motor cortex rhythm, causing an initial suppression followed by a strong and robust rebound.13 Prior studies have suggested that the ~20-Hz rebound reflects deactivation or inhibition of the motor cortex.1417 Moreover, a combined magnetiencephalography (MEG) and magnetic resonance spectroscopy study showed that the ~20-Hz rebound strength is associated with the concentration of the inhibitory neurotransmitter GABA (γ-aminobutyric acid).18

To study alterations in motor cortex excitability after stroke and its association with motor recovery, we measured the dynamics of ~20-Hz motor cortex oscillations during passive movement of the index fingers in 23 stroke patients at the acute phase and during 1-year recovery. The motivation of this study was to understand the neurophysiological mechanisms underlying stroke recovery, which is instrumental for developing novel therapeutic interventions.

Continue —> Strength of ~20-Hz Rebound and Motor Recovery After Stroke – Feb 04, 2017


Figure 1. (A) Setup for passive movement. (B) Representative signals of 1 patient at T0 (1-7 days), T1 (1 month), and T2 (12 months) after stroke. Two upper rows: Magnetoencephalography signals from a single gradiometer channel (raw and filtered to 15-25 Hz over the primary sensorimotor cortex. The ~20-Hz modulation is observable even to a single movement. Third row: Magnitude of acceleration. Total duration of the movement highlighted in gray.

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