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[Abstract + References] Monitoring System for Home-Based Hand Rehabilitation – IEEE Conference Publication


The paper proposes a solution for monitoring of cardiovascular parameters during home-based hand rehabilitation. The most important cause of long-term disability in Europe is cerebral vascular accident (CVA) or stroke. The effects of stroke can vanish after a short period or can remain for the rest of the life depending on therapeutic program. The system developed for this study is not only therapeutically devices that allow the movement of hand for physical exercises controlled by electromyography (EMG) but also record one or more biomedical parameters such as: electromyogram (EMG), electrocardiogram (ECG), pulse wave, heart rate (HR), temperature, respiration rate, non-invasive blood pressure (NIBP) or oxygen concentration in the blood (SpO2). These physiological parameters are selected according to the physician’s prescription and the patient needs. In this paper it is presented an application that refers to the hand rehabilitation of post-stroke. It was observed the cardiovascular system status, analyzing the heart rate variability. During therapeutic procedure it was recorded ECG (1 lead) and pulse wave (using an ear lobe sensor). After that HRV was calculated for each signal. The results were used to determine the stress level induced by the rehabilitation program.
1. I.I. Costache, E. Miftode, O. Petriş, A.D. Popa, D. Iliescu, E.G. Botnariu, “Associations between Area of residence and Cardiovascular risk”, Revista de cercetare şi intervenţie socială, vol. 49, pp. 68-79, May 2015.

2. V.L. Roger, A.S. Go, D.M. Lloyd-Jones, E.J. Benjamin, J.D. Berry, W.B. Borden, D.M. Bravata, S. Dai, E.S. Ford, C.S. Fox, H.J. Fullerton, C. Gillespie, S.M. Hailpern, J.A. Heit, V.J. Howard, B.M. Kissela, S.J. Kittner, D.T. Lackland, J.H. Lichtman, L.D. Lisabeth, D.M. Makuc, G.M. Marcus, A. Marelli, D.B. Matchar, C.S. Moy, D. Mozaffarian, M.E. Mussolino, G. Nichol, N.P. Paynter, E.Z. Soliman et al., “Heart disease and stroke statistics–2012 update: a report from the American Heart Association”, Circulation, vol. 125, pp. e2-e220, 2012.

3. P.U. Heuschmann, A. Di Carlo, Y. Bejot, D. Rastenyte, D. Ryglewicz, C. Sarti, M. Torrent, C.D. Wolfe, “Incidence of stroke in Europe at the beginning of the 21st century”, Stroke, vol. 40, pp. 1557-1563, May 2009.

4. I.I. Costache, E. Miftode, O. Mitu, V. Aursulesei, “Sex differences in cardiovascular risk factors in a rural community from north Romania region”, Revista de cercetare şi intervenţie socială, vol. 55, pp. 204-214, 2016.

5. E. Stevens, C. McKevitt, E. Emmett, C. Wolfe, Y. Wang, “The Burden of Stroke in Europe”, report for Stroke Alliance for Europe, 2017.

6. J. Chen, D. Nichols, E.B. Brokaw, P.S. Lum, “Home-Based Therapy After Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME)”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 12, pp. 2305-2312, 2017.

7. M. Ciorap, M. Munteanu, D. Andritoi, R. Ciorap, “Low Cost Device for “at Home” Rehabilitation After a Stroke Event”, International conference KNOWLEDGE-BASED ORGANIZATION, vol. 24, pp. 26-31, 2018, [online] Available:

8. A. Basteris, S.M. Nijenhuis, A.HA. Stienen, J.H. Buurke, G.B Prange, F. Amirabdollahian, “Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review”, Journal of NeuroEngineering and Rehabilitation, vol. 11, no. 111, 2014.

9. S.M. Hunter, H. Johansen-Berg, N. Ward, N.C. Kennedy, E. Chandler, C.J. Weir, J. Rothwell, A.M. Wing, M.J. Grey, G. Barton, N.M. Leavey, C. Havis, R.N. Lemon, J. Burridge, A. Dymond, V.M. Pomeroy, “Functional Strength Training and Movement Performance Therapy for Upper Limb Recovery Early Poststroke-Efficacy Neural Correlates Predictive Markers and Cost-Effectiveness: FAST-INdiCATE Trial”, FRONTIERS IN NEUROLOGY, vol. 8, 2018.

10. A. Pollock, B. St George, M. Fenton, L. Firkins, “Top ten research priorities relating to life after stroke”, Lancet Neurology, vol. 11, no. 3, pp. 209, 2012.

11. M.T. Schultheis, A.A. Rizzo, “The application of virtual reality technology in rehabilitation”, Rehabil Psychol, vol. 46, no. 3, pp. 296-311, 2001.

12. H. Sveistrup, “Motor rehabilitation using virtual reality”, Journal of Neuro Engineering and Rehabilitation, vol. 1, no. 10, 2004.

13. R. Ciorap, D. Arotariţei, F. Topoliceanu, R. Lupu, C. Corciovă, M. Ungureanu, “E-health application for home monitoring of neuromuscular rehabilitation”, [Aplicaţie e-Health pentru monitorizarea la domiciliu a recuperării neuro-musculare] Revista Medico-Chirurgicală a Societăţii de Medici şi Naturalişti din Iaşi, vol. 109, no. 2, pp. 440-444, 2005.

14. F. Wittmann, J.P. Held, O. Lambercy, M.L. Starkey, A. Curt, R. Hover, R. Gassert, A.R. Luft, R.R. Gonzenbach, “Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system”, Journal of Neuroengineering and Rehabilitation, vol. 13, 2016.

15. F. Muri, C. Carbajal, A.M. Echenique, H. Fernandez, M. Lopez, “Virtual reality upper limb model controlled by EMG signals”, Journal of Physics Conference Series 477 19th Argentinean Bioengineering Society Congress (SABI 2013).

16. R. Ciorap, C. Hritcu-Luca, C. Corciova, A. Stan, D. Zaharia, “Home Monitoring Device for Cardiovascular Diseases”, International Conference on Advancements of Medicine and Health Care through Technology, pp. 49-52, 23-26 Septembrie, 2009.

17. A.J. Meyer, C. Patten, B.J. Fregly, “Lower extremity EMG-driven modeling of walking with automated adjustment of musculoskeletal geometry”, PLOS ONE, vol. 12, no. 7, 2017.

18. R. Ciorap, D. Andritoi, V. Pomazan, L. Petcu, F. Ungureanu, D. Zaharia, “E-health system for monitoring of chronic diseases”, World Congress on Medical Physics and Biomedical Engineering, vol. 25, no. 5, pp. 259-262, 7 – 12 September 2009.

19. V. David, A. Salceanu, R. Ciorap, “Acquisition and Analysis of Biomedical Signals in Case of Peoples Exposed to Electromagnetic Fields” in Pervasive and Mobile Sensing and Computing for Healthcare Subhas Chandra Mukhopadhyay and O. A. Postolache, Springer, pp. 269-295, 2012.

20. V.M. Pomazan, L.C. Petcu, S.R. Sintea, R. Ciorap, “Active Data Transportation and Processing for Chronic Diseases Remote Monitoring”, International Conference on Signal Processing Systems (ICSPS 2009), pp. 853-857, 15-17 May, 2009.

21. R. Ciorap, C. Corciova, M. Ciorap, D. Zaharia, “Optimization of the Treatment for Chronic Disease Using an e-Health System”, 7th International Symposium on ADVANCED TOPICS IN ELECTRICAL ENGINEERING 2011 Bucureşti, pp. 143-146, 12-14 Mai, 2011.

22. D. Andriţoi, V. David, R. Ciorap, “An Portable Device for ECG and Photoplethysmographic Signal Acquisition”, 2014 International Conference and Exposition on Electrical and Power Engineering (EPE2014), 16-18 October 2014.

23. M. Ciorap, M. Munteanu, D. Andritoi, R. Ciorap, “Low Cost Device for at Home Rehabilitation After a Stroke Event”, International conference KNOWLEDGE-BASED ORGANIZATION, vol. 24, no. 3, pp. 26-31, [online] Available:

24. I.I. Costache, M.C. Ungureanu, D. Iliescu, A. Petriş, G. Botnariu, “Electrocardiographic changes in the most frequent endocrine disorders associated with cardiovascular diseases. Review of the literature”, Revista Medico-Chirurgicală a Societăţii de Medici şi Naturalişti din Iaşi, vol. 119, no. 1, pp. 9-13, 2015.

25. I.I. Costache, R. Al Namat, F. Mitu, M. Ciocoiu, V. Aursulesei, O. Mitu, A.D. Costache, D. Marcu, A.M. Buburuz, “The Prognostic Value of Left Bundle Branch Block and Biochemical Parameters in Alcoholic Dilated Cardiomyopathy”, REV. CHIM., vol. 68, no. 12, pp. 2967-2969, 2017.

26. D. Andriţoi, C. Corciovă, C. Luca, D. Matei, R. Ciorap, “Heart Rate dynamics study on the impact of “Mirror therapy” in patients with stroke”, International Conference Advancements of Medicine and Health Care Through Technology MEDITECH 2016, 12th – 15th October 2016.

27. D. Andritoi, V. David, R. Ciorap, M. Branzila, “Recording and processing electrocardiography signals during magneto therapy procedures”, Environmental Engineering and Management Journal, vol. 12, no. 6, pp. 1231-1238, 2013.


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[ARTICLE] Muscle fatigue assessment during robot-mediated movements – Full Text



Several neuromuscular disorders present muscle fatigue as a typical symptom. Therefore, a reliable method of fatigue assessment may be crucial for understanding how specific disease features evolve over time and for developing effective rehabilitation strategies. Unfortunately, despite its importance, a standardized, reliable and objective method for fatigue measurement is lacking in clinical practice and this work investigates a practical solution.


40 healthy young adults performed a haptic reaching task, while holding a robotic manipulandum. Subjects were required to perform wrist flexion and extension movements in a resistive visco-elastic force field, as many times as possible, until the measured muscles (mainly flexor and extensor carpi radialis) exhibited signs of fatigue. In order to analyze the behavior and the characteristics of the two muscles, subjects were divided into two groups: in the first group, the resistive force was applied by the robot only during flexion movements, whereas, in the second group, the force was applied only during extension movements. Surface electromyographic signals (sEMG) of both flexor and extensor carpi radialis were acquired. A novel indicator to define the Onset of Fatigue (OF) was proposed and evaluated from the Mean Frequency of the sEMG signal. Furthermore, as measure of the subjects’ effort throughout the task, the energy consumption was estimated.


From the beginning to the end of the task, as expected, all the subjects showed a decrement in Mean Frequency of the muscle involved in movements resisting the force. For the OF indicator, subjects were consistent in terms of timing of fatigue; moreover, extensor and flexor muscles presented similar OF times. The metabolic analysis showed a very low level of energy consumption and, from the behavioral point of view, the test was well tolerated by the subjects.


The robot-aided assessment test proposed in this study, proved to be an easy to administer, fast and reliable method for objectively measuring muscular fatigue in a healthy population. This work developed a framework for an evaluation that can be deployed in a clinical practice with patients presenting neuromuscular disorders. Considering the low metabolic demand, the requested effort would likely be well tolerated by clinical populations.


Muscle fatigue has been defined as “the failure to maintain a required or expected force” [1] and it is a complex phenomenon experienced in everyday life that has reached great interest in the areas of sports, medicine and ergonomics [2]. Muscle fatigue can affect task performance, posture-movement coordination [3], position sense [4] and it can be a highly debilitating symptom in several pathologies [5]. For many patients with neuromuscular impairments, taking into account muscle fatigue is of crucial importance in the design of correct rehabilitation protocols [6] and fatigue assessment can provide crucial information about skeletal muscle function. Specifically, several neuromuscular diseases (e.g. Duchenne, Becker Muscular Dystrophies, and spinal muscular atrophy) present muscle fatigue as a typical symptom [7], and fatigue itself accounts for a significant portion of the disease burden. A systematic approach to assess muscle fatigue might provide important cues on the disability itself, on its progression and on the efficacy of adopted therapies. In particular, therapeutic strategies are now under deep investigation and a lot of effort has been devoted to accelerate the development of drugs targeting these disorders [8]. Therefore, the need for an objective tool to measure muscle fatigue is impelling and of great relevance.

Currently, in clinical practice muscle fatigue is evaluated by means of qualitative rating scales like the 6-min walk test (6MWT) [9] or through subjective questionnaires administered to the patient (e.g. the Multidimensional Fatigue Inventory (MFI), the Fatigue Severity Scale (FSS), and the Visual Analog Scale (VAS)) [10]. During the 6MWT patients have to walk, as fast as possible, along a 25 meters linear course and repeat it as often as they can for 6 min: ‘fatigue’ is then defined as the difference between the distance covered in the sixth minute compared to the first. Obviously, such a measure is only applicable to ambulant patients and this is a strong limitation to clinical investigation because a patient may lose ambulatory ability during a clinical trial, resulting in lost ability to perform the primary clinical endpoint [11]. It should also be considered that neuromuscular patients, e.g. subjects with Duchenne Muscular Dystrophy, generally lose ambulation before 15 years of age [12], excluding a large part of the population from the measurement of fatigue through the 6MWT. Since neuromuscular patients often experience a progressive weakness also in the upper limb, reporting of muscle fatigue in this region is common. A fatigue assessment for upper limb muscles could be used to monitor patients across different stages of the disease. As for the questionnaires, the MFI is a 20 items scale designed to evaluate five dimensions of fatigue (general fatigue, physical fatigue, reduced motivation, reduced activity, and mental fatigue) [13]. Similarly, the FSS questionnaire contains nine statements that rate the severity of fatigue symptoms and the patient has to agree or disagree with them [14]. The VAS is even more general: the patient has to indicate on a 10 cm line ranging from “no fatigue” to “severe fatigue” the point that best describes his/her level of fatigue [15]. Despite the ease to administer, such subjective assessments of fatigue may not correlate with the actual severity or characteristics of fatigue, and may provide just qualitative information with low resolution, reliability and objectivity. Considering various levels of efficacy among the methods currently used in clinical practice, research should focus on the development of an assessment tool for muscle fatigue, that is easy and fast to administer, even to patients with a high level of impairment. Such a tool, should provide clear results, be easy to read and understand by a clinician, be reliable and objectively correlated with the physiology of the phenomenon.

In general, muscle fatigue can manifest from either central and/or peripheral mechanisms. Under controlled conditions, surface electromyography (sEMG) is a non-invasive and widely used technique to evaluate muscle fatigue [16]. Certain characteristics of the sEMG signal can be indicators of muscle fatigue. For example during sub-maximal tasks, muscle fatigue will present with decreases in muscle fiber conduction velocity and frequency and increases in amplitude of the sEMG signal [16]. The trend and rate of change will depend on the intensity of the task: generally, sEMG amplitude has been observed to increase during sub-maximal efforts and decrease during maximal efforts; further it has been reported that there is a significantly greater decline in the frequency content of the signal during maximal efforts compared to sub-maximal [17]. Accordingly, spectral (i.e. mean frequency) and amplitude parameters (i.e. Root Mean Square (RMS)) of the signals, can be used to measure muscle fatigue as extensively discussed in many widely acknowledged studies [161819], however, context of contraction type and intensity must be specified for proper interpretation. A significant problem with the majority of existing protocols is that they rely on quantifying maximal voluntary force loss, maximum voluntary muscle contraction (MVC) [182021] or high fatiguing dynamic tasks [1922] that cannot be reliably performed in clinical practice, especially in the case of pediatric subjects. Actually, previous works pointed out that not only the capacity to maintain MVC can be limited by a lack of cooperation [2324], but also, that sustaining a maximal force in isometric conditions longer than 30 s reduces subject’s motivation leading to unreliable results [25]. Besides, neuromuscular patients might have a high level of impairment and low residual muscular function thus making even more difficult, as well as dangerous for their muscles, sustaining high levels of effort or the execution of a true MVC. In order to overcome this issue, maximal muscle contractions can be elicited by magnetic [10] or electrical stimulation [26]. Although such procedures allow to bypass the problem mentioned above, these involve involuntary muscle activation and not physiological recruitment of motor units [24]; moreover, they can be uncomfortable for patients and can require advanced training, which makes them difficult to be included in clinical fatigue assessment protocols. As for the above mentioned problem with children motivation, work by Naughton et al. [27] showed that the test-retest coefficient of variation of fatigue index during a Wing-Gate test, significantly decreased when using a computerized feedback game linked to pedal cadence, suggesting that game-based procedures may ensure more consistent results in children assessment.

In recent years, the assessment of sensorimotor function has been deepened thanks to the introduction of innovative protocols administered through robotic devices [28293031]. These methods have the ambition to add meaningful information to the existing clinical scales and can be exploited as a basis for the implementation of a muscle fatigue assessment protocol. In order to fill the gap between the need of a quantitative clinical measurement protocol of muscle fatigue and the lack of an objective method which does not demand a high level of muscle activity, we propose a new method based on a robotic test, which is fast and easy to administer. Further, we decided to address the analysis of muscle fatigue on the upper limb as to provide a test suitable to assess patients from the beginning to the late stages of the disease, regardless of walking ability. Moreover, we focused on an isolated wrist flexion/extension tasks to assess wrist muscle fatigue. This ensured repeatability of the tests and prevented the adoption of compensatory movements or poor postures that may occur in multi-segmental tasks, involving the shoulder-elbow complex. In the present work, we tested the method on healthy subjects with the specific goal to evaluate when during the test the first meaningful symptoms of fatigue appaered and not how much subjects are fatigued at the end of the test. The most relevant and novel features of the proposed test include the ability to perform the test regardless of the subjects’ capability and strength, the objectivity and repeatability of the data it provides, and the simplicity and minimal time required to administer.[…]


Continue —->  Muscle fatigue assessment during robot-mediated movements | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1

Fig. 1 Experimental setup. Participant sitting on a chair with the forearm secured to the WRISTBOT while performing the wrist rotation reaching task. The visual targets of the reaching task are shown on a dedicated screen

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[ARTICLE] A modified standardized nine hole peg test for valid and reliable kinematic assessment of dexterity post-stroke – Full Text



Impairments in dexterity after stroke are commonly assessed by the Nine Hole Peg Test (NHPT), where the only outcome variable is the time taken to complete the test. We aimed to kinematically quantify and to compare the motor performance of the NHPT in persons post-stroke and controls (discriminant validity), to compare kinematics to clinical assessments of upper extremity function (convergent validity), and to establish the within-session reliability.


The NHPT was modified and standardized (S-NHPT) by 1) replacing the original peg container with an additional identical nine hole pegboard, 2) adding a specific order of which peg to pick, and 3) specifying to insert the peg taken from the original pegboard into the corresponding hole of the target pegboard. Eight optical cameras registered upper body kinematics of 30 persons post-stroke and 41 controls during the S-NHPT. Four sequential phases of the task were identified and analyzed for kinematic group differences. Clinical assessments were performed.


The stroke group performed the S-NHPT slower (total movement time; mean diff 9.8 s, SE diff 1.4), less smoothly (number of movement units; mean diff 0.4, SE diff 0.1) and less efficiently (path ratio; mean diff 0.05, SE diff 0.02), and used increased scapular/trunk movements (acromion displacement; mean diff 15.7 mm, SE diff 3.5) than controls (P < 0.000, r ≥ 0.32), indicating discriminant validity. The stroke group also spent a significantly longer time grasping and releasing pegs relative to the transfer phases of the task compared to controls. Within the stroke group, kinematics correlated with time to complete the S-NHPT and the Fugl-Meyer Assessment (rs 0.38–0.70), suggesting convergent validity. Within-session reliability for the S-NHPT was generally high to very high for both groups (ICCs 0.71–0.94).


The S-NHPT shows adequate discriminant validity, convergent validity and within-session reliability. Standardization of the test facilitates kinematic analysis of movement performance, which in turn enables identification of differences in movement control between persons post-stroke and controls that may otherwise not be captured through the traditional time-based NHPT. Future research should ascertain further psychometric properties, e.g. sensitivity, of the S-NHPT.


Impaired upper limb dexterity is evident as in many as 45–70% of the stroke victims one year post-stroke [12]. Such impairment is often evaluated in clinics by performance of the Nine Hole Peg Test (NHPT) [3], which is a frequently used dexterity task in many clinical populations [4567]. The NHPT equipment consists of a container with nine small pegs and a target pegboard with nine holes. Performance of the NHPT requires the pegs to be picked up from the container one-by-one unimanually and transferred and inserted into the holes of the pegboard until it is filled, upon which the pegs are returned unimanually to the container. The test is performed as quickly as possible and the only outcome variable is the total time to complete the task. Consequently, motor performance is currently not analyzed during the NHPT despite potentially providing valuable information relating to upper limb dexterity, especially among persons with a neurological dysfunction.

Among persons with stroke, the NHPT is considered reliable [8], valid [7910], and sensitive to change [71011]. Nevertheless, and despite overall good test-retest reliability post-stroke, low test-retest reliability has been found in persons post-stroke who have spasticity in the affected hand [8]. Further, the measurement errors are large; the minimal detectable change of the NHPT is estimated to 33 s for an individual post-stroke, and even doubled in the presence of spasticity [8]. The measurement properties of computer-assisted assessments of NHPT in virtual environments have been investigated with promising results [1213]. However, high intra-subject variation indicates that haptic and virtual reality technologies are more demanding for a stroke population and for instance require more practice trials prior to the actual test than when performing a conventional NHPT.

Advantages of the NHPT include the simple, cheap and easily portable equipment as well as the test being easy to administer and time-efficient [710]. There are, however, some drawbacks when testing persons post-stroke. First, the outcome score of the test is based solely on the time for task accomplishment [14]. Hence, a time reduction of the NHPT in rehabilitation of a person post-stroke may represent either a true motor recovery (i.e. performing movement patterns in a similar way as before the stroke) or compensation (performing different movement patterns than prior to the stroke) [15]. Compensatory strategies are common during upper limb tasks post-stroke, and thus plausible in a fine manipulative task like the NHPT. Secondly, the current NHPT test procedure may provide unreliable results for repeated measures or group comparisons as there is no standardized procedure with regard to the order in which the pegs are inserted into the target holes. To increase the stringency of the NHPT, we modified and standardized the test, which we henceforth refer to as the Standardized Nine Hole Peg test (S-NHPT). The experimental setup with two pegboards was in analogy with that of a study exploring three different methods of completing the NHPT, focusing on comparisons to tests in a virtual setting [12]. However, we have standardized the experimental setup even further by stipulating the order in which the pegs should be transferred.

Kinematic assessments may detect changes in movement performance that are not captured by only considering the time taken to complete the NHPT [14], and provide objective measures that may be more sensitive and not vulnerable to ceiling effects [16]. Recent research calls for parameters indicating quality of movements in persons post-stroke by means of kinematic analysis in order to better understand motor recovery [141517]. However, a test of fine upper limb fine dexterity like the NHPT has not been investigated. Our modifications and standardization enabled our first aim to kinematically characterize S-NHPT performance in a group of persons post-stroke and compare it to that of a non-disabled control group (discriminant validity). A second aim was to determine the convergent validity of the S-NHPT by comparing kinematics (movement time, peak speed, number of movement units, reach-grasp ratio, path ratio, acromion vertical displacement and trunk displacement) to the total movement time and to other clinical assessments (the Fugl-Meyer Assessment, the Stroke Impact Scale and grip strength). A third aim was to establish the within-session reliability of the S-NHPT, i.e., the consistency of the hand trajectories during the nine pick-up and transfer movements of the test.[…]


Continue —-> A modified standardized nine hole peg test for valid and reliable kinematic assessment of dexterity post-stroke | Journal of NeuroEngineering and Rehabilitation | Full Text


Fig. 1Experimental setup and movement phases. a) Marker positions used for the calculations of the kinematic variables. Markers displayed with a dot in the center of the marker were positioned on the trunk. The enlarged pegboard shows the standardized order of which peg to pick and which hole to fill, referred to as the “vertical row strategy”. The S-NHPT consists of 9 pegs (3.8 cm long, 0.64 cm wide) and two pegboards (12.7 cm × 12.7 cm) with 9 holes (0.70 cm wide) spaced 3.2 cm apart. The two pegboards were attached to a wooden panel with a distance of 18 cm between the center holes of the pegboards. The arrow indicates the direction of the movement. b) The velocity of the index finger marker in the medial direction displays the events defining the transfer phases Peg Transfer (positive curve) and Hand Return (negative curve). The manipulative phases Peg Grip and Peg In Hole are between those transfer movements (see Methods)

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[ARTICLE] Outcome measures in post-stroke arm rehabilitation trials: do existing measures capture outcomes that are important to stroke survivors, carers, and clinicians? – Full Text

We sought to (1) identify the outcome measures currently used across stroke arm rehabilitation randomized trials, (2) identify and compare outcomes important to stroke survivors, carers and clinicians and (3) describe where existing research outcome measures capture outcomes that matter the most to stroke survivors, carers and clinicians and where there may be discrepancies.

First, we systematically identified and extracted data on outcome measures used in trials within a Cochrane overview of arm rehabilitation interventions. Second, we conducted 16 focus groups with stroke survivors, carers and clinicians using nominal group technique, supplemented with eight semi-structured interviews, to identify these stakeholders’ most important outcomes following post-stroke arm impairment. Finally, we described the constructs of each outcome measure and indicated where stakeholders’ important outcomes were captured by each measure.

We extracted 144 outcome measures from 243 post-stroke arm rehabilitation trials. The Fugl-Meyer Assessment Upper Extremity section (used in 79/243 trials; 33%), Action Research Arm Test (56/243; 23%), and modified Ashworth Scale (53/243; 22%) were most frequently used. Stroke survivors (n = 43), carers (n = 10) and clinicians (n = 58) identified 66 unique, important outcomes related to arm impairment following stroke. Between one and three outcomes considered important by the stakeholders were captured by the three most commonly used assessments in research.

Post-stroke arm rehabilitation research would benefit from a reduction in the number of outcome measures currently used, and better alignment between what is measured and what is important to stroke survivors, carers and clinicians.

Up to 77% of stroke survivors experience upper limb (arm) impairment,1 which affects function2 and reduces health-related quality of life.3 Rehabilitation strategies, including those for the arm after stroke, should be based on research evidence. However, only moderate-quality evidence supports the use of interventions to rehabilitate the arm in current clinical practice.4 There is a demand from stroke survivors, carers, clinicians and researchers for research into interventions to improve arm function after stroke.5,6

Efficacy of interventions should be demonstrated using measures that accurately and consistently capture change following treatment.7 Researchers currently use a wide range of measures to assess the efficacy of arm interventions after stroke within randomized controlled trials; recent work has identified at least 48 arm-related measures,8 indicating heterogeneity in what is in current use, as well as the wide range of possible targets for arm interventions including specific impairments, spasticity, pain or task-specific function. The measures in current use are highly varied in their focus and methods, impacting on researchers’ ability to compare and aggregate data from different studies to examine overall efficacy. Consensus on appropriate measures would enhance our ability to detect efficacy of interventions through pooled analysis.9 It has been acknowledged that selection of measures for use in trials should capture domains of importance to patients, carers and clinicians, consider the psychometric properties of measures, and feasibility for use in clinical and research settings.10,11

There is a need for consensus on measure use in post-stroke arm rehabilitation trials.8 The Core Outcome Measures in Effectiveness Trials (COMET) initiative10,12 provides guidance on development of consensus recommendations, highlighting the importance of targeting outcomes that are important and relevant to patients and clinicians.

Considering the views of stroke survivors, clinicians and researchers, the National Institute for Neurological Disorders and Stroke-Common Data Element13 recommends items for inclusion as part of standardized data collection across all stroke trials, and the International Consortium for Health Outcomes Measurement recommends measures for standardized data collection in stroke clinical practice.14 Other recommendations exist for general stroke outcomes and reflect physicians’ opinions on important outcomes according to the World Health Organization International Classification of Functioning, Disability and Health framework.15

The Stroke Recovery and Rehabilitation Round Table, consisting of researchers and clinicians, has generated consensus recommendations for core data collection across sensorimotor stroke rehabilitation trials, including a recommendation to use the Action Research Arm Test for measurement of arm activity limitation across trials.16 In addition, work has been completed to describe the psychometric properties of 53 available arm measures17 in order to inform selection. However, due to the wide-ranging impact of stroke on people’s lives,18 arm-specific measures are unlikely to capture all important outcomes.

To date, there is no clear consensus recommendation for the selection of measures in post-stroke arm rehabilitation randomized trials. Furthermore, there is a lack of information about which outcomes are most meaningful to stroke survivors, carers and clinicians. With a view to inform recommendations for selecting measures in future trials, we sought to investigate (1) existing measures used in post-stroke arm rehabilitation research studies, (2) outcomes important to stroke survivors, their carers and practising clinicians and (3) where important outcomes are captured by existing measures.[…]


Continue —-> Outcome measures in post-stroke arm rehabilitation trials: do existing measures capture outcomes that are important to stroke survivors, carers, and clinicians? – Julie Duncan Millar, Frederike van Wijck, Alex Pollock, Myzoon Ali, 2019

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[Abstract] A Virtual Reality based Training and Assessment System for Hand Rehabilitation – IEEE Conference Publication


Virtual reality is widely applied in rehabilitation robot to help post-stoke patients complete rehabilitation training for the body function recovery. Most of virtual rehabilitation training systems lack scientific assessment standards and doctors don’t usually use quantitative examinations but qualitative observation and conversation with patients to evaluate the motor function of limb. Based on this situation, a virtual rehabilitation training and assessment system is designed, which contains two rehabilitation training games and one assessment system. The virtual system can attract patient attention and decrease the boredom of rehabilitation training and assessment. Compared with the existing rehabilitation assessment methods, the proposed virtual assessment system can give the assessment results similar to Fugl-Meyer Assessment, which is more quantitative, interesting and convenient. Five volunteers participate in the study of assessment system and the experimental results confirm the effectiveness of assessment system.

I. Introduction

In recent years, according to American Heart Association, stroke is the leading cause of serious long-term disability in the US and about 795,000 people suffer from a stroke each year [1]. China is also facing the same problem. The stroke is the first leading cause of death. Every year, 2.4 million people suffer from stroke [2]. Fortunately, about 60-75 percent of those can survive. However, about 65 percent of them still remain severely handicapped because of the neurological damage caused by stroke, for example, movement disorders, hemiparesis and so on [3], [4]. Those sequelae have an effect on body movement function, especially arm and hand function [4], [5]. The lost of hand movement function will affect the Activities of Daily Living (ADLs), which will decrease the quality of life [6].[…]

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[Abstract] The comparative efficacy of theta burst stimulation or functional electrical stimulation when combined with physical therapy after stroke: a randomized controlled trial

via The comparative efficacy of theta burst stimulation or functional electrical stimulation when combined with physical therapy after stroke: a randomized controlled trial – Fayaz Khan, Chaturbhuj Rathore, Mahesh Kate, Josy Joy, George Zachariah, P C Vincent, Ravi Prasad Varma, Kurupath Radhakrishnan, 2019

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[Abstract] Rehabilitation of stroke patients with plegic hands: Randomized controlled trial of expanded Constraint-Induced Movement therapy

via Rehabilitation of stroke patients with plegic hands: Randomized controlled trial of expanded Constraint-Induced Movement therapy – IOS Press

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[ARTICLE] Effect of a four-week virtual reality-based training versus conventional therapy on upper limb motor function after stroke: A multicenter parallel group randomized trial – Full Text



Virtual reality-based training has found increasing use in neurorehabilitation to improve upper limb training and facilitate motor recovery.


The aim of this study was to directly compare virtual reality-based training with conventional therapy.


In a multi-center, parallel-group randomized controlled trial, patients at least 6 months after stroke onset were allocated either to an experimental group (virtual reality-based training) or a control group receiving conventional therapy (16×45 minutes within 4 weeks). The virtual reality-based training system replicated patients´ upper limb movements in real-time to manipulate virtual objects.

Blinded assessors tested patients twice before, once during, and twice after the intervention up to 2-month follow-up for dexterity (primary outcome: Box and Block Test), bimanual upper limb function (Chedoke-McMaster Arm and Hand Activity Inventory), and subjective perceived changes (Stroke Impact Scale).


54 eligible patients (70 screened) participated (15 females, mean age 61.3 years, range 20–81 years, time since stroke 3.0±SD 3 years). 22 patients were allocated to the experimental group and 32 to the control group (3 drop-outs). Patients in the experimental and control group improved: Box and Block Test mean 21.5±SD 16 baseline to mean 24.1±SD 17 follow-up; Chedoke-McMaster Arm and Hand Activity Inventory mean 66.0±SD 21 baseline to mean 70.2±SD 19 follow-up. An intention-to-treat analysis found no between-group differences.


Patients in the experimental and control group showed similar effects, with most improvements occurring in the first two weeks and persisting until the end of the two-month follow-up period. The study population had moderate to severely impaired motor function at entry (Box and Block Test mean 21.5±SD 16). Patients, who were less impaired (Box and Block Test range 18 to 72) showed higher improvements in favor of the experimental group. This result could suggest that virtual reality-based training might be more applicable for such patients than for more severely impaired patients.


Virtual reality-based rehabilitation systems are gaining popularity because of their ease of use, applicability to wide range of patients, and ability to provide patient-personalized training []. Additional reported benefits of virtual reality systems for both patients and health providers include increased therapy efficiency and a high level of attention in patients during training [].

One of the main struggles therapists encounter is keeping patients motivated throughout conventional training sessions. The Yerkes-Dodson Law describes the relationship between arousal or motivation and performance []. At first, an increase in arousal and motivation leads to an increase in performance. But once a certain point is reached, this point can vary based on many factors including the task, the participant, and the context, the relationship becomes inverse and increases in arousal caused decreases in performance. In line with these ideas, previous research has shown that increased performance leads to greater improvement in patients after stroke up to a certain point. Virtual reality-based systems allow manipulation of arousal through training settings to ensure that peak performance is maintained for as large a portion of the therapy time as possible [].

Laver et al. systematically evaluated the literature regarding the efficacy of virtual reality-based training in stroke rehabilitation in 2011 and in its updates in 2015 and 2017 []. Their current meta-analysis of 22 trials including 1038 patients after stroke that focused on upper limb function did not reveal a statistically significant difference between VR-based training and conventional therapy (0.07 standard deviation higher in virtual reality-based compared to conventional therapy. Furthermore, the authors rated the quality of evidence as low, based on the GRADE system. However, for ADL function the experimental groups showed a 0.25 higher standard deviation than the conventional therapy groups based on ten studies, including 466 patients after a stroke with moderate quality of evidence.

Only 10% of the included studies included more than 50 participants, with mean ages between 46 to 76 years. However, due to the different systems used no conclusion could be drawn regarding grip strength, dosage, type or program of the virtual reality-based training. Furthermore, the authors pointed out the low sample sizes and the low methodological quality of the reported trials. In their recommendations for further research, the authors encouraged researchers and clinicians again to conduct larger trials and to increase the detail in reporting to enable more firm conclusions.

YouGrabber (now renamed Bi-Manu Trainer), a game-based virtual reality system designed for upper-limb rehabilitation, has been shown to be effective in children with cerebral palsy. A 2-subject feasibility study indicated that the findings might extend to chronic stroke patients []. Both male subjects, who were trained three years after insult onset, showed increases in scores for the bimanual activities of daily living focused Chedoke McMaster Arm and Hand Activity Inventory (CAHAI) that persisted at the final follow-up, and corresponding cortical changes measured with fMRI.

Based on these findings the present multicenter parallel group randomized single-blinded trial aimed to investigate the efficacy of a virtual reality-based training with the YouGrabber training device (now renamed Bi-Manu Trainer) compared to conventional therapy. The study was designed to test the hypothesis that patients in the chronic stage after stroke in the virtual reality-based training group will show no higher post-intervention performance in the Box and Block Test (BBT) compared to patients receiving an equal training time of physiotherapy or occupational therapy.

For comparison with published and ongoing international studies we selected the Box and Block Test as the primary outcome measure and the CAHAI as the secondary outcome measure.

Methods and materials

Study design

This prospective, multicenter, single-blinded, parallel-group randomized trial was conducted in the outpatient departments of three rehabilitation hospitals in the German and French speaking parts of Switzerland: University hospital Inselspital Bern, Buergerspital Solothurn, and Reha Rheinfelden. In the study plan, each hospital was responsible for the recruitment, assessment, and therapy of 20 patients: 10 patients for the experimental group (EG) and 10 for the control group (CG), respectively.

More details regarding the study methodology can be found in the study flow chart in Fig 1 and the previously published study protocol strictly followed by each center ( []. Ethics approval was warranted by the ethics committee of the Canton Aargau (2012/065) and the Canton Berne (220/12). The study was registered with NCT01774669 before the start of patient recruitment.

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Fig 1
Patient flow chart.BS = Buergerspital Solothurn, IS = Inselspital Bern, Reha Rheinfelden Measurement sessions: twice within one to two weeks before intervention start (BL, T0), once after eight (T1) and after 16 (T2) intervention sessions, and after a two months follow-up period (FU).


Continue —> Effect of a four-week virtual reality-based training versus conventional therapy on upper limb motor function after stroke: A multicenter parallel group randomized trial

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[Abstract] Error-augmented bimanual therapy for stroke survivors

via Error-augmented bimanual therapy for stroke survivors – IOS Press

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[Abstract] EXOPINCH – A Robotic Mirror Therapy System for Hand Rehabilitation


Introduction ExoPinch is a robotic mirror therapy system for hand rehabilitation, focusing to increase the corticospinal excitability for the patients with hemiparesis. We propose that specific type of visual stimuli may be implemented in the action observation treatment to have a positive additional impact by activating the mirror neuron system (MNS) in premotor cortex. Recently, mirror therapy (MT) has been used as an alternative treatment for stroke of upper and lower limbs. In MT, the patient places the intact limb on the reflective side of a mirror and the non-intact limb on the non-reflective side of the mirror. Observation of the healthy limb’s reflection gives the illusion that the affected limb is functioning as instructed [1]. The underlying mechanism of the MT of stroke patients has mainly been related to the activation of the neurons with mirror-like properties. They were first discovered in the macaque monkey ventral premotor area F5 [2]. These mirror neurons discharge both when a particular action is done by an individual and when that same action done by another individual is observed. MT together with robotic assistive devices in the field of rehabilitation has led researchers to the robotics neuro-rehabilitation [3] and robots are particularly suitable for the application of motor learning principles to neurorehabilitation [4]. In the robotic mirror therapy systems, the motion of the functional hand is tracked by the intact hand using the robotic system. Based on the properties of the MNS and its role in motor learning, this system has been activated as a novel approach for training in the rehabilitation of patients with motor impairment of the upper limb following stroke. In this study, unlike the conventional mirror therapy where the functional hand motion is observed through a mirror, selected motions which provide higher activation for the mirror neuron system are observed through the prepared video streams aiming to improve the efficacy of the therapy. ExoPinch assists the patient’s index and thumb fingers to track the observed and imagined pinching actions. The selected motions are determined by the experiments on healthy subjects. In general, MNS is supposed to decode the kinematics of the observed motion. During the experiments, it is seen that the observed actions that include kinetic features (imposing force or torque) also increase the MNS activity. Therefore, the selected motions for the robotic mirror therapy system include features enforcing the kinetics, as well. This approach is supported by the motor learning principles where the kinematic and kinetic aspects are both concerned [6]. Methods ExoPinch is an exoskeletal type of rehabilitation robot. The index and thumb fingers are the parts of fully-actuated mechanisms with 2 degrees of actuation and 1 degree of actuation respectively. The exoskeleton mechanism of ExoPinch is synthesized using genetic algorithm over a multiobjective objective function. The mechanism design is based on the kinematic synthesis and the optimization of the transmission angles during the pinching motion. Dynamical models are built in MATLAB and Simechanics. Passive joint torques of the index and thumb fingers with spasticity are modeled as well to introduce the resistances to the motion. 10 healthy volunteers participated in this study. In the experiments, the suppression (desynchronization) in mu band (8-12 Hz) power as an index of the human mirror neuron system (MNS) [7] was studied while subjects observed object-directed hand actions with varying kinetics and kinematics contexts: squeezing a hard and a soft spring; grasping a long and a short stick, Fig.1. Our main purpose was to explore whether observation of any of these actions may have a relatively strong effect on MNS activity. The activation of mirror neurons in premotor cortex during action observation plays a crucial role in observational learning [5],[8] and rehabilitation is a motor relearning process [6]. Therefore, the recruitment of MNS in this respect with action observation might provide an effective neurorehabilitative program for patients with strok that may lead to a personal optimal therapy in the future. Figure 1. Video library elements imposing kinetic and kinematic features Electroencephalography (EEG) method was used to investigate the activity of the MNS. EEG data were recorded continuously (bandpass, 0.1-100 Hz; sampling rate, 250 Hz) with the 16 channel 32-bit A/D converter using OpenBCI. UltraCortex Mark 2 dry electrode headset was used conforming international 10-20 electrode placement. EEG data were processed offline using EEGLAB. The mean mu (8-12 Hz) band power values (in dB) were extracted at a number of frontal (F7, F8), central (C3, C4) and parietal (P3, P4) channels since these regions almost exclusively included regions that have been associated with the MNS in the literature. Event Related Spectral Perturbation (ERSP) method was used for analyzing the mirror neuron activity in time-frequency domain. A two-way repeated measures of ANOVA revealed the main effect of video stimuli of squeezing soft/hard springs, at the frontal channels close to ventral premotor cortex area of the brain. These results showed that the observed actions imposing kinetic features can increase the MNS activity. Therefore, the selected motions to be observed by the patients will include the features that impose the kinetics, as well, aiming to improve the efficacy of the therapy.

via (PDF) EXOPINCH-A Robotic Mirror Therapy System for Hand Rehabilitation


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