Archive for category Paretic Hand

[WEB SITE] Virtual Reality Training Rivals Conventional Therapy After Stroke

Virtual reality training was as effective as, but not superior to, conventional therapy for improving arm and hand function after stroke when both were added to standard rehabilitation in the subacute phase of stroke recovery, researchers found.

In the phase III VIRTUES study, conducted at five rehabilitation hospitals in Europe, similar and significant improvements from baseline assessments of arm and hand mobility were seen at the end of the 4-week intervention and at 3-month follow-up, but there was no difference between the two groups in the results for any endpoints (P<0.001), Iris Brunner, PhD, of Aarhus University, Hammel Neurocenter in Denmark, and colleagues reported online in Neurology.

“These results suggest that either type of training could be used, depending on what the patient prefers,” Brunner said in a statement. “Virtual reality training may be a motivating alternative for people to use as a supplement to their standard therapy after a stroke.”

Improvement of upper extremity motor function performance on the Action Research Arm Test (ARAT) was similar with the virtual reality and conventional training after the 4-week intervention and at follow-up. Patients in virtual reality training improved their ARAT scores an average of 12 points (21%) from baseline to the postintervention assessment, and 17 points (30%) at 3-month follow-up, while those receiving conventional training improved 13 points (21%) at those respective assessments.

Likewise, no differences were seen between the virtual reality and conventional training groups in secondary endpoints, including the Box and Blocks Test, Functional Independence Measure, and Patient Global Impression of Change assessment.

The study involved 120 patients (average age 62) enrolled between March 2014 and April 2016 who had mild-to-severe upper extremity impairment in their wrists, hands, or upper arms as a result of suffering a stroke an average of one month before the study started.

For the add-on conventional or virtual reality therapy, participants had four to five hour-long training sessions per week for four weeks: 62 received conventional physical and occupational therapy, and 58 received virtual reality training that involved using a screen and gloves with sensors to play games that could be adapted to the person’s abilities.

Level of impairment had no differential effect on outcomes, which were similar for patients with mild/moderate impairment – defined as the ability to extend the wrist at least 20 degrees and the fingers at least 10 degrees from drop hand position – or severe impairment. On ARAT, improvements at 3-month follow-up in the mild/moderate group were 14 points (25%) with virtual reality (VR) training and 13 points (23%) with conventional therapy, while the severe group improved 23 points (40%) with VR and 23 points (40%) with conventional therapy.

While active training time was considerably increased among severely impaired participants using virtual reality training compared to those using conventional training, this was not reflected in a larger improvement in arm motor function, authors wrote. This reflects a study design limitation, they wrote: The addition of a third arm receiving only standard rehabilitation would have helped identify potential benefits of more intensive training and increased training time, as previously reported.

Danielle Levac, MD, PhD, PT, of Northeastern University in Boston, who was not involved in the study, agreed with Brunner and colleagues that future study should apply outcome measures that differentiate between recovery on an impairment level and compensation, given that training intensity within the first few months of a stroke is crucial for maximally exploiting the window of increased plasticity.

Also, neither patient engagement nor motivation — attributes through which VR systems are thought to increase adherence and potentially enhance motor learning — were “subjectively or objectively measured here, which seriously detracts from the author’s conclusions that VR constitutes ‘motivating’ training,” Levac told MedPage Today.

The numerous small studies that have demonstrated benefits of virtual reality training have used specially engineered rehab-specific systems, whereas a recent larger trial in subacute stroke patients that did not find superiority over conventional training used a commercial gaming system.

“It is the low cost and easy accessibility of off-the-shelf gaming systems that have made them so pervasive and attractive in clinical practice, despite the disadvantages for tailoring to individual patient needs noted by the authors,” Levac said.

Robert Teasell MD, of Western University in London, Ontario, and head of the Stroke Rehabilitation Writing Group for the Canadian Stroke Best Practice Recommendations, told MedPage Today that many small trials of virtual reality training have demonstrated a benefit in stroke patients.

“This study is important because it is comparatively larger, employs a multisite design, and has an active control group which gets an equal amount of ‘conventional’ therapy and not just ‘usual care,'” said Teasell, who was not involved in the study. “It demonstrates effectiveness – although not superiority – of virtual reality as a promising adjunct treatment.”

via Virtual Reality Training Rivals Conventional Therapy After Stroke | Medpage Today


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[ARTICLE] Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study – Full Text



Technology-mediated neurorehabilitation is suggested to enhance training intensity and therefore functional gains. Here, we used a novel virtual reality (VR) system for task-specific upper extremity training after stroke. The system offers interactive exercises integrating motor priming techniques and embodied visuomotor feedback. In this pilot study, we examined (i) rehabilitation dose and training intensity, (ii) functional improvements, and (iii) safety and tolerance when exposed to intensive VR rehabilitation.


Ten outpatient stroke survivors with chronic (>6 months) upper extremity paresis participated in a ten-session VR-based upper limb rehabilitation program (2 sessions/week).


All participants completed all sessions of the treatment. In total, they received a median of 403 min of upper limb therapy, with 290 min of effective training. Within that time, participants performed a median of 4713 goal-directed movements. Importantly, training intensity increased progressively across sessions from 13.2 to 17.3 movements per minute. Clinical measures show that despite being in the chronic phase, where recovery potential is thought to be limited, participants showed a median improvement rate of 5.3% in motor function (Fugl-Meyer Assessment for Upper Extremity; FMA-UE) post intervention compared to baseline, and of 15.4% at one-month follow-up. For three of them, this improvement was clinically significant. A significant improvement in shoulder active range of motion (AROM) was also observed at follow-up. Participants reported very low levels of pain, stress and fatigue following each session of training, indicating that the intensive VR intervention was well tolerated. No severe adverse events were reported. All participants expressed their interest in continuing the intervention at the hospital or even at home, suggesting high levels of adherence and motivation for the provided intervention.


This pilot study showed how a dedicated VR system could deliver high rehabilitation doses and, importantly, intensive training in chronic stroke survivors. FMA-UE and AROM results suggest that task-specific VR training may be beneficial for further functional recovery both in the chronic stage of stroke. Longitudinal studies with higher doses and sample sizes are required to confirm the therapy effectiveness.


Stroke affects about 17 million people per year worldwide, with an increasing rate every year [1]. Stroke survivors often suffer from physical and mental disabilities, heavily impacting their quality of life. Five years after the first stroke, nearly 66% of patients exhibit different degrees of disability and only 34% are functionally independent in their activities of daily living [2].[…]


Continue —> Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study | Journal of NeuroEngineering and Rehabilitation | Full Text

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[Abstract] MaLT – Combined Motor and Language Therapy Tool for Brain Injury Patients Using Kinect.



The functional connectivity and structural proximity of elements of the language and motor systems result in frequent co-morbidity post brain injury. Although rehabilitation services are becoming increasingly multidisciplinary and “integrated”, treatment for language and motor functions often occurs in isolation. Thus, behavioural therapies which promote neural reorganisation do not reflect the high intersystem connectivity of the neurologically intact brain. As such, there is a pressing need for rehabilitation tools which better reflect and target the impaired cognitive networks.


The objective of this research is to develop a combined high dosage therapy tool for language and motor rehabilitation. The rehabilitation therapy tool developed, MaLT (Motor and Language Therapy), comprises a suite of computer games targeting both language and motor therapy that use the Kinect sensor as an interaction device. The games developed are intended for use in the home environment over prolonged periods of time. In order to track patients’ engagement with the games and their rehabilitation progress, the game records patient performance data for the therapist to interrogate.


MaLT incorporates Kinect-based games, a database of objects and language parameters, and a reporting tool for therapists. Games have been developed that target four major language therapy tasks involving single word comprehension, initial phoneme identification, rhyme identification and a naming task. These tasks have 8 levels each increasing in difficulty. A database of 750 objects is used to programmatically generate appropriate questions for the game, providing both targeted therapy and unique gameplay every time. The design of the games has been informed by therapists and by discussions with a Public Patient Involvement (PPI) group.


Pilot MaLT trials have been conducted with three stroke survivors for the duration of 6 to 8 weeks. Patients’ performance is monitored through MaLT’s reporting facility presented as graphs plotted from patient game data. Performance indicators include reaction time, accuracy, number of incorrect responses and hand use. The resultant games have also been tested by the PPI with a positive response and further suggestions for future modifications made.


MaLT provides a tool that innovatively combines motor and language therapy for high dosage rehabilitation in the home. It has demonstrated that motion sensor technology can be successfully combined with a language therapy task to target both upper limb and linguistic impairment in patients following brain injury. The initial studies on stroke survivors have demonstrated that the combined therapy approach is viable and the outputs of this study will inform planned larger scale future trials.



via MaLT – Combined Motor and Language Therapy Tool for Brain Injury Patients Using Kinect. – PubMed – NCBI

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[ARTICLE] Study of Repetitive Movements Induced Oscillatory Activities in Healthy Subjects and Chronic Stroke Patients – Full Text


Repetitive movements at a constant rate require the integration of internal time counting and motor neural networks. Previous studies have proved that humans can follow short durations automatically (automatic timing) but require more cognitive efforts to track or estimate long durations. In this study, we studied sensorimotor oscillatory activities in healthy subjects and chronic stroke patients when subjects were performing repetitive finger movements. We found the movement-modulated changes in alpha and beta oscillatory activities were decreased with the increase of movement rates in finger lifting of healthy subjects and the non-paretic hands in stroke patients, whereas no difference was found in the paretic-hand movements at different movement rates in stroke patients. The significant difference in oscillatory activities between movements of non-paretic hands and paretic hands could imply the requirement of higher cognitive efforts to perform fast repetitive movements in paretic hands. The sensorimotor oscillatory response in fast repetitive movements could be a possible indicator to probe the recovery of motor function in stroke patients.


Timing in the brain has its important role in many aspects, such as speech perception, speech production, reading, attention, memory, cognitive processing, decision-making, and motor coordination1. Especially, internal time counting is crucial for motor control in our daily life activities. The processing of time estimation for movements has been studied in many literatures2. Morillon et al. postulated the time estimation in human motor system as a dual system, which can track a short duration automatically (automatic timing) but requires more cognitive demands to track a long duration by a so-called default mode network (DMN)3. Poppel E. studied the capability of time estimation in a stimulus reproduction task from 0.5 s to 7 s, and found movements become temporally irregular for inter-movement interval (IMI) above 3 s which indicated precisely control of movements with IMIs longer than 3 s is not possible4. Though these literatures have shown great difference between movements in short and long durations in healthy subjects, nevertheless, the study of brain responses induced by rapid movements in patients with motor neurological disorder was seldom reported.

Several imaging modalities have been developed to quantify motor response in human brain, including EEG, MEG, fMRI, TMS, etc.5,6. The EEG, which is the tool used most widely, has the advantages of low-cost, easy preparation, and its superiority of high temporal resolution to measure fast changes of neural oscillatory activities. Neural oscillatory activities in human brain can be either phase-locked or non-phase-locked reactive to external or internal stimuli. These oscillatory activities usually exist in specific frequency bands and spatial locations. Event-related non-phase-locked neural activities represent power changes, either enhanced or suppressed relative to baseline activities. The power changes in event-related activities can be caused by the decrease or increase in synchrony of the underlying activated neuronal populations. Pfurtscheller et al.7 studied the Mu-rhythm changes in discrete voluntary finger movements, and found oscillatory activities were suppressed, started about 1.5 s preceding movement onsets, followed by post-movement power rebound, occurred around 0.7 s~1 s after movement offsets7. The power suppression was referred to as event-related desynchronization (ERD), reflecting the motor planning and preparation of initialization a movement, whereas the post-movement power rebound was referred to as event-related synchronization (ERS), indicating the motor inhibition or idling of motor neural network. Other EEG techniques, such as temporal-spectral evolution (TSE)8, amplitude modulation (AM)9, autoregression model method (AR)10, etc., were also developed to quantify task-specific brain oscillatory activity. These signal processing tools enable researchers to quantify the neural activities under different experimental manipulations and provide evidences for diagnosing clinical neurological diseases11,12,13.

The difference of brain oscillatory activities between healthy and stroke patients has been investigated in some studies. Rossiter et al. studied the movement-related beta desynchronization (MRBD) in healthy and middle cerebral artery (MCA) stroke patients14. They found reduced MRBD when patients were performing visually-cued grip task with their affected hand, compared to the MRBD obtained from healthy subjects. Giaquinto et al. followed up the changes of resting EEG in different frequency bands over six months in MCA stroke patients15, and they observed the amplitudes of movement-related Mu – rhythm improved significantly in the first three months and reached stable states in six months after stroke. Tecchio et al. studied the rhythmic brain activity at resting states in mono-hemispheric MCA stoke patients16. They found both the values of spectral power in affected and unaffected hemispheres were increased over Rolandic areas. Stepien et al. studied alpha ERD in stroke patients with cortical and subcortical lesions in performing a visually-cued button press task17. They found suppressed ERD in affected hemisphere when moving paretic hand, while no suppression in alpha ERD was found in the affected hemisphere when moving non-paretic hand. These studies measured oscillatory activities of sensorimotor Mu rhythm in visual selection task or slow self-paced voluntary movement (IMI ≥ 7 s). Oscillatory activity induced by fast repetitive movement in stroke patient was not studied. Since fast simple movement has been reported to have strong coupled connections among motor-related cortices18, study of cortical oscillatory activity in rapid simple movements could be crucial for the understanding of motor function in stroke patients.

Fast repetitive movement with short IMI recruits several motor-related areas in human brain, including primary motor cortex (M1), premotor cortex, supplementary motor cortex, cingulate cortex, basal ganglia, and thalamus19. Studies in healthy subjects have shown clear difference between the oscillatory activities induced by slow and fast repetitive movements. Wu et al. recorded the post-movement beta rebound (PMBD) in healthy subjects and observed that the PMBD was suppressed with the decrease of IMI in repetitive finger-lifting movements19. Erbil and Ungan19 investigated EEG alpha and beta oscillatory activities in repetitive extension-flexion finger movements over rolandic regions. Sustained suppression in Mu rhythm was observed during continuous movements which indicated that continuous movements are conducted through neural processing distinct from discrete movements. Bortoletto and Cunnington measured the fMRI responses of repetitive movements, and compared the results with another two finger movements with highly cognitive demands, one was a complicated sequencing task and the other was a timing task20. They found neural activities in lateral prefrontal regions were participated differently in the three tasks, owing to the different levels of cognitive efforts involved in the three tasks. In this study, we aimed to study the oscillatory activities induced by simple repetitive movements in healthy subjects and chronic stroke patients. The difference of oscillatory activities between stroke patients and healthy subjects might be a potential feature to evaluate the recovery of motor function in stroke patients.[…]

Continue —> Study of Repetitive Movements Induced Oscillatory Activities in Healthy Subjects and Chronic Stroke Patients | Scientific Reports

Figure 1

Figure 1: Demonstration of signal processing for quantifying event-related oscillatory response in subject H1.

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[ARTICLE] Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients – Full Text



Virtual reality is nowadays used to facilitate motor recovery in stroke patients. Most virtual reality studies have involved chronic stroke patients; however, brain plasticity remains good in acute and subacute patients. Most virtual reality systems are only applicable to the proximal upper limbs (arms) because of the limitations of their capture systems. Nevertheless, the functional recovery of an affected hand is most difficult in the case of hemiparesis rehabilitation after a stroke. The recently developed Leap Motion controller can track the fine movements of both hands and fingers. Therefore, the present study explored the effects of a Leap Motion-based virtual reality system on subacute stroke. Twenty-six subacute stroke patients were assigned to an experimental group that received virtual reality training along with conventional occupational rehabilitation, and a control group that only received conventional rehabilitation. The Wolf motor function test (WMFT) was used to assess the motor function of the affected upper limb; functional magnetic resonance imaging was used to measure the cortical activation. After four weeks of treatment, the motor functions of the affected upper limbs were significantly improved in all the patients, with the improvement in the experimental group being significantly better than in the control group. The action performance time in the WMFT significantly decreased in the experimental group. Furthermore, the activation intensity and the laterality index of the contralateral primary sensorimotor cortex increased in both the experimental and control groups. These results confirmed that Leap Motion-based virtual reality training was a promising and feasible supplementary rehabilitation intervention, could facilitate the recovery of motor functions in subacute stroke patients. The study has been registered in the Chinese Clinical Trial Registry (registration number: ChiCTR-OCH-12002238).


Chronic conditions such as stroke are becoming more prevalent as the world’s population ages (Christensen et al., 2009). Although the number of fatalities caused by stroke has fallen in most countries, stroke is still a leading cause of acquired adult hemiparesis (Langhorne et al., 2009; Liu and Duan, 2017). Up to 85% of patients who survive a stroke experience hemiparesis, resulting in impaired movement of an arm and hand (Nakayama et al., 1994). Among them, a large proportion (46% to 95%) remain symptomatic six months after experiencing an ischemic stroke (Kong et al., 2011). The loss of upper limb function adversely affects the quality of life and impedes the normal use of other body parts. The motor function recovery of the upper limbs is more difficult than that of the lower extremities (Kwakkel et al., 1996; Nichols-Larsen et al., 2005; Día and Gutiérrez, 2013). Functional motor recovery in the affected upper extremities in patients with hemiparesis is the primary goal of physical therapists (Page et al., 2001). Evidence suggests that repetitive, task-oriented training of the paretic upper extremity is beneficial (Barreca et al., 2003; Wolf et al., 2006). Rehabilitation intervention is a critical part of the recovery and studies have reported that intensive repeated practice is likely necessary to modify the neural organization and favor the recovery of the functional upper limb motor skills of stroke survivors (Brunnstrom, 1966; Kopp et al., 1999; Taub et al., 1999; Wolf et al., 2006; Nudo, 2011). Meta-analyses of clinical trials have indicated that longer sessions of practice promote better outcomes in the case of impairments, thus improving the daily activities of people after a stroke (Nudo, 2011; Veerbeek et al., 2014; Sehatzadeh, 2015; French et al., 2016). However, the execution of these conventional rehabilitation techniques is tedious, resource-intensive, and often requires the transportation of patients to specialized facilities (Jutai and Teasell, 2003; Teasell et al., 2009).

Virtual reality training is becoming a promising technology that can promote motor recovery by providing high-intensity, repetitive, and task-orientated training with computer programs simulating three-dimensional situations in which patients play by moving their body parts (Saposnik et al., 2010, 2011; Kim et al., 2011; Laver et al., 2015; Tsoupikova et al., 2015). The gaming industry has developed a variety of virtual reality systems for both home and clinical applications (Saposnik et al., 2010; Bao et al., 2013; Orihuela-Espina et al., 2013; Gatica-Rojas and Méndez-Rebolledo, 2014). The most difficult task related to hemiparesis rehabilitation after a stroke is the functional recovery of the affected hand (Carey et al., 2002). To facilitate the functional recovery of a paretic hand along with that of the proximal upper extremity, an ideal virtual reality system should be able to track hand position and motion, which is not a feature of most existing virtual reality systems (Jang et al., 2005; Merians et al., 2009). The leap motion controller developed by Leap Motion ( provides a means of capturing and tracking the fine movements of the hand and fingers, while controlling a virtual environment requiring hand-arm coordination as part of the practicing of virtual tasks (Iosa et al., 2015; Smeragliuolo et al., 2016).

Most virtual reality studies have often only involved patients who have experienced chronic stroke (Piron et al., 2003; Yavuzer et al., 2008; Saposnik et al., 2010; da Silva Cameirao et al., 2011). For patients in the chronic stage, who had missed the window of opportunity present at the acute and subacute stages (in which the brain plasticity peaks), rehabilitation-therapy-induced neuroplasticity can only be effective within a relatively narrow range (Chen et al., 2002). No motor function recovery of the hands, six months after the onset of a stroke, indicates a poor prognosis for hand function (Duncan et al., 1992).

We hypothesized that Leap Motion-based virtual reality training would facilitate motor functional recovery of the affected upper limb, as well as neural reorganization in subacute stroke patients. Functional magnetic resonance imaging (fMRI), also called blood oxygenation level-dependent fMRI (BOLD-fMRI), is widely used as a non-invasive, convenient, and economical method to examine cerebral function (Ogawa et al., 1990; Iosa et al., 2015; Yu et al., 2016). In the present study, we evaluated the brain function reorganization by fMRI, as well as the motor function recovery of the affected upper limb in patients with subacute stroke using Leap Motion-based virtual reality training.[…]

Continue —>  Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients Wang Zr, Wang P, Xing L, Mei Lp, Zhao J, Zhang T – Neural Regen Res

Figure 1: Leap Motion-based virtual reality system and training games.
(A, B) Leap Motion-based virtual reality system; (C) petal-picking game; (D) piano-playing game; (E) robot-assembling game; (F) object-catching with balance board game; (G) firefly game; (H) bee-batting game.



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[ARTICLE] Active rehabilitation training system for upper limb based on virtual reality – Full Text


In this article, an active rehabilitation training system based on the virtual reality technology is designed for patients with the upper-limb hemiparesis. The six-axis inertial measurement unit sensors are used to acquire the range of motion of both shoulder and elbow joints. In order to enhance the effect of rehabilitation training, several virtual rehabilitation training games based on the Unity3D engine are designed to complete different tasks from simple level to complicated level. The purpose is to increase the patients’ interest during the rehabilitation training. The basic functions of the virtual rehabilitation task scenes are tested and verified through the single-joint training and the multi-joint compounding training experiments. The experimental results show that the ranges of motion of both shoulder and elbow joints can reach the required ranges of a normal human in the rehabilitation training games. Therefore, the system which is easy to wear, low cost, wireless communication, real-time data acquisition, and interesting virtual rehabilitation task games can provide an effective rehabilitation training process for the upper-limb hemiparesis at home.

The upper limb has many degrees of freedom, and it is also a complex part of the human body by which people can accomplish fine movements during their activities in daily life. With the intensification of the aging problem in the world, the amount of stroke hemiparesis has shown a growing trend, especially in China, which has an enormous population.1 Approximately 30%–50% of these stroke survivors will suffer from chronic hemiparesis, especially involving their hands. In addition, spinal cord injury (SCI) and traffic accident survivors may also find limb movements’ disorder. Injury within the cervical region of the cord leads to tetraplegia, which leads to impairment of all four limbs. An estimated result shows that 55% of new cases will result in tetraplegia, while the other 45% will experience paraplegia due to injury below the cervical level.2Limb hemiparesis which is caused by stroke, SCI, or traffic accidents not only gives the patient’s daily life a great deal of inconvenience and even more makes the patient suffer from great mental pain but also brings a heavy stress and medical burden for the patient’s family and society. Technology has been developed in an effort to facilitate rehabilitation for the patient. Upper-limb rehabilitation is one of the fastest growing areas in modern neurorehabilitation. Quality of life can be improved with efficient therapy.3 At present, rehabilitation therapy of upper limb with traditional rehabilitation therapy is commonly used, that is, rehabilitation therapists perform rehabilitation trainings on individuals. Now with the development of robot technology, the rehabilitation of robot-assisted training is also rising up. The MIT-Manus4 is an example of end-effector-based and arm-rehabilitation robotic device, while the ARMin device5 is an example of arm-rehabilitation exoskeletons which also allows pronation/supination of the lower arm and wrist flexion/extension. It could be operated in three modes: passive mobilization, active game-supported arm therapy, and active training of activities of daily living (ADLs). The end-effector-based robots have practical advantages (usability, simplicity, and cost-effectiveness), and exoskeleton robots have biomechanical advantages (better guidance). Currently, the automatic rehabilitation devices on market as mentioned above are mostly complex and expensive, which are often used in the hospitals and clinics are not affordable to ordinary patients. Therefore, one of the research objectives aims to develop the upper-limb rehabilitation training system with minimal structure and low cost and can be used in patient’s home. But in China, it can be seen that patients with upper-limb orthosis in home is only for fixing the arm and just move autonomously according to the setting angle. The researches on intelligent domestic rehabilitation device just begins, most of which are in the experimental stage and not yet market oriented.6,7

Another problem is that the patients are treated with low initiative and dull training process which does not motivate them, while the treatment effect is not obvious.8,9 Computer games based on virtual reality (VR) are a good way to mobilize the patients’ initiative in the training, so the rehabilitation effect on a particular movement task will be greatly improved.10 VR environments provide an excellent method to manipulate task conditions in training. The effects and the intensity of training can be enhanced and designed more challenging, since the implementation of VR can build a channel both visual and haptic communication can be involved in. The research on VR system which is applied to rehabilitation training was initiated a few years ago. Mazzone et al.11 made a study on the effect of rehabilitation training for patients with shoulder joints training using VR technology. This study aimed to determine whether performance of shoulder exercises in virtual reality gaming (VRG) results in similar muscle activation as non-VRG exercise. The conclusion was drawn that exercise with VRG should be effective to reduce shoulder pain caused by spinal injury. Fischer et al.12 conducted a preliminary study claim that stroke patients could assist themselves in training their hands in the virtual environment. The purpose of this pilot study was to investigate the impact of assisted motor training in a virtual environment on hand function for the stroke survivors. Participants had 6 weeks of training in reach-to-grasp of both virtual and actual objects. After the training period, participants in all three groups demonstrated a decrease in time to perform some of the functional tasks. These designs based on VR have achieved some success and then the second research objective is to add the VR technology to the intelligent domestic rehabilitation device. These studies are mainly designed for the single joint of the upper-limb rehabilitation training. Therefore, it is necessary to carry out the research on multi-joint combined training device for patients who can just stay home by training with VR tasks of adjustable game levels.

Another important element which needs to be considered as an ultimate success using at home is its ease of use. Therefore, simple active rehabilitation device should be developed. The setup time of such device should be fast, besides measurement, treatment approaches, and incorporating gaming, and should provide intuitive interfaces that can be directly utilized by the individuals. This study will introduce an active rehabilitation training system for upper limb based on VR technology, which has some advantages such as simple structure, easy to manipulate, and portable for household. It also mobilizes patients’ initiative with adjustable difficulty level of VR tasks so that the individuals’ rehabilitation effect of the upper limb is obviously improved.

The active rehabilitation training system for upper limb based on VR is designed for the pronation/supination and flexion movement trainings of the elbow joint and the extension/flexion and abduction exercises of the shoulder joint. By adding the games in training processes, the patients may actively participate in rehabilitation trainings, while the efficiency will be greatly improved. The portable and easy-to-use design of this system can effectively reduce the problem of the medical resources shortage in the rehabilitation field.

Overall scheme of the system

The system is composed of two parts: the upper-limb posture detection system and the virtual rehabilitation training task scene, as shown in Figure 1.


Figure 1. Schematic diagram of an active rehabilitation training system for upper limb based on VR.


Continue —> Active rehabilitation training system for upper limb based on virtual realityAdvances in Mechanical Engineering – Jianhai Han, Shujun Lian, Bingjing Guo, Xiangpan Li, Aimin You, 2017

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[ARTICLE] Mapping upper-limb motor performance after stroke – a novel method with utility for individualized motor training – Full Text



Chronic upper limb motor impairment is a common outcome of stroke. Therapeutic training can reduce motor impairment. Recently, a growing interest in evaluating motor training provided by robotic assistive devices has emerged. Robot-assisted therapy is attractive because it provides a means of increasing practice intensity without increasing the workload of physical therapists. However, movements practised through robotic assistive devices are commonly pre-defined and fixed across individuals. More optimal training may result from individualizing the selection of the trained movements based on the individual’s impairment profile. This requires quantitative assessment of the degree of the motor impairment prior to training, in relevant movement tasks. However, standard clinical measures for profiling motor impairment after stroke are often subjective and lack precision. We have developed a novel robot-mediated method for systematic and fine-grained mapping (or profiling) of individual performance across a wide range of planar arm reaching movements. Here we describe and demonstrate this mapping method and its utilization for individualized training. We also present a novel principle for the individualized selection of training movements based on the performance maps.

Methods and Results

To demonstrate the utility of our method we present examples of 2D performance maps produced from the kinetic and kinematics data of two individuals with stroke-related upper limb hemiparesis. The maps outline distinct regions of high motor impairment. The procedure of map-based selection of training movements and the change in motor performance following training is demonstrated for one participant.


The performance mapping method is feasible to produce (online or offline). The 2D maps are easy to interpret and to be utilized for selecting individual performance-based training. Different performance maps can be easily compared within and between individuals, which potentially has diagnostic utility.


Impaired upper-limb (UL) function is one of the most common consequences of stroke [123], which can severely hamper activities of daily living and reduce quality of life. Certain intervention methods can promote some recovery of UL motor function though their outcome shows high variability and depends on the intensity (repetition) of the intervention [456789].

Robotic assistive technologies can be beneficial for improving clinical scores of UL motor impairment [910], by allowing intensive training [911121314]. However, currently there is no consistent evidence for the effectiveness of robot-assisted UL therapy for improving daily living activity [15]. One possibility is that the tasks performed with robotic assistance do not generalise to everyday tasks. Another possibility is that the tasks are not optimised for the trained individuals. Currently, in robot-assisted therapy the set of practiced movements are usually pre-determined, with limited regard to the motor profile of the individual (e.g. ‘centre-out’ point-to-point reaches, or forearm pronation/supination, wrist extension/flexion [161718]). However, the effectiveness of training for motor recovery is likely to depend on the difficulty to perform the task due to motor impairment [19]. For example, training focused on unimpaired movements or on tasks that are either too easy or too difficult is likely to contribute relatively little to motor learning and recovery [192021]. An advantage of the robot-mediated approach is that it allows the collection of various accurate and real-time data about motor performance that would be potentially useful for individualized adjustments of the therapy; e.g. selection of training tasks based on the profile of motor performance. Yet, prescribing training conditions based on a motor performance profile requires characterising motor performance across a range of movement conditions for each individual. Here we present a novel computerised method for systematically mapping individuals’ UL motor performance (or impairment) across a wide range of robot-mediated reaching movements. The map can then serve as a basis for individualised and performance-based selection of training movements.

For optimal utilization of a motor performance map, the mapped metrics should reflect basic components of sensorimotor control, so that the map can be directly linked to processes underlying the movements (e.g. muscle activity and movement representation). Continuous metrics, allowing smoothing and interpolation from tested movements to neighbouring untested regions are also valuable. Accordingly, our mapping of reaching performance is done across the two dimensions of target location (in angular coordinates relative to a central position) and of prescribed starting location (again in angular coordinates relative to the selected target, which indicates the dictated movement direction). The range of target and start locations tests both postural and movement-related aspects of motor control, respectively. Importantly, muscle activation patterns and population neural activity in the motor-related cortices show tuning to one or both task dimensions [22232425], and behavioural studies support the essential underlying role of these parameters in planning of reaching movements [2627].

Of course, the usefulness of a motor performance map for prescribing performance-based training also depends on an appropriate principle for the selection of movements to be practiced. Here we demonstrate the utility of our mapping method for individualized task selection based on a principle which we term “steepest gradients” (SG), although the motor performance map can be the basis for alternative task selection principles. The SG principle is founded on the idea that training with tasks performed with an intermediate range of difficulty would allow more improvement and learning-induced plasticity, compared to training with very difficult or easy tasks [1928] .

Here we report the details of the mapping methods, and show its efficacy in portraying relevant motor impairment patterns for individual subjects. We also briefly demonstrate its utility for individually-tailored selection of practiced movement using the SG principle. However, our evidence for the utility and benefit of the mapping method for individualizing UL robot-mediated rehabilitation after stroke will be reported in subsequent publications.[…]


Continue —> Mapping upper-limb motor performance after stroke – a novel method with utility for individualized motor training | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1Schematic description of the experimental setting (top view). a The participant held the handle of a robotic manipulandum (indicated onscreen by a red disc; not shown), which allowed planar reaching movements from a start position (white onscreen disc (here gray) to a target position (blue onscreen disc; here black) and provided assisting and guiding forces as needed. Hand’s grip was maintained via a special glove and the forearm was supported against gravity (not shown). The participant leaned his/her head against a headrest, maintaining upright seating posture (ensured using a harness). The horizontal display occluded the hand and the manipulandum from vision. The start-to-target axis (y) and its perpendicular axis (x) correspond to the axes of the assisting and guiding forces, respectively, which were provided during the arm movement as needed by the robot. Adapted from Howard et al. (2009). b The reaching workspace used for mapping performance. The locations of the 8 targets, used in the mapping sessions, are indicated by small open circles. An example of the arm posture when the hand located at the 90o target is shown. Participants were tested with 5cm reaches to each target from 8 start locations (indicated, for the example target, by small black dots). The dashed circle indicates the extent of the mapped workspace. The drawing reflects the actual relationship of target and start locations and arm posture, based on a photograph taken with a healthy participant

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[Abstract+References] Cortical and functional responses to an early protocol of sensory re-education of the hand using audio–tactile interaction.

Early sensory re-education techniques are important strategies associated with cortical hand area preservation. The aim of this study was to investigate early cortical responses, sensory function outcomes and disability in patients treated with an early protocol of sensory re-education of the hand using an audio-tactile interaction device with a sensor glove model.

After surgical repair of median and/or ulnar nerves, participants received either early sensory re-education twice a week with the sensor glove during three months or no specific sensory training. Both groups underwent standard rehabilitation. Patients were assessed at one, three and six months after surgery on training-related cortical responses by functional magnetic resonance imaging, sensory thresholds, discriminative touch and disability using the Disabilities of the Arm, Shoulder and Hand patient-reported questionnaire.

At six-months, there were no statistically significant differences in sensory function between groups. During functional magnetic resonance imaging, trained patients presented complex cortical responses to auditory stimulation indicating an effective connectivity between the cortical hand map and associative areas.

Training with the sensor glove model seems to provide some type of early cortical audio-tactile interaction in patients with sensory impairment at the hand after nerve injury. Although no differences were observed between groups related to sensory function and disability at the intermediate phase of peripheral reinnervation, this study suggests that an early sensory intervention by sensory substitution could be an option to enhance the response on cortical reorganization after nerve repair in the hand. Longer follow-up and an adequately powered trial is needed to confirm our findings.

1. Lundborg, G. Sensory relearning and sensory re-education. In: Lundborg G (ed) Nerve injury and repair: regeneration, reconstruction and cortical remodeling. 2nd ed. New York: Elsevier, 2005, pp. 224–234. Google Scholar
2. Navarro, X, Vivo, M, Valero, CA. Neural plasticity after peripheral nerve injury and regeneration. Prog Neurobiol 2007; 82: 163201. Google ScholarCrossrefMedline
3. Lundborg, G, Rosén, B. Hand function after nerve repair. Acta Physiol (Oxf) 2007; 189: 207217. Google ScholarCrossrefMedline
4. Bach-y-Rita, P. Brain plasticity as a basis of sensory substitution. J Neuro Rehab 1987; 1: 6771Google ScholarAbstract
5. Lundborg, G. Sensation and sensorimotor integration in hand function. In: Lundborg G (ed) Nerve injury and repair: regeneration, reconstruction and cortical remodeling. 2nd ed. New York: Elsevier, 2005, pp. 198–244. Google Scholar
6. Lundborg, G, Rosén, B, Lindberg, S. Hearing as a substitution for sensation: a new principle for artificial sensibility. J Hand Surg Am 1999; 24: 209214. Google ScholarCrossref
7. Mendes, RM, Mazzer, N, Barbosa, RI. Model. Acta Ortop Bras 2011; 19: 289292. Google ScholarCrossref
8. Lanzetta, M, Perani, D, Anchisi, D. Early use of artificial sensibility in hand transplantation. Scand J Plast Reconstr Surg Hand Surg 2004; 38: 106111. Google ScholarCrossrefMedline
9. Mendes, RM, Barbosa, RI, Salmón, CEG. Auditory stimuli from a sensor glove model modulate cortical audiotactile integration. Neurosci Lett 2013; 548: 3337. Google ScholarCrossrefMedline
10. Rosén, B, Lundborg, G. Enhanced sensory recovery after median nerve repair using cortical audio-tactile interaction. A randomized multicentre study. J Hand Surg Eur Vol 2007; 32: 3137. Google ScholarLink
11. Pettengill, K, Van Strien, G. Postoperative management of flexor tendon injuries. In: Skirven T, et al. (eds) Rehabilitation of the hand and upper extremity. Philadelphia, 2011, pp. 457–478. Google Scholar
12. ASHT, Clinical assessment recommendations1992Google Scholar
13. Rosén, B, Lundborg, G. A new tactile gnosis instrument in sensibility testing. J Hand Ther 1998; 11: 251257. Google ScholarCrossrefMedline
14. Rosén, B, Lundborg, G. A model instrument for the documentation of outcome after nerve repair. J Hand Surg Am 2000; 25: 535543. Google ScholarCrossrefMedline
15. Beaton, DE, Katz, JN, Fossel, AH. Measuring the whole or the parts? Validity, reliability, and responsiveness of the disabilities of the arm, shoulder and hand outcome measure in different regions of the upper extremity. J Hand Ther 2001; 14: 128146. Google ScholarCrossrefMedline
16. Roebroeck, A, Formisano, E, Goebel, R. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 2005; 25: 230242. Google ScholarCrossrefMedline
17. Waxman, SG. Clinical neuroanatomy. 26th ed. Columbus: McGraw Hill Education, 2010Google Scholar
18. Rosén, B, Lundborg, G. Sensory reeducation. In: Skirven T, et al. (eds) Rehabilitation of the hand and upper extremity. Philadelphia, 2011, pp. 634–645. Google Scholar
19. Lundborg, G, Björkman, A, Hansson, T. Artificial sensibility of the hand based on cortical audiotactile interaction: a study using functional magnetic resonance imaging. Scand J Plast Reconstr Surg Hand Surg 2005; 39: 370372. Google ScholarCrossrefMedline
20. Björkman, A, Waites, A, Rosén, B. Cortical sensory and motor response in a patient whose hand has been replanted: one-year follow up with functional magnetic resonance imaging. Scand J Plast Reconstr Surg Hand Surg 2007; 41: 7076. Google ScholarCrossrefMedline
21. Walbruch, B, Kalliainen, L. The optimization of peripheral nerve recovery using cortical reorganization techniques: a retrospective study of wrist level nerve repairs. J Hand Ther 2015; 28: 341346. Google ScholarCrossrefMedline
22. Jaquet, J-B, Luijsterburg, AJM, Kalmijn, S. Median, ulnar, and combined median-ulnar nerve injuries: functional outcome and return to productivity. J Trauma 2001; 51: 687692. Google ScholarCrossrefMedline
23. Fonseca, MCR, Mazzer, N, Barbieri, CH. Hand injuries: a retrospective study. Rev Bras Ortop 2006; 41: 181186. Google Scholar
24. Trybus, M, Lorkowski, J, Brongel, L. Causes and consequences of hand injuries. Am J Surg 2006; 192: 5257. Google ScholarCrossrefMedline
25. Saadat, S, Eslami, V, Rahimi-Movaghar, V. The incidence of peripheral nerve injury in trauma patients in Iran. Ulus Travma Acil Cerrahi Derg 2011; 17: 539544. Google ScholarCrossrefMedline
26. Gupta, A, Gupta, AK, Uppal, SK Demographic profile of hand injuries in an industrial town of north India: a review of 436 patients. Indian J Surg 2013; 75: 454461Google ScholarCrossrefMedline
27. Rosén, B, Lundborg, G. A new model instrument for outcome after nerve repair. Hand Clin 2003; 19: 463470. Google ScholarCrossrefMedline
28. Vordemvenne, T, Langer, M, Ochman, S. Long-term results after primary microsurgical repair of ulnar and median nerve injuries. A comparison of common score systems. Clin Neurol Neurosurg 2007; 109: 263271. Google ScholarCrossrefMedline
29. Svens, B, Rosén, B. Early sensory re-learning after median nerve repair using mirror training and sense substitution. Hand Ther 2009; 14: 7582. Google ScholarLink
30. Lundborg, G. Brain plasticity and hand surgery: an overview. J Hand Surg Br 2000; 25B: 242252. Google ScholarLink
31. Rosén, B, Balkenius, C, Lundborg, G. Sensory re-education today and tomorrow: a review of evolving concepts. Hand Ther 2003; 8: 4856. Google ScholarLink
32. Paula, MH, Barbosa, RI, Marcolino, AM. Early sensory re-education of the hand after peripheral nerve repair based on mirror therapy: a randomized controlled trial. Braz J Phys Ther 2016; 20: 5865. Google ScholarCrossrefMedline
33. Oud, T, Beelen, A, Eijffinger, E. Sensory re-education after nerve injury of the upper limb: a systematic review. Clin Rehabil 2007; 21: 483494. Google ScholarLink
34. Miller, L, Chester, R, Jerosch-Herold, C. Effects of sensory re-education programs on functional hand sensibility after median and ulnar nerve repair: a systematic review. J Hand Ther 2012; 25: 297307. Google ScholarCrossrefMedline

via Cortical and functional responses to an early protocol of sensory re-education of the hand using audio–tactile interactionHand Therapy – Raquel Metzker Mendes, Carlo Rondinoni, Marisa de Cássia Registro Fonseca, Rafael Inácio Barbosa, Carlos Ernesto Garrido Salmón, Cláudio Henrique Barbieri, Nilton Mazzer, 2017

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[BOOK Chapter] Biomechanics of the Upper Limb – Google Books

The BOOK —> Atlas of Orthoses and Assistive Devices E-Book – Joseph Webster, Douglas Murphy – Google Books

 Go to Chapter 11: Biomechanics of the Upper Limb


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[Abstract+References] Finite element analysis of the wrist in stroke patients: the effects of hand grip.


The provision of the most suitable rehabilitation treatment for stroke patient remains an ongoing challenge for clinicians. Fully understanding the pathomechanics of the upper limb will allow doctors to assist patients with physiotherapy treatment that will aid in full arm recovery. A biomechanical study was therefore conducted using the finite element (FE) method. A three-dimensional (3D) model of the human wrist was reconstructed using computed tomography (CT)-scanned images. A stroke model was constructed based on pathological problems, i.e. bone density reductions, cartilage wane, and spasticity. The cartilages were reconstructed as per the articulation shapes in the joint, while the ligaments were modelled using linear links. The hand grip condition was mimicked, and the resulting biomechanical characteristics of the stroke and healthy models were compared. Due to the lower thickness of the cartilages, the stroke model reported a higher contact pressure (305 MPa), specifically at the MC1-trapezium. Contrarily, a healthy model reported a contact pressure of 228 MPa. In the context of wrist extension and displacement, the stroke model (0.68° and 5.54 mm, respectively) reported a lower magnitude than the healthy model (0.98° and 9.43 mm, respectively), which agrees with previously reported works. It was therefore concluded that clinicians should take extra care in rehabilitation treatment of wrist movement in order to prevent the occurrence of other complications.

Graphical abstract


  1. 1.
    Ada L, O’Dwyer N, O’Neill E (2006) Relation between spasticity, weakness and contracture of the elbow flexors and upper limb activity after stroke: an observational study. Disabil Rehabil 28(13-14):891–897. Scholar
  2. 2.
    Aprile I., Rabuffetti M, Padua L, DI Sipio E, Simbolotti C, Ferrarin M (2014) Kinematic analysis of the upper limb motor strategies in stroke patients as a tool towards advanced neurorehabilitation strategies: a preliminary study. Biomed Res IntGoogle Scholar
  3. 3.
    Arya KN, Pandian S, Verma R, Garg RK (2011) Movement therapy induced neural reorganization and motor recovery in stroke: a review. J Bodyw Mov Ther 15(4):528–537. Scholar
  4. 4.
    Bajuri MN, Abdul Kadir MR, Raman MM, Kamarul T (2012) Mechanical and functional assessment of the wrist affected by rheumatoid arthritis: a finite element analysis. Med Eng Phys 34(9):1294–1302. Scholar
  5. 5.
    Bajuri MN, Abdul Kadir MR, Yahya MY (2011) Biomechanical analysis on the effect of bone graft of the wrist after arhroplasty. IFMBE Proc 35:773–777. Scholar
  6. 6.
    Beebe JA, Lang CE (2009) Active range of motion predicts upper extremity function 3 months after stroke. Stroke 40(5):1772–1779. Scholar
  7. 7.
    Bettinger PC, Smutz WP, Linscheid RL, Cooney WP, An KN (2000) Material properties of the trapezial and trapeziometacarpal ligaments. J Hand Surg 25(6):1085–1095. Scholar
  8. 8.
    Boissy P, Bournonnais D, Carlotti MM, Gravel D, Arsenault BA (1999) Maximal grip force in chronic stroke subjects and its relationship to global upper extremity function. Clin Rehabil 13(4):354–362. Scholar
  9. 9.
    Bosisio MR, Talmant M, Skalli W, Laugier P, Mitton D (2007) Apparent Young’s modulus of human radius using inverse finite element method. J Biomech 9:2022–2028CrossRefGoogle Scholar
  10. 10.
    Brown CP, Nguyen TC, Moody HR, Crawford RW, Oloyede A (2009) Assessment of common hyperelastic constitutive equations for describing normal and osteoarthritis articular cartilage. Proc Inst Mech Eng H J Eng Med 6:643–652CrossRefGoogle Scholar
  11. 11.
    Buma F, Kwakkel G, Ramsey N (2013) Understanding upper limb recovery after stroke. Restor Neurol Neurosci 31:707–722PubMedGoogle Scholar
  12. 12.
    Carrigan SD, Whiteside RA, Pichora DR, Small CF (2003) Development of a three-dimensional finite element model for carpal load transmission in a static neutral posture. Ann Biomed Eng 31(6):718–725. Scholar
  13. 13.
    Chen X, Zhou L, Zhang Y, Yi D, Liu L, Rao W, Wu Y, Ma D, Liu X, Zhou X-HA, Lin H, Cheng D, Yi D (2014) Risk factors of stroke in Western and Asian countries: a systematic review and meta-analysis of prospective cohort studies. BMC Public Health 14:1–13CrossRefGoogle Scholar
  14. 14.
    Cheng H-YK, Lin C-L, Lin Y-H, Chen AC (2007) Biomechanical evaluation of the modified double-plating fixation for the distal radius fracture. Clin Biomech 22(5):510–517. Scholar
  15. 15.
    Chuang L-L, Wu C-Y, Lin K-C, Lur S-Y (2012) Quantitative mechanical properties of the relaxed biceps and triceps brachii muscles in patients with subacute stroke: a reliability study of the myoton-3 myometer. Stroke Res Treat 2012:1–7. Scholar
  16. 16.
    Coburn JC, Upal MA, Crisco JJ (2007) Coordinate systems for the carpal bones of the wrist. J Biomech 40(1):203–209. Scholar
  17. 17.
    Dennis MS, Lo KM, McDowall M, West T (2002) Fractures after stroke. Frequency, types and associations. Stroke 33(3):728–734. Scholar
  18. 18.
    Dietz V, Berger W (1984) Interlimb coordination of posture in patients with spastic hemiparesis. Brain 107(3):965–978. Scholar
  19. 19.
    Ezquerro F, Jimenez S, Perez A, Prado M, de Diego G, Simon A (2007) The influence of wire positioning upon the initial stability of scaphoid fractures fixed using Kirschner wires: a finite element study. Med Eng Phys 29(6):652–660. Scholar
  20. 20.
    Finlay JB, Repo RU (1979) Energy absorbing ability of articular cartilage during impact. Med Biol Eng Comput 17(3):397–403. Scholar
  21. 21.
    Fischli S, Sellens RW, Beek M, Pichora DR (2009) Simulation of extension, radial and ulnar deviation of the wrist with a rigid body spring model. J Biomech 224:477–485Google Scholar
  22. 22.
    Gislason MK, Nash DH, Nicol A, Kanellopoulus A, Bransby-Zachary M, Hems T, Condon B, Stansfield B (2009) A three dimensional finite element model of maximal grip loading in the human wrist. Proc Inst Mech Eng H J Eng Med 7:849–861CrossRefGoogle Scholar
  23. 23.
    Gislason MK, Nash DH, Stansfield B (2010) Finite element model creation and stability considerations of complex biological articulation. Med Eng Phys 32(5):523–531. Scholar
  24. 24.
    Givissis PK, Antonarakos P, Vafiades VE, Christodoulus AG (2009) Management of posstraumatic arthritis of the wrist with radiolunate fusion enhanced with a sliding autograft: a case report and description of a novel technique. Tech Hand Upper Extrem Surg 13(2):90–93. Scholar
  25. 25.
    Gopura RARC, Bandara DSV, Kiguchi K, Mann GKI (2016) Developments in hardware systems of active upper-limb exoskeleton robots: a review. Robot Auton Syst 75:203–220. Scholar
  26. 26.
    Guo S, Zhang F, Wei W, Guo J, Ge W (2013) Development of force analysis-based exoskeleton for the upper limb rehabilitation system. Proc IEEE:285–289Google Scholar
  27. 27.
    Guo X, Fan Y, Li Z-M (2009) Effects of dividing the transverse carpal ligament on the mechanical behavior of the carpal bones under axial compressive load: a finite element study. Med Eng Phys 2:188–194CrossRefGoogle Scholar
  28. 28.
    Heller A, Wade DT, Wood VA, Sunderland A, Hewer RL, Ward E (1987) Arm function after stroke: measurement and recovery over the first three months. J Neurol Neurosurg Psychiatry 50(6):714–719. Scholar
  29. 29.
    James CB, Uhl TL (2001) A review of articular cartilage pathology and the use of glucosamine sulfate. J Athl Train 36(4):413–419PubMedPubMedCentralGoogle Scholar
  30. 30.
    Jorgensen L, Engstad T, Jacobsen BK (2001) Bone mineral density in acute stroke patients. Stroke 32(1):47–51. Scholar
  31. 31.
    Kamper DG, Schmit BD, Rymer WZ (2001) Effect of muscle biomechanics on the quantification of spasticity. Ann Biomed Eng 29(12):1122–1134. Scholar
  32. 32.
    Kerin AJ, Wisnom MR, Adams MA (1998) The compressive strength of articular cartilage. Proc Inst Mech Eng H J Eng Med 4:273–280CrossRefGoogle Scholar
  33. 33.
    Kim K, Park D-S, Ko B-Y, Lee J, Yang S-N, Kim J, Song W-K (2011) Arm motion analysis of stroke patients in activities of daily living tasks: a preliminary study. IEEE. Exp Dermatol:1287–1291Google Scholar
  34. 34.
    Krishna KR, Sridhar I, Ghista DN (2008) Analysis of the helical plate for bone fracture fixation. Injury 39(12):1421–1436. Scholar
  35. 35.
    Kumar TS, Pandyan AD, Sharma AK (2006) Biomechanical measurement of post-stroke spasticity. Age Ageing 35(4):371–375. Scholar
  36. 36.
    Lang CE, Beebe JA (2007) Relating movement control at 9 upper extremity segments to loss of hand function in people with chronic hemiparesis. Neurorehabil Neural Repair 21(3):279–291. Scholar
  37. 37.
    Lazoura O, Groumas N, Antoniadou E, Papadaki PJ, Papadimitriou A, Thriskos P, Fezoulidis I, Vlychou M (2008) Bone mineral density alterations in upper and lower extremities 12 months after stroke measured by peripheral quantitative computed tomography and DXA. J Clin Densitom Assess Skeletal Health 11(4):511–517. Scholar
  38. 38.
    Li C, Zhou Y, Wang H, Liu J, Xiang L (2014) Treatment of unstable thoracolumbar fractures through short segment pedicle screw fixation techniques using pedicle fixation at the level of the fracture: a finite element analysis. PLoS One 9(6):e99156. Scholar
  39. 39.
    Li Z, Kim JE, Davidson JS, Etheridge BS, Alonso JE, Eberhardt AW (2007) Biomechanical response of the pubic symphysis in lateral pelvic impacts: a finite element study. J Biomech 12:2758–2766CrossRefGoogle Scholar
  40. 40.
    Lo HS, Xie SQ (2012) Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Med Eng Phys 34(3):261–268. Scholar
  41. 41.
    Lum PS, Burgar CG, Kenney DE, Van der Loos HFM (1999) Quantification of force abnormalities during passive and active-assisted upper-limb reaching movements in post-stroke hemiparesis. IEEE Trans Biomed Eng 46(6):652–662. Scholar
  42. 42.
    M.H. R, Abdul Kadir MR, Murali MR, Kamarul T (2014) Finite element analysis of three commonly used external fixation devices for treating type III pilon fractures. Med Eng Phys 36(10):1322–1330. Scholar
  43. 43.
    Macleod N.A., Nash DH, Stansfield BW, Bransby-Zachary M, Hems T (2007) Cadaveric analysis of the wrist and forearm load distribution for finite element validation. In: Sixth International Hand and Wrist Biomechanics Symposium, Tainan, Taiwan, Republic of ChinaGoogle Scholar
  44. 44.
    Magermans DJ, Chadwick EKJ, Veeger HEJ, van der Helm FCT (2005) Requirement for upper extremity motions during activities of daily living. Clin Biomech 20(6):591–599. Scholar
  45. 45.
    Materialise (2008) Mimics help manual, vol Version 12.1. MaterialiseGoogle Scholar
  46. 46.
    McGrouther DA. Interactive Hand-Anatomy CD. 1.0 edn. Prima PicturesGoogle Scholar
  47. 47.
    Mirbagheri MM, Settle K, Harvey R, Rymer WZ (2007) Neuromuscular abnormalities associated with spasticity of upper extremity muscles in hemiparetic stroke. J Neurophysiol 98(2):629–637. Scholar
  48. 48.
    Mirbagheri MM, Tsao C, Rymer WZ (2009) Natural history of neuromuscular properties after stroke: a longitudinal study. J Neurol Neurosurg Psychiatry 80(11):1212–1217. Scholar
  49. 49.
    Mirbagheri MM, Tsao C, Settle K, Lilaonitkul T, Rymer WZ (2008) Time course of changes in neuromuscular properties following stroke. Conf Proc IEEE Eng Med Biol Soc 1:5097–5100Google Scholar
  50. 50.
    Nascimento LR, Polese JC, Faria CDCM, Teixeira-Salmela LF (2012) Isometric hand grip strength correlated with isokinetic data of the shoulder stabilizers in individuals with chronic stroke. J Bodyw Mov Ther 16(3):275–280. Scholar
  51. 51.
    Netter FH (2003) Atlas of human anatomy, 3rd edn. ICON Learning System, New York CityGoogle Scholar
  52. 52.
    Olney SJ, Richards C (1996) Hemiparetic gait following stroke. Part I. Charact Gait Posture 4:136–148CrossRefGoogle Scholar
  53. 53.
    Opheim A, Danielsson A, Murphy MA, Persson HC, Sunnerhagen KS (2014) Upper-limb spasticity during the first year after stroke. Am J Phys Med Rehabil 93(10):884–896. Scholar
  54. 54.
    Patterson RM, Viegas SF, Elder K, Buford WL (1995) Quantification of anatomic, geometric, and load transfer characteristics of the wrist joint. Semin Arthroplast 6:13–19Google Scholar
  55. 55.
    Poli P, Morone G, Rosati G, Masiero S (2013) Robotic technologies and rehabilitation: new tools for stroke patient’s therapy. Biomed Res Int 2013:1–8. Scholar
  56. 56.
    Powers RK, Marder-Meyer J, Rymer WZ (1988) Quantitative relations between hypertonia and strecth reflex threshold in spastic hemiparesis. Ann Neurol 23(2):115–124. Scholar
  57. 57.
    Ramlee MH, Abdul Kadir MR, Harun H (2014) Three-dimensional modelling and finite element analysis of an ankle external fixator. Adv Mater Res 845:183–188CrossRefGoogle Scholar
  58. 58.
    Ramlee MH, Abdul Kadir MR, Murali MR, Kamarul T (2014) Biomechanical evaluation of two commonly used external fixators in the treatment of open subtalar dislocation—a finite element analysis. Med Eng Phys 36(10):1358–1366. Scholar
  59. 59.
    Ramlee MH, Beng GK (2017) Function and biomechanics of upper limb in post-stroke patients—a systematic review. J Mech Med Biol 0(06):1750099. Scholar
  60. 60.
    Sato Y (2000) Abnormal bone and calcium metabolism in patients after stroke. Arch Phys Med Rehabil 81(1):117–121. Scholar
  61. 61.
    Sato Y, Fujimatsu Y, Kikuyama M, Kaji M, Oizumi K (1998) Influence of immobilization on bone mass and bone metabolism in hemiplegic elderly patients with a ling-standing stroke. J Neurol Sci 156(2):205–210. Scholar
  62. 62.
    Savelberg HH, Kooloos JG, Huiskes R, Kauer JM (1992) Stiffness of the ligaments of the human wrist joints. J Biomech 25(4):369–376. Scholar
  63. 63.
    Schuind F, Cooney WP, Linscheid RL, An KN, Chao EY (1995) Force and pressure transmission through the normal wrist. A theoretical two-dimensional study in the posteroanterior plane. J Biomech 28(5):587–601. Scholar
  64. 64.
    Starsky AJ, Sangani SG, McGuire JR, Logan B, Schmit BD (2005) Reliability of biomechanical spasticity measurements at the elbow of people poststroke. Arch Phys Med Rehabil 86(8):1648–1654. Scholar
  65. 65.
    Sunderland A, Tinson D, Bradley L, Langton H (1989) Arm function after stroke. An evaluation of grip strength as a measure of recovery and a prognostic indicator. J Neurol Neurosurg Psychiatry 52(11):1267–1272. Scholar
  66. 66.
    Tao K, Wang D, Wang C, Wang X, Liu A, Nester CJ, Howard D (2009) An in vivo experimental validation of a computational model of human foot. J Bionic Eng 6(4):387–397. Scholar
  67. 67.
    Tunc H, Oken O, Kara M, Tiftik T, Dogu B, Unlu Z, Ozcakar L (2012) Ultrasonographic measurement of the femoral cartilage thickness in hemiparetic patients after stroke. Int J Rehabil Res 35(3):203–207. Scholar
  68. 68.
    Warlow C, van Gijn J, Dennis M, Wardlaw J, Bamford J, Hankey G, Sandercock P, Rinkel G, Langhorne P, Sudlow C, Rothwell P (2008) Stroke: practical management. Blackwell Publishing, Oxford. Scholar
  69. 69.
    Wasay W, Khatri IA, Kaul S (2014) Stroke in South Asian countries. Nat Rev Neurol 10(3):135–143. Scholar
  70. 70.
    Wei W, Guo S, Zhang F, Guo J, Ji Y, Wang Y (2013) A novel upper limb rehabilitation system with hand exoskeleton mechanism. Proc IEEE:285–290Google Scholar
  71. 71.
    Welmer AK, Widen HL, Sommerfeld DK (2010) Location and severity of spasticity in the first 1-2 weeks and at 3 and 18 months after stroke. Eur J Neurol 17(5):720–725. Scholar
  72. 72.
    Wong DW-C, Niu W, Zhang M (2016) Finite element analysis of foot and ankle impact injury: risk evaluation of calcaneus and talus fracture. PLoS One 11(4):e0154435. Scholar
  73. 73.
    Yalcin S, Kara M, Ozturk GT, Ozcakar L (2016) Ultrasonographic measurements of the metacarpal and talar cartilage thicknesses in hemiplegic patients after stroke. Top Stroke Rehabil 9:1–4Google Scholar

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