Archive for October, 2018

[WEB SITE] PolyU develops robotic arm for self-help mobile rehabilitation for stroke patients


The PolyU-developed robotic arm is the first-of-its-kind integration of exo-skeleton, soft robot and exo-nerve stimulation technologies. It is light in weight, compact in size, fast in response and demands minimal power supply, thus suitable for use in both indoor and outdoor environment.
The Hong Kong Polytechnic University


(October 31, 2018) The Hong Kong Polytechnic University (PolyU) recently developed a robotic arm to facilitate self-help and upper-limb mobile rehabilitation for stroke patients. The lightweight device enables the patients to engage in intensive and effective self-help rehabilitation exercise anywhere, anytime after they are discharged from hospital. The robotic arm, called “mobile exo-neuro-musculo-skeleton”, is the first-of-its-kind integration of exo-skeleton, soft robot and exo-nerve stimulation technologies.

Stroke is the third leading cause of disability worldwide. In Hong Kong, there are about 25,000 new incidences of stroke annually in recent years. Research studies have proven that intensive, repeated and long-term rehabilitation training are critical for enhancing the physical mobility of stroke patients, thus help alleviating post-stroke symptoms such as disability. However, access to the outpatient rehabilitation service for stroke patients has been difficult. Due to the overwhelming demand for rehabilitation services, patients have to queue up for a long time to get a slot for rehabilitation training. As such, they can’t get timely support and routine rehabilitation exercises. Stroke patients also find it challenging to travel from home to outpatient clinics.

The “mobile exo-neuro-musculo-skeleton”, developed by Dr Hu Xiao-ling and her research team in the Department of Biomedical Engineering (BME) of PolyU, features lightweight design (up to 300g for wearable upper limb components, which are fit for different functional training needs), low power demand (12V rechargeable battery supply for 4-hour continuous use), and sportswear features. The robotic arm thus provides a flexible, self-help, easy-to-use, mobile tool for patients to supplement their rehabilitation sessions at the clinic. The innovative training option can effectively enhance the rehabilitation progress.

Dr Hu Xiaoling said development of the novel device was inspired by the feedback of many stroke patients who were discharged from hospital. They faced problems in having regular and intensive rehabilitation training crucial for limb recovery. “We are confident that with our mobile exo-neuro-musculo-skeleton, stroke patients can conduct rehabilitation training anytime and anywhere, turning the training into part of their daily activities. We hope such flexible self-help training can well supplement traditional outpatient rehabilitation services, helping stroke patients achieve a much better rehabilitation progress.” Her team anticipated that the robotic arm can be commercialised in two years.

The BME innovation integrates exo-skeleton and soft robot structural designs – the two technologies commonly adopted in existing upper-limb rehabilitation training devices for stroke patients as well as the PolyU-patented exo-nerve stimulation technology.

Integration of exo-skeleton, soft robot and exo-nerve stimulation technologies

The working principle of both exo-skeleton and soft robot designs is to provide external mechanical forces driven by voluntary muscle signals to assist the patient’s desired joint movement. Conventional exo-skeleton structure is mainly constructed by orthotic materials such as metal and plastic, simulating external bones of the patient. Although it is compact in size, it is heavy and uncomfortable to wear. Soft robot, made of air-filled or liquid-filled pipes to simulate one’s external muscles, is light in weight but very bulky in size. Both types of structures demand high electrical power for driving motors or pumps, thus it is not convenient for patients to use them outside hospitals or rehabilitation centres. Combining the advantages of both structural designs, the BME innovative robotic arm is light in weight, compact in size, fast in response and demands minimal power supply, therefore it is suitable for use in both indoor and outdoor environment.

The robotic arm is unique in performing outstanding rehabilitation effect by further integrating the external mechanical force design with the PolyU-patented Neuro-muscular Electrical Stimulation (NMES) technology. Upon detecting the electromyography signals at the user’s muscles, the device will respond by applying NMES to contract the muscles, as well as exerting external mechanical forces to assist the joint’s desired voluntary movement. Research studies found that the combination of muscle strength triggered by NMES and external mechanical forces is 40% more effective for stroke rehabilitation than applying external mechanical forces alone.

Rehabilitation effect proven in trials

An initial trial of the robotic arm on 10 stroke patients indicated better muscle coordination, wrist and finger functions, and lower muscle spasticity of all after they have completed 20 two-hour training sessions. Further clinical trials will be carried out in collaboration with hospitals and clinics.

The robotic arm consists of components for wrist/hand, elbow, and fingers which can be worn separately or together for different functional training needs. The sportswear design, using washable fabric with ultraviolet protection and good ventilation, also makes the robotic arm a comfortable wear for the patients.

The device also has a value-added feature of connecting to a mobile application (APP) where user can use the APP interface to control their own training. The APP also records real-time training data for better monitoring of the rehabilitation progress by both healthcare practitioners and the patients themselves. It can also serve as a social network platform for stroke patients to communicate online with each other for mutual support.


Press Contacts:

Dr Hu Xiaoling, Assistant Professor
Department of Biomedical Engineering, PolyU
Telephone : 3400 3206
Email :


via PolyU develops robotic arm for self-help mobile rehabilitation for stroke patients | EurekAlert! Science News

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[ARTICLE] Minimal Clinically Important Difference of the 6-Minute Walk Test in People With Stroke – Full Text


Background and Purpose: The 6-minute walk test (6MWT) is commonly used in people with stroke. The purpose of this study was to estimate the minimal clinically important difference (MCID) of the 6MWT 2 months poststroke.

Methods: We performed a secondary analysis of data from a rehabilitation trial. Participants underwent physical therapy between 2 and 6 months poststroke and the 6MWT was measured before and after. Two anchors of important change were used: the modified Rankin Scale (mRS) and the Stroke Impact Scale (SIS). The MCID for the 6MWT was estimated using receiver operating characteristic curves for the entire sample and for 2 subgroups: initial gait speed (IGS) <0.40 m/s and ≥0.40 m/s.

Results: For the entire sample, the estimated MCID of the 6MWT was 71 m with the mRS as the anchor (area under the curve [AUC] = 0.66) and 65 m with the SIS as the anchor (AUC = 0.59). For participants with IGS <0.40 m/s, the estimated MCID was 44 m with the mRS as the anchor (AUC = 0.72) and 34 m with the SIS as the anchor (AUC = 0.62). For participants with IGS ≥0.40 m/s, the estimated MCID was 71 m with the mRS as the anchor (AUC = 0.59) and 130 m with the SIS as the anchor (AUC = 0.56).

Discussion and Conclusions: Between 2 and 6 months poststroke, people whose IGS is <0.40 m/s and experience a 44-m improvement in the 6MWT may exhibit meaningful improvement in disability. However, we were not able to estimate an accurate MCID for the 6MWT in people whose IGS was ≥0.40 m/s. MCID values should be estimated across different levels of function and anchors of importance.

Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at:


The 6-minute walk test (6MWT) is commonly used in people with stroke undergoing rehabilitation.1–3 Although originally developed and validated as a submaximal oxygen consumption test for individuals with cardiac or pulmonary disease,4 , 5 the 6MWT is a valid6–11and reliable12 , 13 measure of walking endurance and is highly recommended by the Academy of Neurologic Physical Therapy for use with people with stroke and other neurologic conditions across the continuum of care.14 More recently, the 6MWT has been used to predict community walking activity.15

An important psychometric property of any outcome measure is its sensitivity to change and responsiveness. Liang and colleagues16 , 17 define sensitivity to change as the ability of an instrument to measure change regardless of whether or not that change is important; it is the amount of change that exceeds measurement error and patient variability. Responsiveness is the ability of an instrument to measure important change. In particular, the minimal detectable change ([MDC] an index of sensitivity to change) and the minimal clinically important difference ([MCID] an index of responsiveness) are useful for clinicians and researchers when interpreting scores and/or change on an outcome measure. The MDC is an estimate of the measurement error and random fluctuation in the test score in patients who are stable.18 , 19

Although MDC is useful for interpreting change scores, it is not ideal, as it provides only the information that the change has exceeded measurement error and variability in patients who are stable. Conversely, the MCID is more useful clinically as it provides an index of important change. The MCID involves an anchor-based approach to estimating how much change in an outcome measure is clinically important and meaningful. The anchor is some external variable that is judged to be important.20 External anchors can be patients’ perception of important change, clinicians’ perception of important change, or an objective marker of important change (eg, discharge home).20 For example, Fulk and colleagues21 used patient and therapist’s perception of important change measured with a Global Rating of Change Scale as an anchor to estimate clinically important change in the Arm Motor Ability Test. When estimating the MCID of gait speed, Tilson and colleagues22 used a 1-point improvement on the modified Rankin Scale (mRS) as the anchor of important improvement in disability.

Unfortunately, there is limited research on the sensitivity to change and responsiveness of the 6MWT in people with stroke. In people with chronic stroke, the MDC is estimated to be 29 m,12 ,23 while in people with stroke undergoing inpatient rehabilitation 30 days poststroke, the MDC is estimated to be 54 m.10 To the best of our knowledge, the MCID of the 6MWT has been reported for people with stroke in only 1 other study. Using data from a completed rehabilitation trial, Perera and colleagues24 estimated meaningful change in the 6MWT using 3 different methodologies. They used an anchor-based approach using decline on 2 items of the 36-Item Short Form Health Survey (walking 1 block and climbing a flight of stairs) as the anchors. Using a distribution-based approach, they calculated standard error of measurement, and they multiplied mean baseline 6MWT distance by a small (0.2) and medium effect size (0.5). Limitations in their findings are that the anchor-based approach used was in relation to decline in performance on the anchor and so should not be applied when trying to interpret improvement. The distribution-based methods Perera and colleagues24 used to estimate change in the 6MWT were based on patients whose condition was stable and are indices of sensitivity to change not responsiveness (ie, important change). However, the MCID of the 6MWT has been reported for other patient populations and has been estimated to be between 14.0 m and 156 m in people with chronic obstructive pulmonary disease, lung disease (lung disease), coronary artery disease, fibromyalgia, and older adults.25–28

The purpose of this research study was to estimate the MCID of the 6MWT in people with stroke undergoing outpatient rehabilitation 2 months poststroke using an anchor-based approach. Based on the MDC values reported in the literature, we hypothesized that the MCID would be greater than the reported MDC values.[…]


Continue —> Minimal Clinically Important Difference of the 6-Minute Walk… : Journal of Neurologic Physical Therapy

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[ARTICLE] Motor Overflow and Spasticity in Chronic Stroke Share a Common Pathophysiological Process: Analysis of Within-Limb and Between-Limb EMG-EMG Coherence – Full Text


The phenomenon of exaggerated motor overflow is well documented in stroke survivors with spasticity. However, the mechanism underlying the abnormal motor overflow remains unclear. In this study, we aimed to investigate the possible mechanisms behind abnormal motor overflow and its possible relations with post-stroke spasticity. 11 stroke patients (63.6 ± 6.4 yrs; 4 women) and 11 healthy subjects (31.18 ± 6.18 yrs; 2 women) were recruited. All of them were asked to perform unilateral isometric elbow flexion at submaximal levels (10, 30, and 60% of maximum voluntary contraction). Electromyogram (EMG) was measured from the contracting biceps (iBiceps) muscle and resting contralateral biceps (cBiceps), ipsilateral flexor digitorum superficialis (iFDS), and contralateral FDS (cFDS) muscles. Motor overflow was quantified as the normalized EMG of the resting muscles. The severity of motor impairment was quantified through reflex torque (spasticity) and weakness. EMG-EMG coherence was calculated between the contracting muscle and each of the resting muscles. During elbow flexion on the impaired side, stroke subjects exhibited significant higher motor overflow to the iFDS muscle compared with healthy subjects (ipsilateral or intralimb motor overflow). Stroke subjects exhibited significantly higher motor overflow to the contralateral spastic muscles (cBiceps and cFDS) during elbow flexion on the non-impaired side (contralateral or interlimb motor overflow), compared with healthy subjects. Moreover, there was significantly high EMG-EMG coherence in the alpha band (6–12 Hz) between the contracting muscle and all other resting muscles during elbow flexion on the non-impaired side. Our results of diffuse ipsilateral and contralateral motor overflow with EMG-EMG coherence in the alpha band suggest subcortical origins of motor overflow. Furthermore, correlation between contralateral motor overflow to contralateral spastic elbow and finger flexors and their spasticity was consistently at moderate to high levels. A high correlation suggests that diffuse motor overflow to the impaired side and spasticity likely share a common pathophysiological process. Possible mechanisms are discussed.


When a stroke survivor with spastic hemiplegia is asked to squeeze the hand or flex the elbow joint on the non-impaired side as shown in Figure Figure1,1, there is involuntary activation of spastic finger and elbow flexors on the impaired side (Figures 1A, B). This phenomenon of involuntary activation of spastic muscles can occur in about 30% of hemiplegic stroke (). It is often referred as motor overflow or associated reaction (). Other terms, such as mirror movement, global synkinesis, are sometimes used interchangeably for the same clinical observation (). Motor overflow is one form of the spastic muscle overactivity. Other types of muscle overactivity are also seen clinically, such as spastic dystonia, co-contraction ().

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Figure 1
Motor overflow in a 41 year old stroke survivor with right spastic hemiplegia from a left middle cerebral artery hemorrhagic stroke. (A) standing and relaxed; (B)standing and left hand squeezing; (C) sitting and relaxed; (D) sitting and resisted hand/finger extension on the left side. Photos were recently taken from PI’s spasticity clinic, a written consent of media release was signed by the patient.

Motor overflow is commonly observed in the contralateral homologous resting muscle(s). It can also be seen from proximal muscles to distal muscles in a form of abnormal synergy (), and between limbs on the impaired side through interlimb coupling (). As demonstrated in Figures 1C,D, motor overflow to the contralateral spastic finger and elbow flexors occurs during voluntary finger extension on the non-impaired side. These clinical presentations indicate that motor overflow to the spastic muscles is non-selective, diffuse, and concomitantly with voluntary activation of other muscles. In contrast, motor overflow seen in neurologically intact adults is mainly in contralateral homologous muscles in the context of extreme effort or fatigue [see review ()]. Therefore, motor overflow in stroke survivors is likely mediated by different mechanisms than in healthy adults. However, the underlying mechanisms for motor overflow after stroke are poorly understood.

A number of methods have been used in the literature to evaluate motor overflow after neurological impairments, including surface EMG, goniometry, dynamometry, electrogoniometry, and clinician rating form. Surface EMG is the most commonly used laboratory-based method (). In our recent studies (), involuntary EMG activities of the contralateral resting muscles were used to quantify the extent of motor overflow during unilateral voluntary elbow flexion tasks. Using quantitative assessment, the level of motor overflow is found to be graded by the effort of the non-impaired muscles (). Furthermore, EMG-EMG coherence analysis between EMG signals from the contracting muscle and the contralateral resting muscles could provide potential sources of motor overflow. Coherence analysis is based on the cross-correlation between two separate signals in the frequency domain. Coherence values fall between 0 and 1. Commonly studied frequency bands include 6–12 Hz (alpha band), 13–30 Hz (beta band), and 30–60 Hz (gamma band). It is well accepted that both beta and gamma bands have cortical origins (). Coherence in the alpha band is believed to have subcortical influences, may be related to the reticulospinal drive (). For example, EMG signals were recorded from bilateral homologous muscles, such as biceps muscles during motoric responses of acoustic startle reflex and during similar voluntary movements in healthy subjects. EMG-EMG coherence in the alpha band was significantly greater during startle reflex responses than during voluntary movement, suggestive of a reticulospinal origin of such coherence in the alpha band ().

Motor overflow is often seen and elicited in stroke survivors with spasticity. Its relation with post-stroke spasticity remains controversial. Motor overflow is found to be associated with spasticity in some studies (), but not in others (). In all these studies, spasticity was assessed using clinical scales, such as modified Ashworth scale or Tardieu scale. Quantitative assessment is likely to provide better insights into this relationship. Based on the velocity-dependent increase in resistance feature of spasticity, a quantitative assessment with computerized control of external stretch was developed (). In this approach, a joint is stretched by a motorized device at a controlled, constant speed. Resistance torque is obtained to quantify responses from spastic muscles. Reflex torque is quantified objectively by subtracting passive resistance at a very slow speed of stretch, e.g., 5°/s from that at a fast speed, e.g., 100°/s. Reflex torque is attributed primarily to underlying neural mechanisms of spasticity. In a previous study (), we have demonstrated that reflex torque was velocity-dependent at the same wrist position (muscle length), and changed with various wrist positions at the same speed of stretch. This biomechanical quantification of spasticity is also sensitive to quantify reflex and non-reflex responses from spastic elbow flexors in response to controlled cold exposure ().

In the present study, the specific aim was to examine the possible mechanisms mediating the phenomenon of motor overflow in chronic stroke. Stroke survivors and healthy controls were instructed to flex the elbow joint voluntarily at submaximal levels. Surface EMG signals were recorded from bilateral elbow flexors and finger flexors to quantify motor overflow. Within-limb and between-limb EMG-EMG coherence analyses were performed. Elbow flexor spasticity was quantified using our established biomechanical approach. Since motor overflow is commonly seen in stroke survivors with spasticity, they may share the same underlying pathophysiology. We hypothesized that there is greater motor overflow to the spastic elbow and finger flexors and that greater motor overflow is highly correlated with spasticity, as compared to the control group. Furthermore, post-stroke spasticity is primarily attributed to reticulospinal hyperexcitability and has separate underlying mechanisms for weakness (). between-limb intermuscular EMG signals were hypothesized to have significant EMG-EMG coherence in the alpha band to reflect reticulospinal hyperexcitability. Motor overflow was further hypothesized to correlate with spasticity (reflex torque), but not weakness.[…]

Continue —>  Motor Overflow and Spasticity in Chronic Stroke Share a Common Pathophysiological Process: Analysis of Within-Limb and Between-Limb EMG-EMG Coherence

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[Abstract] Efficiency in stroke management from acute care to rehabilitation: bedside versus telemedicine consultation



BACKGROUND: Telemedicine has changed over the last years, becoming an integrated service used in various clinical settings such as stroke units or radiological departments, but also as an important tool for home rehabilitation. Assessment of usefulness and efficiency of performing teleconsultations to manage stroke from acute care hospital to tertiary care rehabilitation hospital has not been referred by scientific literature.
AIM: This article analyzes the process of discharging stroke patients from acute care to intensive rehabilitation, based on the comparison between conventional bedside patient evaluations and teleconsultation patient evaluations, to assess efficiency and efficacy of two different discharging workflows.
DESIGN: Retrospective study.
SETTING: Consultations were carried out between the Acute Care Stroke Unit (ACSU) and the Stroke Rehabilitation Unit (SRU) of Valduce Hospital System.
POPULATION: 257 stroke patients underwent physiatric consultation during 2 years considered in this study and 101 patients were considered eligible for intensive rehabilitation treatment after a physiatric consultation.
METHODS: we compared the efficiency and efficacy of the dismission workflow of bedside medical consultation and teleconsultation over a 12 months period. We considered the following outcome measures: time elapsed between consultation and rehabilitation unit admission, number of re-admissions to acute care hospital, complications occurred during rehabilitation, length of stay in the rehabilitation hospital and clinical outcomes of rehabilitation process.
RESULTS: we observed a significant reduction in waiting time from the acute event to the admission in rehabilitation department, an improvement in efficiency of the admission process itself in the rehabilitation unit and a reduction of clinical complications occurred during rehabilitation period, without changes in rehabilitative outcomes.
CONCLUSIONS: it has been highlighted that the use of telemedicine to perform medical consultation as a tool to evaluate patients eligible for tertiary care rehabilitation hospital admission from stroke care unit is feasible and more efficient when compared with conventional bedside consultations.
CLINICAL REHABILITATION IMPACT: this study reveals teleconsultations as a useful tool to improve efficiency of the stroke management workflow.


via Efficiency in stroke management from acute care to rehabilitation: bedside versus telemedicine consultation – European Journal of Physical and Rehabilitation Medicine 2018 Oct 29 – Minerva Medica – Journals

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[WEB SITE] New Epilepsy Bracelet Could Save Thousands of Lives

High-Tech “Nightwatch” is Capable of Detecting 85 Percent of Severe Night-Time Epileptic Seizures

Scientists in the Netherlands are optimistic that their new device will reduce the number of sudden unexpected death in epilepsy (SUDEP) patients worldwide. Currently, for people with an intellectual disability and severe treatment-resistant epilepsy, the outlook is poor, with a possible 20 percent lifetime risk of dying from epilepsy. While several techniques exist for monitoring patients at night, many seizures are still being missed.

With this in mind, a consortium of researchers (from Kempenhaeghe epilepsy centre, Eindhoven University of Technology, the Foundation for Epilepsy Institutions in the Netherlands (SEIN), UMC Utrecht, the Epilepsy Fund, patient representatives, and LivAssured) developed Nightwatch, a bracelet that recognizes unusually fast heartbeat and rhythmic jolting movements, two critical characteristics of severe attacks. When these occur, the device sends a wireless alert to caregivers or nurses.

In a test among 28 intellectually handicapped patients with epilepsy, over an average of 65 nights, Nightwatch detected 85 percent of all serious attacks and 96 percent of the most severe ones (tonic-clonic seizures). In comparison, a bed sensor, which is the current detection standard, sounded the alarm for only 21 percent of serious attacks. While the bed sensor was silent once every four nights per patient, the Nightwatch only missed a serious attack once every 25 nights, on average.

Prof. Dr. Johan Arends, neurologist and research leader, expects that the bracelet may reduce the number of SUDEP cases by two-thirds, although this also depends on the speed and efficiency with which caregivers respond to the alerts.

Source:, October 29, 2018


via New Epilepsy Bracelet Could Save Thousands of Lives | Managed Care magazine

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[Abstract + References] Methods of Motion Assessment of Upper Limb for Rehabilitation Application – IEEE Conference Publication


The aim of this paper is to describe methods proposed for motion capture subsystem of smart orthosis for quantitative evaluation of movement activity of upper limbs during a rehabilitation process carried out at a clinic or at home. To quantify the description of motion we used methods of evaluation of the relationship between measured variables and nonlinear methods. To test the functionality of the methods, we compared the movement of the dominant and non-dominant limbs, assuming cyclical and acyclic movement, to obtain the expected values for a healthy population. In accordance with the goal, a group of cyclic and non-cyclic movements common to the home environment were proposed. The movements were divided according to the activities performed during sitting, standing and walking. It was: pen writing, typing on the keyboard / using the mouse, eating with a spoon and eating a croissant combing, lifting weights, reading a book, etc. Twenty healthy subjects participated in the study. Four gyro-accelerometers (Xsens Technologies B.V.) attached to the forearms and upper arms of both upper limbs were used to record the upper limb movements. The results show that the calculated values of dominant and non-dominant limb parameters differ significantly in most movements. The motion capture subsystem which uses the proposed methods can be used to valuate the physical activity for quantification of the evaluation of the rehabilitation process, and thus, it finds use in practice.
1. D. P Romilly, C Anglin, R. G Gosine, C Hershler, S. U. Raschke, “A Functional Task Analysis and Motion Simulation for the Development of a Powered Upper-Limb Orthosis”, IEEE Transactions on Rehabilitation Engineering, pp. 119-129, 1994.

2. R. Rupp, M. Rohm, M. Schneiders, A. Kreilinger, G. R Müller-Putz, “Functional rehabilitation of the paralyzed upper extremity after spinal cord injury by noninvasive hybrid neuroprostheses”, Proceedings of the IEEE, pp. 954-968, 2015.

3. R. C. Oldfield, “The assessment and analysis of handedness”, The Edinburgh inventory. Neuropsychologia, pp. 97-113, 1971.

4. P. Kutilek, O. Cakrt, J. Hejda, “Com-parative measurement of the head orientation using camera system and gyroscope system”, 13th Mediterranean conference on medical and biological engineering and computing Seville Spain IFMBE Proceedings Volume 41, pp. 1519-1522, 2013.

5. P. Kutilek, V. Socha, O. Cakrt, J. Schlenker, L. Bizovska, “Trajectory length of pitch vs. roll. Technique for assessment of postural stability”, Acta Gymnica, pp. 85-92, 2015.

6. J. H Allum, L. B. O. Nijhuis, M. G. Carpenter, “Differences in coding provided by proprioceptive and vestibular sensory signals may con-tribute to lateral instability in vestibular loss subjects”, Experimental brain research, vol. 184, no. 3, pp. 391-410, 2008.

7. Á. Gil-Agudo, L. A. Reyes-Guzman, Dimbwadyo-Terrer, I. Peñasco-Martín, B. Bernal-Sahún, A. P.López-Monteagudo, A. Ama-Espinosa, J. L Pons, “A novel motion tracking system for evaluation of functional rehabilitation of the upper limbs”, Neural regeneration research, vol. 8, no. 19, pp. 1773-1782, 2013.

8. D. Stirling, A. Hesami, C. Ritz, K. Kdistambha, F. Naghdy, “Symbolic Modelling of Dynamic Human Motions”, Biosensors. Pier Andrea Serra, 2013.

9. F. Lorussi, N. Carbonaro, D. D. Rossi, A. Tognetti, “A biarticular model for scapular-humeral rhythm reconstruction through data from wearable sensors”, J Neuroeng Rehabil, vol. 13, pp. 40, 2016.

10. D. Winter, “Stiffness Control of Balance in Quiet Standing”, Journal of Neurophysiology, pp. 1211-1221, 1998.

11. P. Kutilek, B. Farkasova, “Prediction of Lower Extremities’ Motion by Angle-angle Diagrams and Neural Networks”, Acta of Bioengineering and Biomechanics, pp. 57-65, 2011.

12. S. M. Bruijn, “Assessing Stability of Human Locomotion: a review of current measures” in Journal of the Royal Society Interface, 2013.

13. B. Coley, B. M. Jolles, A. Farron, A. Bourgeois, F. Nussbaumer, C. Pichonnaz, K. Aminian, “Outcome evaluation in shoulder surgery using 3D kinematics sensors”, Gait& Posture, vol. 25, pp. 523-532, 2007.

14. A. Wolf, J. B. Swift, H. L. Swinney, J. A. Vastano, “Determining Lyapunov exponents from a time series”, Physica 16D, pp. 285-317, 1985.

15. D. E. Lake, J. S. Richman, M. P. Griffin, J. R. Moorman, “Sample entropy analysis of neona-tal heart rate variability”, American Journal of Physiology – Regulatory Integrative and Comparative Physiology, vol. 283, no. 3, 2002.

16. M. O. Sokunbi, “Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets”, Front. Neuroinform, 2014.

17. B. Singh, M. Singh, V. K. Banga, “Sample Entropy based HRV: Effect of ECG Sampling Frequency”, Biomedical Science and Engineering, 2014.

18. Z. Jian-Jun, N. Xin-Bao, Y. Xiao-Dong, H. Feng-Zhen, H. Cheng-Yu, “Decrease in Hurst expo-nent of human gait with aging and neurodegenerative diseases”, Chin. Phys. Soc. and IOP Publishing Ltd Chinese Physics B, vol. 17, 2008.

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via Methods of Motion Assessment of Upper Limb for Rehabilitation Application – IEEE Conference Publication

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[ARTICLE] Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training – Full Text


Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients.

1. Introduction

Over the past decade, the increasing stroke patient population has brought great economic and medical pressures to the whole society. Surviving stroke patients usually have a lower quality of life dues to physical disability and cognitive impairment. Studies on clinical stroke treatment indicate that appropriate rehabilitation training has positive therapeutic effects for avoiding muscle atrophy and recovering musculoskeletal motor functions. However, the conventional one-on-one manual-assisted movement training conducted by physiotherapists suffers from many inherent limitations, such as high labor intensity, high cost, long time consumption, lack of repeatability, low participation levels of patient, and high dependence on personnel with specialized skills [1,2]. In recent years, robot-assisted rehabilitation therapies have gained growing interest from academic researchers and the healthcare industry around the world due to their unique advantages and promising application perspectives. Compared with the traditional manual rehabilitation treatment, the combination of robotic technologies and clinical experience can significantly improve the performance and quality of training. Robot-assisted therapy is capable of delivering high-intensity, long-endurance, goal-directed, and low-cost rehabilitation treatment. Moreover, the functional motivations of patient can be activated to enhance active participation and recover cognitive functions. The physical parameters and therapy data can be recorded and analyzed via sensing system, and that can provide objective basis to optimize training strategy and accelerate recovery process [3,4].
Many therapeutic robot system have been developed to assist stroke patients with motor dysfunctions perform the desired rehabilitation training. The existing rehabilitation robotic devices can be categorized into two types, i.e., end-effector-based robots and exoskeleton-based robots. End-effector-based robot has only a connection between its distal end and the impaired extremity of patient. However, the movement of end-effector cannot uniquely identify the configuration of human limb due to the kinematic redundancy. Miller et al. developed a lightweight and potable end-effector-based therapeutic robot, which is integrated with a wrist and finger force sensor module named WFES, for the upper limb rehabilitation training of hemiplegic stroke patients [5]. Pedro et al. developed a parallel kinematic mechanism (PKM) with two translational and two rotational degrees of freedom (DOFs) for knee diagnosis and rehabilitation tasks [6]. Kang et al. proposed a modular and reconfigurable wrist robot called CR2-Haptic for post-stroke subjects to train forearm and wrist movements [7]. Besides, many other end-effector-based therapeutic robot have been investigated and can be referred to [8,9,10,11,12,13]. Comparatively, the exoskeleton-based rehabilitation robots are developed with more complex structures imitating the anatomical human skeleton and guaranteeing the alignment between the joints axis of robot and impaired limb. ChARMin is a powered exoskeleton integrated with audiovisual game-like interface. It can provide intensive pediatric arm rehabilitation training for the children and adolescents with affected motor functions [14]. Simon et al. proposed a spherical shoulder exoskeleton with a double parallelogram linkage to eliminate singularities and achieve good manipulability properties [15]. Crea et al. developed a semi-autonomous whole-arm exoskeleton for the stroke patients performing activities of daily living (ADL) utilizing hybrid electroencephalography and electrooculography feedback signals [16]. Many other representative exoskeleton-based therapeutic robot have also been designed, such as CAREX-7 [17], RUPERT [18], ULEL [19], ArmeoPower [20], Indego [21], and ETS-MARSE [22].
The effectiveness of robot-assisted rehabilitation training depends on the control strategies applied in the therapeutic robot system. Currently, many kinds of control strategies have been developed according to the requirements of patients with various impairment severities in different therapy periods. The existing control schemes can be basically divided into two categories based on the interaction between therapeutic robots and patients, i.e., patient-passive training control and patient-cooperative training control. During the acute period of hemiplegia, the impaired extremity is fully paralyzed without any muscle contraction. The patient-passive training can imitate the manual therapeutic actions of a physiotherapist. It is especially well suited for the patients with severe paralysis to passively execute repetitive reaching missions along predefined training trajectories. However, it is a challenge to guarantee the position control accuracy during rehabilitation training due to the highly nonlinear properties and unexpected uncertainties of human-robot interaction. Different kinds of control algorithms have been developed to improve control performance of patient-passive training, including neural proportional-integral-derivative (PID) control [23], neural proportional-integral (PI) control [24], adaptive nonsingular terminal sliding mode control (SMC) [25], disturbance observer-based fuzzy control [26], neural-fuzzy adaptive control [27], adaptive backlash compensation control [28], and so on. Comparatively, the patient-cooperation training is applicable for the patients at the comparative recovery period, who have regained parts of motor functions. Clinical studies show that integrating the voluntary efforts of patients into rehabilitation training benefits to accelerate recovery progress and promote psychological confidence. Thus, patient-cooperation training should be able to regulate the human-robot interaction in accordance with the motion intentions and hemiplegia degrees of patients. Many patient-cooperation control strategies have been proposed, such as minimal assist-as-needed controller [29], myoelectric pattern recognition controller [30], electromyography (EMG)-based model predictive controllers [31], subject-adaptive controller [32], and fuzzy adaptive admittance controller [33].
Taking the above into consideration, the contribution of this paper is to develop an upper limb exoskeleton to assist the patient with motor disabilities perform multi-modal rehabilitation training. Firstly, the overall mechanical structure and the MATLAB/xPC-based real-time control system of the proposed therapeutic robot are introduced. Secondly, the dynamic modeling of the human-robot system is researched, and the dynamics parameters are obtained via virtual prototype and calibration experiments. After that, a multi-modal control strategy integrated with an adaptive sliding mode controller and a cascade-proportional-integral-derivative (CPID)-based impedance controller is proposed. The controller is combined with an audiovisual therapy interface and is able to realize patient-passive and patient-cooperation training based on the motor ability of patient. Finally, the effectiveness and feasibility of the developed rehabilitation exoskeleton system and control scheme are verified through three experiments conducted by several volunteers.[…]

Continue —> Sensors | Free Full-Text | Development, Dynamic Modeling, and Multi-Modal Control of a Therapeutic Exoskeleton for Upper Limb Rehabilitation Training | HTML

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Figure 1. Architecture and major components of the upper extremity rehabilitation exoskeleton. (a) Virtual prototype model. (b) Real-life picture of exoskeleton.

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[BLOG POST] Employment and People with Disabilities: A Global Perspective

Collection Spotlight from the National Rehabilitation Information Center

According to the International Labour Organization (ILO), people with disabilities make up around 15%, of the world’s population. Of that 15%, about 80% are of working age, or about 1 billion people. However, people with disabilities frequently face attitudinal, physical, and programmatic barriers to equal opportunities in employment. They not only experience higher rates of unemployment and economic inactivity in comparison to their contemporaries without disabilities; but they also are at a greater risk of insufficient social protection, which is key to reducing extreme poverty.

As we near the end of National Disability Employment Awareness Month (NDEAM), we would like to share with you about the ILO:

The ILO, an agency of the United Nations, aims to promote rights at work, encourage decent employment opportunities, enhance social protection, and strengthen dialogue on work-related issues for all people. The ILO is committed to promoting and achieving decent work…

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[WEB SITE] Stroke alert

Stroke alert

To mark the World Stroke Day observed on October 29, experts emphasise on the key issues related to it and the needs of stroke survivors and caregivers. Pioneer Health reports

What is stroke?

Stroke or brain attack is potentially life-threatening in which a part of the brain is deprived of adequate oxygen and energy. Stroke may be ischemic due to clotting in artery of brain that results in the brain damage, or it may be haemorrhagic due to tear in the wall of artery that results in bleeding in the brain. Nearly 80 per cent of all strokes are ischemic.

— Dr KM Hassan, Associate Director & Head, Department of Neurology,

Jaypee Hospital, Noida

How to Recognise?

Warming signs

  • Sudden weakness or numbness of face, arm or leg on one side of body
  • Sudden loss of vision— particularly in one eye
  • Sudden loss of speech or trouble talking or understanding speech
  • Sudden severe headaches
  • Sudden confusion
  • Sudden dizziness, unsteadiness or falls

The pneumonic — BEFAST — can help people to remember the symptoms:

B: Balance loss

E: vision loss in one or both Eyes

F: fascial tilt

A: Arm drift

S: Speech slurring or loss

F: time to act Fast.

People must be aware of these symptoms and caregivers need to be extra careful in recognising these.

—Dr Vinit Suri, senior consultant, neurology, Indraprastha Apollo Hospitals and President, Indian Stroke Association


Stroke is the third commonest cause of death worldwide and there is an increase in the number of stroke patients with disabilities every day.  India and China contribute to 40 per cent of world’s stroke patients. Increased prevalence of diabetes, high blood pressure, heart problems are directly contributing to the increased stroke prevalence.

Diet which is high in carbohydrates and fats add to the risk.  There is a pandemic of obesity in our country along with sleep apnea, which adds to the risk of stroke. It’s unfortunate to note that 20 per cent of stroke patients are less than 40 years of age. Stroke in young patients cause significant morbidity and has a huge impact  economically as well in the family.  This year, world stroke day 2018 is celebrated with the theme of “#up again after stroke” to reinstate hope among stroke survivors

Every minute after stroke 1.9 million brain cells (neurons) die and 84km of nerve fibers get permanently damaged.

— Dr Suryanarayana Sharma, consultant, neurologist & stroke specialist, head – division of Stroke & Neurosonology, BGS Gleneagles Global Hospitals

Nine preventive strategies

The nine preventive strategies for stroke include control of blood pressure, control of diabetes, controlling cholesterol levels, regular exercise, stopping smoking and tobacco chewing, reduction of body weight, appropriate diet modification, avoiding alcohol or drinking in moderation and controlling cardiac disease especially atrial fibrillation.

—Dr Suri

Treatable risk factors

  • High blood pressure: Increases the risk two fold. This is because it can narrow the blood vessels causing them to rupture or leak. It can also result in the formation of blood clots which further increase the risk of stroke.
  • Smoking: It is known to cause stroke as it leads to increased blood pressure which can cause the blood to clot and additionally builds up fatty substance in the main artery which provides blood to the brain.
  • Diabetes: Doubles the risk. High blood sugar in the blood can damage blood vessels making them harder, narrower and more likely to be blocked.
  • High Levels of cholesterol: Low-density lipoprotein cholesterol carries cholesterol through the blood which causes blockage. The build of plaque in the arteries makes it difficult for the blood to carry the oxygen to the brain.

  — Dr Rajesh Garg, director and HOD, Neurology, Fortis Hospital, Shalimar Bagh


Up to 85 per cent of all strokes are ischemic. For this, there is an option of intravenous medication called TPA (recombinant tissue plasminogen activator) available which can be given to the patient within first 3 to 4.5 hours of the symptom onset. The patients who have a blockage in a large blood vessel can be offered mechanical thrombectomy or ‘clot buster’ drug up to 24 hours, but sooner the better), which involves removing the blockage in the blood vessel and restoring the blood supply.  This procedure is done through a small nick in the groin. Trials have shown that patients do well post mechanical thrombectomy and have a greater chance to live independently.

— Dr Chandril Chugh, senior consultant & head – Interventional Neurology, Max Super Speciality Hospital, Saket

Understanding the golden hour

It is imperative for a stroke patient to get to the hospital in the ‘Golden Period’, that is within first 4.5 hours (the sooner the better). This is because clot busting medication will be effective if administered within 4.5 hours. For every minute in which the blood flow is not restored, nearly two million additional nerve cells die.It is important that patient should reach an equipped stroke centre as early as possible.

— Dr Garg

Caring for a stroke patient at home

  • A patient may undergo behavioural problems like depression. It is important to ensure that they feel  supported. Making them a part of a support group is one way to enable them to handle their emotions.
  • Motor-skill exercises can help improve their muscle strength.
  • Forced – use therapy: An unaffected limb is restrained while they practice moving the affected limb to help improve its function.
  • Range-of-motion therapy: Certain exercises and treatments can ease muscle tension and help them regain range of motion.
  • Functional electrical stimulation: Electricity is applied to weakened muscles, causing them to contract and may help re-educate muscles.
  • Robotic devices can assist impaired limbs with performing repetitive motions, helping the limbs to regain strength and function
  • Virtual reality: The use of video games and other computer-based therapies involves interacting with a simulated, real-time environment
  • Therapy for cognitive disorders: Occupational therapy and speech therapy can help them with lost cognitive abilities, such as memory, processing, problem-solving, social skills, judgment and safety awareness
  • Therapy for communication disorders: Speech therapy can help them regain lost abilities in speaking, listening and writing.
  • Treatments such as massage, herbal therapy, acupuncture and oxygen therapy are being evaluated

—Dr Garg


via Stroke alert

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[WEB SITE] Metabolic Rate-Reducing Exoskeleton Developed in Lab

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A team of scientists from the University of Tehran have developed an unpowered exoskeleton that they suggest could help reduce a runner’s metabolic rate. (Photo courtesy of Rezvan Nasiri)

A team of Iranian biomedical engineers has developed an unpowered exoskeleton that they suggest is able to reduce a wearer’s metabolic rate while running.

The research team is led by Rezvan Nasiri, and includes Arjang Ahmadi, and Majid Nili Ahmadabadi, all from the Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence at the School of Electrical and Computer Engineering at the University of Tehran, in Iran.

A study describing the exoskeleton’s development and testing is featured in IEEE Transactions on Neural Systems and Rehabilitation Engineering.

“The exoskeleton we developed reduces the metabolic rate by 8% during running by using a rotational spring system which couples two hips in the sagittal plane. The device takes advantage of the ‘scissor kick’ motion that occurs naturally during running – the reciprocal motion of the body recycles that energy, thereby allowing us to create an unpowered exoskeleton. No external battery is required, making the device lightweight and unobtrusive,” says lead researcher, Rezvan Nasiri, in a media release from IEEE EMBS.

“Users do not need to run at a constant speed to achieve metabolic rate reduction. We look forward to testing our device under a wide variety of settings in our future work.”

The exoskeleton the team developed was tested on 10 healthy active subjects for running at 2.5 meters per second. The team repeatedly achieved 8% metabolic rate reduction when compared to the subjects running at the same speed without wearing an exoskeleton. The exoskeleton is entirely human powered, and does not have any motors, electrical systems, or sensors. The research team project that by reducing the mass of their device, up to 10%, metabolic rate reduction is possible.

“This new research is special because it has been incredibly hard to reduce energy costs of a physically intact human by adding a device to their legs. Achieving this is a tremendous breakthrough in the field of human augmentation because humans are so incredibly good at minimizing metabolic energy cost during locomotion. Until now, no one has been able to add a device to humans that could reduce that metabolic energy expenditure for running,” states Dr Rodger Kram, associate professor emeritus of integrative physiology at the University of Colorado, Boulder.

“The beauty of this new device really lies in its simplicity – at less than 2 kilograms, it is lightweight, and requires no external power. It is portable, quiet, and not at all bulky. I’m impressed by this team’s achievement, which has big implications for the running industry. It will be exciting to follow the innovations that result from their work,” he adds.

“This team has developed a very simple, elegant solution that integrates almost seamlessly into the body’s natural movement,” comments Dr Greg Sawicki, an associate professor of mechanical engineering and biological sciences at Georgia Tech, and head of the Human Physiology of Wearable Robotics Lab, the release continues.

“The team in Iran has found a way to remap the structure of the musculoskeletal system with little more than a spring, essentially giving runners the equivalent of a new body part and an alternative pathway for exchanging energy. Their research has tremendous implications for our field, and I’m excited to see what develops as a result of this trailblazing work.”

[Source(s): IEEE EMBS, Business Wire]


via Metabolic Rate-Reducing Exoskeleton Developed in Lab – Rehab Managment

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