Posts Tagged Muscles

[Abstract + References] Multi-modal Intent Recognition Method for the Soft Hand Rehabilitation Exoskeleton


Stroke has become the second most disabling disease in the world. Due to the intensive demand for physical therapists and the severe dependence on hospitals, the cost for the treatment of stroke patients is huge. As the most flexible limb of the human body, the hand faces more severe challenges, which has a much lower degree of recovery than the upper and lower limbs. In the face of these challenges, a new treatment, exoskeleton-based rehabilitation, has demonstrated new vitality. This paper proposes a novel design of the soft hand exoskeleton based on bionics and anatomy and the exoskeleton could help the users bend and extend their fingers, which would greatly improve the motor ability of stroke patients. Through the control of the six drive motors, the exoskeleton could achieve most of the hand’s freedom of training. At the same time, we propose a multi-modal intent recognition method based on machine vision and machine speech. Under specific rehabilitation training scenarios, both healthy subjects and patients could complete grasping tasks in the wearing of the exoskeleton, overcoming potential security risks caused by misidentification due to using the single-modal intent understanding method.


1. M. P. Lindsay, B. Norrving, R. L. Sacco, M. Brainin, W. Hacke, S. Martins, et al., “World stroke organization (wso): Global stroke fact sheet 2019”, 2019.CrossRef  Google Scholar 

2. [online] Available: Show Context

3. K. B. Lee, S. H. Lim, K. H. Kim, K. J. Kim, Y. R. Kim, W. N. Chang, et al., “Six-month functional recovery of stroke patients: a multi-time-point study”, International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation, vol. 38, no. 2, pp. 173, 2015. Show Context CrossRef  Google Scholar 

4. P. Polygerinos, S. Lyne, Z. Wang, L. F. Nicolini, B. Mosadegh, G. M. Whitesides, et al., “Towards a soft pneumatic glove for hand rehabilitation”, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1512-1517, 2013. Show Context View Article Full Text: PDF (1334KB) Google Scholar 

5. J. Yi, X. Chen and Z. Wang, “A three-dimensional- printed soft robotic glove with enhanced ergonomics and force capability”, IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 242-248, 2017. Show Context View Article Full Text: PDF (676KB) Google Scholar 

6. N. Ho, K. Tong, X. Hu, K. Fung, X. Wei, W. Rong, et al., “An emg-driven exoskeleton hand robotic training device on chronic stroke subjects: task training system for stroke rehabilitation”, 2011 IEEE international conference on rehabilitation robotics, pp. 1-5, 2011. Show Context View Article Full Text: PDF (848KB) Google Scholar 

7. S. Park, L. Weber, L. Bishop, J. Stein and M. Ciocarlie, “Design and development of effective transmission mechanisms on a tendon driven hand orthosis for stroke patients”, 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2281-2287, 2018. Show Context View Article Full Text: PDF (2666KB) Google Scholar 

8. L. Gerez and M. Liarokapis, “An underactuated tendon-driven wearable exo-glove with a four-output differential mechanism”, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6224-6228, 2019. Show Context View Article Full Text: PDF (3125KB) Google Scholar 

9. T. Bützer, J. Dittli, J. Lieber, H. J. van Hedel, A. Meyer-Heim, O. Lambercy, et al., “Pexo- a pediatric whole hand exoskeleton for grasping assistance in task-oriented training”, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 108-114, 2019. Show Context View Article Full Text: PDF (1216KB) Google Scholar 

10. M. Mirakhorlo, N. Van Beek, M. Wesseling, H. Maas, H. Veeger and I. Jonkers, “A musculoskeletal model of the hand and wrist: model definition and evaluation”, Computer methods in biomechanics and biomedical engineering, vol. 21, no. 9, pp. 548-557, 2018. Show Context CrossRef  Google Scholar 

11. M. Suarez-Escobar and E. Rendon-Velez, “An overview of robotic/mechanical devices for post-stroke thumb rehabilitation”, Disability and Rehabilitation: Assistive Technology, vol. 13, no. 7, pp. 683-703, 2018. Show Context CrossRef  Google Scholar 

12. M. Li, Z. Liang, B. He, C.-G. Zhao, W. Yao, G. Xu, et al., “Attention-controlled assistive wrist rehabilitation using a low-cost eeg sensor”, IEEE Sensors Journal, vol. 19, no. 15, pp. 6497-6507, 2019. Show Context View Article Full Text: PDF (4401KB) Google Scholar 

13. D. Huggins-Daines, M. Kumar, A. Chan, A. W. Black, M. Ravishankar and A. I. Rudnicky, “Pocketsphinx: A free real-time continuous speech recognition system for hand-held devices”, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 1, pp. I-I, 2006. Show Context View Article Full Text: PDF (89KB) Google Scholar 

14. J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You only look once: Unified real-time object detection”, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016. Show Context View Article Full Text: PDF (1742KB) Google Scholar 


, , , , , , , , , , , , , ,

Leave a comment



This guide describes a sampling of these at-home biofeedback assistive technology (AT) devices that may help users better understand, interpret, and manage depressive effects that involve your brain, heart, and muscles. Biofeedback AT devices are designed to assist with monitoring and voluntarily controlling certain mental and physical functions such as increasing mental focus, regulating breathing, or relaxing muscles to get brainwaves, heartrate, and muscle tension levels back to normal intensities.

Download Full Text PDF

, , , , , , , , ,

Leave a comment

[ARTICLE] Self-Support Biofeedback Training for Recovery From Motor Impairment After Stroke – Full Text


Unilateral arm paralysis is a common symptom of stroke. In stroke patients, we observed that self-guided biomechanical support by the nonparetic arm unexpectedly triggered electromyographic activity with normal muscle synergies in the paretic arm. The muscle activities on the paretic arm became similar to the muscle activities on the nonparetic arm with self-supported exercises that were quantified by the similarity index (SI). Electromyogram (EMG) signals and functional near-infrared spectroscopy (fNIRS) of the patients (n=54) showed that self-supported exercise can have an immediate effect of improving the muscle activities by 40–80% according to SI quantification, and the muscle activities became much more similar to the muscle activities of the age-matched healthy subjects. Using this self-supported exercise, we investigated whether the recruitment of a patient’s contralesional nervous system could reactivate their ipsilesional neural circuits and stimulate functional recovery. We proposed biofeedback training with self-supported exercise where the muscle activities were visualized to encourage the appropriate neural pathways for activating the muscles of the paretic arm. We developed the biofeedback system and tested the recovery speed with the patients (n=27) for 2 months. The clinical tests showed that self-support-based biofeedback training improved SI approximately by 40%, Stroke Impairment Assessment Set (SIAS) by 35%, and Functional Independence Measure (FIM) by 20%.


Stroke is the leading cause of long-term disability worldwide. Of more than 750,000 stroke victims in the United States each year [1], approximately two-thirds survive and require immediate rehabilitation to recover lost brain functions [2]. These stroke rehabilitation programs, of which direct and indirect costs were estimated to be 73.7 billion dollars in 2010 [3], aim to help survivors gain physical independence and better quality of life.

Stroke damage typically interrupts blood flow within one brain hemisphere, resulting in unilateral motor deficits, sensory deficits, or both. The preservation of long-term neural and synaptic plasticity is essential for the functional reorganization and recovery of neural pathways disrupted by stroke [4]–[5][6]. Stroke survivors typically require long-term, intensive rehabilitation training due to the length of time required for these recovery processes [7], [8]. The typical time course for partial recovery of arm movement after mild to moderate unilateral stroke damage is 2 to 6 months, depending on the severity of tissue damage and the latency of treatment initiation [9], [10]; however, patients with severe damage require additional months to years of rehabilitation. Given the economic burden on patients’ families and the medical system, novel rehabilitation methods that promote rapid and complete functional recovery are needed, along with a better understanding of the functional mechanisms and neural circuits that can participate in potential therapeutic processes. The identification of rehabilitation methods that can more effectively recover brain functions in the damaged hemisphere by re-engaging dormant motor functions should be a major global objective, from both economic and societal perspectives. Such an objective would require the interface of biology, medical research, and clinical practice [4].

Recently, candidate brain areas that become activated during stroke recovery have been identified in patients and animal models [7]. Brain imaging studies during stroke recovery suggest that the extent of functional motor recovery is associated with an increase in neuronal activity in the sensorimotor cortex of the ipsilesional hemisphere [10]–[11][12]. Other work has suggested that repetitive sensorimotor tasks may promote cortical reorganization and functional recovery in the ipsilesional area by increasing bilateral cortical activity to enhance neuroplasticity [13]. Activation in the contralesional hemisphere is also observed in the early stages of post-stroke patients. This activation has been explained by the emergence of communication in corticospinal projections that are silent in the healthy state [11], and it may also contribute to movement-related neural activity on the ipsilesional limb [14], [15]. Functional brain imaging studies show that activity of the contralesional hemisphere is increased early after stroke and gradually declines as recovery progresses [16]. The functional relevance of contralesional recruitment remains unclear [17], [18]. Some reported studies have linked high abnormal activity to a high inhibitory signaling drive onto the ipsilesional cortex [19], which may be a major contributor to motor impairment [6], [20]. Recent studies have also investigated the benefits of activating the contralesional and/or ipsilesional hemispheres in functional motor recovery using brain-computer interface (BCI) and transcranial magnetic stimulation (TMS) therapies [21], [22].

Current stroke rehabilitation approaches have largely focused on paretic limb rehabilitation interventions such as muscle strengthening and endurance training [23], forced-use therapy [24], constraint-induced exercise [25], robot therapy with biofeedback [26], nonparetic limb interventions (e.g., mirror-therapy [27], [28]), or bilateral/bimanual training [29], [30]. However, to date, none have clearly investigated how the use of a patient’s unaffected neural circuits in the healthy cortical hemisphere, or in the local peripheral circuit, affect the impaired limb in terms of functional rehabilitation of the bilateral cortical sensorimotor network [31].

In this study, we investigated a motor recovery approach for post-stroke unilateral arm impairment that combined sensory feedback, motor control, and motor intention. While observing a patient cohort with unilateral stroke damage and arm movement impairment, we found that a specific self-guided motion, which we termed self-supported exercise, surprisingly reactivated a healthy muscles pattern in the paretic arm. The key of the self-supported exercise is use of the nonparetic arm as a support to help move the paretic arm. First, we will show the observation of appropriate muscle recruitment and reduction of abnormal muscle synergies for post-stroke patients during the self-supported exercise, which are a common problem in stroke recovery [32]. Then, we conduct the experiments of functional imaging and electromyography recordings and characterized the neurobiology and physiology of this self-supported exercise. Based on this mechanism, we designed a rehabilitation program involving biofeedback-aided self-supported exercises that employ a patients’ self-initiated motor intention. The results of the comparative experiments between the feedback training cohorts and the control cohorts show that this method results in efficient recovery from post-stroke motion paralysis. Finally, we discuss the significance of our findings for the design of biologically-based stroke rehabilitation.[…]

via Self-Support Biofeedback Training for Recovery From Motor Impairment After Stroke – IEEE Journals & Magazine

FIGURE 2. - The four types of exercises.

FIGURE 2.The four types of exercises.




, , , , , , , , , , ,

Leave a comment

[Abstract] Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings


Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation of the wrist and fingers joint, an electromechanical exoskeleton was developed that simultaneously moves the wrist and metacarpophalangeal joints.
The device was designed for the ease of manufacturing and maintenance, with specific considerations for countries with limited resources. Active participation of the user is ensured by the implementation of electromyographic control and visual feedback of performance. Muscle activity requirements, movement parameters, range of motion, and speed of the device can all be customized to meet the needs of the user.
Twelve stroke survivors, ranging from the subacute to chronic phases of recovery (mean 10.6 months post-stroke) participated in a pilot study with the device. Participants completed 20 sessions, each lasting 45 minutes. Overall, subjects exhibited statistically significant changes (p < 0.05) in clinical outcome measures following the treatment, with the Fugl-Meyer Stroke Assessment score for the upper extremity increasing from 36 to 50 and the Barthel Index increasing from 74 to 89. Active range of wrist motion increased by 190 while spasticity decreased from 1.75 to 1.29 on the Modified Ashworth Scale.
Thus, this device shows promise for improving rehabilitation outcomes, especially for patients in countries with limited resources.

via Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings – IEEE Journals & Magazine

, , , , , , , , , , ,

Leave a comment

[Abstract + References] Upper Limb Rehabilitation Therapies Based in Videogames Technology Review


Worldwide, stroke is the third cause of physical disability, rehabilitation therapy is a main topic of focus for the recovery of life quality. Rehabilitation of these patients presents great challenges since many of them do not find the motivation to perform the necessary exercises, or do not have the economic resources or the adequate support to receive physiotherapy. For several years now, an alternative that has been in development is game-based rehabilitation, since this could be used in a hospital environment and eventually at patients home. The aim of this review is to present the advances in videogames technology to be used for rehabilitation and training purposes- in preparation for prosthetics fitting or Neuroprosthesis control training–, as well as the devices that are being used to make this alternative more tangible. Videogames technology rehabilitation still has several challenges to work on, more research and development of platforms to have a larger variety of games to engage with different age-range patients is still necessary.
1. Y. X. Hung , P. C. Huang , K. T. Chen , and W. C. Chu , “ What do stroke patients look for in game-based rehabilitation: A survey study ,” Med. (United States) , vol. 95 , no. 11 , pp. 1 – 10 , 2016 .

2. E. Vogiatzaki , Y. Gravezas , N. Dalezios , D. Biswas , A. Cranny , and S. Ortmann , “ Telemedicine System for Game-Based Rehabilitation of Stroke Patients in the FP7- ‘ StrokeBack ’ Project ,” 2014 .

3. W. Johnson , O. Onuma , and S. Sachdev , “ Stroke: a global response is needed ,” Bull. World Heal. Organ ., vol. 94 p. 634 – 634A , 2016 .

4. A. Tabor , S. Bateman , E. Scheme , D. R. Flatla , and K. Gerling , “ Designing Game-Based Myoelectric Prosthesis Training ,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems – CHI ’17 , 2017 , pp. 1352 – 1363 .

5. B. Lange et al. , “ Interactive game-based rehabilitation using the Microsoft Kinect ,” Proc. – IEEE Virtual Real ., no. November 2016 , pp. 171 – 172 , 2012 .

6. C. Prahm , I. Vujaklija , F. Kayali , P. Purgathofer , and O. C. Aszmann , “ Game-Based Rehabilitation for Myoelectric Prosthesis Control ,” JMIR Serious Games , vol. 5 , no. 1 , pp. 1 – 13 , 2017 .

7. B. D. Winslow , M. Ruble , and Z. Huber , “ Mobile, Game-Based Training for Myoelectric Prosthesis Control ,” Front. Bioeng. Biotechnol .,vol. 6 , no. July , pp. 1 – 8 , 2018 .

8. “ The SENIAM Project ,” 2019 . [Online]. Available: . [Accessed: 21-Jan-2019 ].

9. M. B. I. Reaz , M. S. Hussain , and F. Mohd-Yasin , “ Techniques of EMG signal analysis: Detection, processing, classification and applications ,” Biol. Proced. Online , vol. 8 , no. 1 , pp. 11 – 35 , 2006 .

10. R. S. Armiger and R. J. Vogelstein , “ Air-Guitar Hero: A real-time video game interface for training and evaluation of dexterous upper-extremity neuroprosthetic control algorithms ,” Circuits Syst. Conf. BIOCAS 2008 , pp. 121 – 124 , 2008 .

11. H. Oppenheim , R. S. Armiger , and R. J. Vogelstein , “ WiiEMG: A real-time environment for control of the Wii with surface electromyography ,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems , 2010 , pp. 957 – 960 .

12. G. I. Yatar and S. A. Yildirim , “ Wii Fit balance training or progressive balance training in patients with chronic stroke: a randomised controlled trial ,” J. Phys. Ther. Sci ., vol. 27 , no. 4 , pp. 1145 – 1151 , 2015 .

13. N. Norouzi-Gheidari , M. F. Levin , J. Fung , and P. Archambault , “ Interactive virtual reality game-based rehabilitation for stroke patients ,” in 2013 International Conference on Virtual Rehabilitation, ICVR 2013 2013 .

14. B. Lange , C. Chang , E. Suma , B. Newman , A. S. Rizzo , and M. Bolas , “ Development and Evaluation of Low Cost Game-Based Balance Rehabilitation Tool Using the Microsoft Kinect Sensor ,” 2011 , pp. 1831 – 1834 .

15. Y. Chen et al. , “ Game Analysis, Validation, and Potential Application of EyeToy Play and Play 2 to Upper-Extremity Rehabilitation ,” no. December , 2014 .

16. P. Visconti , F. Gaetani , G. A. Zappatore , and P. Primiceri , “ Technical features and functionalities of Myo armband: An overview on related literature and advanced applications of myoelectric armbands mainly focused on arm prostheses ,” Int. J Smart Sens. Intell. Syst ., vol. 11 , no. 1 , pp. 1 – 25 , 2018 .

17. S. S. Esfahlani and G. Wilson , “ Development of Rehabilitation System (ReHabgame) through Monte-Carlo Tree Search Algorithm ,” 2018 , pp. 1 – 8 .

18. “ Welcome to Myo Support ,” 2019 . [Online]. Available: [Accessed: 19-Jan-2019 ].

19. “ PAULA 1.2 | Myo Software | Myo Hands and Components |Upper Limb Prosthetics | Prosthetics | Ottobock US Healthcare .”[Online]. Available: [Accessed: 21-Jan-2019 ].

20. J. Lewis , P. Merritt , M. Bowler , and D. Brown , “ Evaluation of the suitability of games based stroke rehabilitation using the Novint Falcon ,” 2018 , no. August .

21. G. Ghazaei , A. Alameer , P. Degenaar , G. Morgan , and K. Nazarpour , “ Deep learning-based artificial vision for grasp classification in myoelectric hands ,” J. Neural Eng ., vol. 14 , no. 3 , 2017 .

22. B. Terlaak , H. Bouwsema , C. K. V. D. Sluis , and R. M. Bongers , “ Virtual training of the myosignal ,” PLoS One , vol. 10 , no. 9 , 2015 .

23. J. W. Burke , M. D. J. McNeill , D. K. Charles , P. J. Morrow , J. H. Crosbie , and S. M. McDonough , “ Designing Engaging, Playable Games for Rehabilitation ,” in 8th International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT) , 2010 , pp. 195 – 201 .


via Upper Limb Rehabilitation Therapies Based in Videogames Technology Review – IEEE Conference Publication

, , , , , , , , , ,

Leave a comment

[Abstract + References] Electromyographic indices of muscle fatigue of a severely paralyzed chronic stroke patient undergoing upper limb motor rehabilitation


Modern approaches to motor rehabilitation of severe upper limb paralysis in chronic stroke decode movements from electromyography for controlling rehabilitation orthoses. Muscle fatigue is a phenomenon that influences these neurophysiological signals and may diminish the decoding quality. Characterization of these potential signal changes during movement patterns of rehabilitation training could therefore help improve the decoding accuracy. In the present work we investigated how electromyographic indices of muscle fatigue in the Deltoid Anterior muscle evolve during typical forward reaching movements of a rehabilitation training in healthy subjects and a stroke patient. We found that muscle fatigue in healthy subjects changed the neurophysiological signal. In the patient, however, no consistent change was observed over several sessions.
1. V. L. Feigin , B. Norrving , M. G. George , J. L. Foltz , A. Roth Gregory , and G. A. Mensah , “Prevention of stroke: a strategic global imperative,” Nat. Rev. Neurol., vol. 107, pp. 501–512, 2016.

2. A. Ramos-Murguialday et al , “Brain-machine interface in chronic stroke rehabilitation: a controlled study,” Ann. Neurol., vol. 74, no. 1, pp. 100–108, 2013.

3. A. Sarasola-Sanz et al , “A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients,” IEEE Int Conf Rehabil Robot, vol. 2017, pp. 895–900, Jul. 2017.

4. R. M. Enoka and J. Duchateau , “Muscle fatigue: what, why and how it influences muscle function,” J Physiol, vol. 586, no. 1, pp. 11–23, Jan. 2008.

5. M. González-Izal , A. Malanda , E. Gorostiaga , and M. Izquierdo , “Electromyographic models to assess muscle fatigue,” J. Electromyogr. Kinesiol., vol. 22, no. 4, pp. 501–512, Aug. 2012.

6. A. Sarasola Sanz et al , “EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies,” in 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), 2015.

7. P. V. Komi and P. Tesch , “EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man,” Eur. J Appl Physiol, vol. 42, no. 1, pp. 41–50, Sep. 1979.

8. D. R. Rogers and D. T. MacIsaac , “A comparison of EMG-based muscle fatigue assessments during dynamic contractions,” J. Electromyogr. Kinesiol., vol. 23, no. 5, pp. 1004–1011, Oct. 2013.

9. B. Bigland-Ritchie , E. F. Donovan , and C. S. Roussos , “Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts,” J Appl Physiol Respir Env. Exerc Physiol, vol. 51, no. 5, pp. 1300–1305, Nov. 1981.

10. G. V. Dimitrov , T. I. Arabadzhiev , K. N. Mileva , J. L. Bowtell , N. Crichton , and N. A. Dimitrova , “Muscle Fatigue during Dynamic Contractions Assessed by New Spectral Indices,” Med. Sci. Sports Exerc., 2006.

11. N. A. Riley and M. Bilodeau , “Changes in upper limb joint torque patterns and EMG signals with fatigue following a stroke,” Disabil Rehabil, vol. 24, no. 18, pp. 961–969, Dec. 2002.

12. M. J. Campbell , A. J. McComas , and F. Petito , “Physiological changes in ageing muscles,” J. Neurol. Neurosurg. Psychiatry, vol. 36, no. 2, pp. 174–182, 1973.


via Electromyographic indices of muscle fatigue of a severely paralyzed chronic stroke patient undergoing upper limb motor rehabilitation – IEEE Conference Publication

, , , , , , , , , , ,

Leave a comment

[Abstract + References] Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions


Functional electrical stimulation (FES) is capable of activating muscles that are under-recruited in neurological diseases, such as stroke. Therefore, FES provides a promising technology for assisting upper-limb motor functions in rehabilitation following stroke. However, the full benefits of FES may be limited due to lack of a systematic approach to formulate the pattern of stimulation. Our preliminary work demonstrated that it is feasible to use muscle synergy to guide the generation of FES patterns.In this paper, we present a methodology of formulating FES patterns based on muscle synergies of a normal subject using a programmable multi-channel FES device. The effectiveness of the synergy-based FES was tested in two sets of experiments. In experiment one, the instantaneous effects of FES to improve movement kinematics were tested in three patients post ischemic stroke. Patients performed frontal reaching and lateral reaching tasks, which involved coordinated movements in the elbow and shoulder joints. The FES pattern was adjusted in amplitude and time profile for each subject in each task. In experiment two, a 5-day session of intervention using synergy-based FES was delivered to another three patients, in which patients performed task-oriented training in the same reaching movements in one-hour-per-day dose. The outcome of the short-term intervention was measured by changes in Fugl–Meyer scores and movement kinematics. Results on instantaneous effects showed that FES assistance was effective to increase the peak hand velocity in both or one of the tasks. In short-term intervention, evaluations prior to and post intervention showed improvements in both Fugl–Meyer scores and movement kinematics. The muscle synergy of patients also tended to evolve towards that of the normal subject. These results provide promising evidence of benefits using synergy-based FES for upper-limb rehabilitation following stroke. This is the first step towards a clinical protocol of applying FES as therapeutic intervention in stroke rehabilitation.

I. Introduction

Muscle activation during movement is commonly disrupted due to neural injuries from stroke. A major challenge for stroke rehabilitation is to re-establish the normal ways of muscle activation through a general restoration of motor control, otherwise impairments may be compensated by the motor system through a substitution strategy of task control [1]. In post-stroke intervention, new technologies such as neuromuscular electrical stimulation (NMES) or functional electrical stimulation (FES) offer advantages for non-invasively targeting specific groups of muscles [2]–[4] to restore the pattern of muscle activation. Nevertheless, their effectiveness is limited by lack of a systematic methodology to optimize the stimulation pattern, to implement the optimal strategy in clinical settings, and to design a protocol of training towards the goal of restoring motor functions. This pioneer study addresses these issues in clinical application with a non-invasive FES technology.

Sign in to Continue Reading

1. M. F. Levin, J. A. Kleim, and S. L. Wolf, “What do motor ‘recovery’ and ‘compensation’ mean in patients following stroke?” Neurorehabilitation Neural Repair, vol. 23, no. 4, pp. 313–319, 2008.

2. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation (FES) may modify the poor prognosis of stroke survivors with severe motor loss of the upper extremity: A preliminary study,” Amer. J. Phys. Med. Rehabil., vol. 87, no. 8, pp. 627–636, 2008.

3. W. Rong, “A neuromuscular electrical stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke,” J. Neuroeng. Rehabil., vol. 14, no. 1, p. 34, Dec. 2017.

4. J. J. Daly, “Recovery of coordinated gait: Randomized controlled stroke trial of functional electrical stimulation (FES) versus no FES, with weight-supported treadmill and over-ground training,” Neurorehabilitation Neural Repair, vol. 25, no. 7, pp. 588–596, Sep. 2011.

5. R. Nataraj, M. L. Audu, R. F. Kirsch, and R. J. Triolo, “Comprehensive joint feedback control for standing by functional neuromuscular stimulation—A simulation study,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18, no. 6, pp. 646–657, Dec. 2010.

6. R. Nataraj, M. L. Audu, and R. J. Triolo, “Restoring standing capabilities with feedback control of functional neuromuscular stimulation following spinal cord injury,” Med. Eng. Phys., vol. 42, pp. 13–25, Apr. 2017.

7. H. Rouhani, M. Same, K. Masani, Y. Q. Li, and M. R. Popovic, “PID controller design for FES applied to ankle muscles in neuroprosthesis for standing balance,” Frontiers Neurosci., vol. 11, p. 347, Jun. 2017.

8. V. K. Mushahwar, P. L. Jacobs, R. A. Normann, R. J. Triolo, and N. Kleitman, “New functional electrical stimulation approaches to standing and walking,” J. Neural Eng., vol. 4, no. 3, pp. S181–S197, Sep. 2007.

9. B. J. Holinski, “Intraspinal microstimulation produces over-ground walking in anesthetized cats,” J. Neural Eng., vol. 13, no. 5, p. 056016, Oct. 2016.

10. M. B. Popovic, D. B. Popovic, T. Sinkjær, A. Stefanovic, and L. Schwirtlich, “Restitution of reaching and grasping promoted by functional electrical therapy,” Artif. Organs, vol. 26, no. 3, pp. 271–275, Mar. 2002.

11. A. B. Ajiboye, “Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration,” Lancet Lond. Engl., vol. 389, no. 10081, pp. 1821–1830, May 2017.

12. J. H. Grill and P. H. Peckham, “Functional neuromuscular stimulation for combined control of elbow extension and hand grasp in C5 and C6 quadriplegics,” IEEE Trans. Rehabil. Eng., vol. 6, no. 2, pp. 190–199, Jun. 1998.

13. M. R. Popovic, T. A. Thrasher, M. E. Adams, V. Takes, V. Zivanovic, and M. I. Tonack, “Functional electrical therapy: Retraining grasping in spinal cord injury,” Spinal Cord, vol. 44, no. 3, pp. 143–151, Mar. 2006.

14. C. Ethier, E. R. Oby, M. J. Bauman, and L. E. Miller, “Restoration of grasp following paralysis through brain-controlled stimulation of muscles,” Nature, vol. 485, no. 7398, pp. 368–371, May 2012.

15. G. Alon, “Use of neuromuscular electrical stimulation in neureorehabilitation: A challenge to all,” J. Rehabil. Res. Develop., vol. 40, no. 6, pp. 9–12, Dec. 2003.

16. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation enhancement of upper extremity functional recovery during stroke rehabilitation: A pilot study,” Neurorehabilitation Neural Repair, vol. 21, no. 3, pp. 207–215, Jun. 2007.

17. C. Church, C. Price, A. D. Pandyan, S. Huntley, R. Curless, and H. Rodgers, “Randomized controlled trial to evaluate the effect of surface neuromuscular electrical stimulation to the shoulder after acute stroke,” Stroke, vol. 37, no. 12, pp. 2995–3001, Dec. 2006.

18. J. H. Cauraugh and S. B. Kim, “Chronic stroke motor recovery: Duration of active neuromuscular stimulation,” J. Neurolog. Sci., vol. 215, nos. 1–2, pp. 13–19, Nov. 2003.

19. S. Ferrante, T. Schauer, G. Ferrigno, J. Raisch, and F. Molteni, “The effect of using variable frequency trains during functional electrical stimulation cycling,” Neuromodulation, Technol. Neural Interface, vol. 11, no. 3, pp. 216–226, Jul. 2008.

20. R. W. Fields, “Electromyographically triggered electric muscle stimulation for chronic hemiplegia,” Arch. Phys. Med. Rehabil., vol. 68, no. 7, pp. 407–414, Jul. 1987.

21. G. H. Kraft, S. S. Fitts, and M. C. Hammond, “Techniques to improve function of the arm and hand in chronic hemiplegia,” Arch. Phys. Med. Rehabil., vol. 73, no. 3, pp. 220–227, Mar. 1992.

22. G. van Overeem Hansen, “EMG-controlled functional electrical stimulation of the paretic hand,” Scand. J. Rehabil. Med., vol. 11, no. 4, pp. 189–193, 1979.

23. J. H. Cauraugh, S. B. Kim, and A. Duley, “Coupled bilateral movements and active neuromuscular stimulation: Intralimb transfer evidence during bimanual aiming,” Neurosci. Lett., vol. 382, nos. 1–2, pp. 39–44, Jul. 2005.

24. J. S. Knutson, D. D. Gunzler, R. D. Wilson, and J. Chae, “Contralaterally controlled functional electrical stimulation improves hand dexterity in chronic hemiparesis: A randomized trial,” Stroke, vol. 47, no. 10, pp. 2596–2602, Oct. 2016.

25. D. A. E. Bolton, J. H. Cauraugh, and H. A. Hausenblas, “Electromyogram-triggered neuromuscular stimulation and stroke motor recovery of arm/hand functions: A meta-analysis,” J. Neurol. Sci., vol. 223, no. 2, pp. 121–127, Aug. 2004.

26. M. K.-L. Chan, R. K.-Y. Tong, and K. Y.-W. Chung, “Bilateral upper limb training with functional electric stimulation in patients with chronic stroke,” Neurorehabilitation Neural Repair, vol. 23, no. 4, pp. 357–365, May 2009.

27. J. B. Manigandan, G. S. Ganesh, M. Pattnaik, and P. Mohanty, “Effect of electrical stimulation to long head of biceps in reducing gleno humeral subluxation after stroke,” Neuro Rehabil., vol. 34, no. 2, pp. 245–252, 2014.

28. S. Li, C. Zhuang, C. M. Niu, Y. Bao, Q. Xie, and N. Lan, “Evaluation of functional correlation of task-specific muscle synergies with motor performance in patients poststroke,” Frontiers Neurol., vol. 8, p. 337, Jul. 2017.

29. A. d’Avella, P. Saltiel, and E. Bizzi, “Combinations of muscle synergies in the construction of a natural motor behavior,” Nature Neurosci., vol. 6, no. 3, pp. 300–308, Mar. 2003.

30. V. C. K. Cheung, “Muscle synergy patterns as physiological markers of motor cortical damage,” Proc. Nat. Acad. Sci. USA, vol. 109, no. 36, pp. 14652–14656, Sep. 2012.

31. D. J. Clark, L. H. Ting, F. E. Zajac, R. R. Neptune, and S. A. Kautz, “Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke,” J. Neurophysiol., vol. 103, no. 2, pp. 844–857, Feb. 2010.

32. E. Ambrosini, “Neuro-mechanics of recumbent leg cycling in post-acute stroke patients,” Ann. Biomed. Eng., vol. 44, pp. 3238–3251, Jun. 2016.

33. C. Zhuang, J. C. Marquez, H. E. Qu, X. He, and N. Lan, “A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation,” in Proc. IEEE 7th Int./EMBS Conf. Neural Eng. (NER), vol. 15, Apr. 2015, pp. 816–819.

34. R. S. Razavian, B. Ghannadi, N. Mehrabi, M. Charlet, and J. McPhee, “Feedback control of functional electrical stimulation for 2-D arm reaching movements,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26, no. 10, pp. 2033–2043, Oct. 2018.

35. C. M. Niu, C. Zhuang, Y. Bao, S. Li, N. Lan, and Q. Xie, “Synergy-based NMES intervention accelerated rehabilitation of post-stroke hemiparesis,” in Proc. Assoc. Acad. Physiatrists Annu. Conf., Las Vegas, NV, USA, 2017.

36. H. Qu, “Development of network-based multichannel neuromuscular electrical stimulation system for stroke rehabilitation,” J. Rehabil. Res. Develop., vol. 52, no. 3, pp. 263–278, 2016.

37. C. M. Niu, “Effectiveness of short-term training with a synergy-based FES paradigm on motor function recovery post stroke,” in Proc. 12th Int. Soc. Phys. Rehabil. Med. World Congr., Paris, France, 2018.

38. T. Wang, “Customization of synergy-based FES for post-stroke rehabilitation of upper-limb motor functions,” in Proc. IEEE 40th Annu. Int. Conf. Eng. Med. Biol. Soc. (EMBS), Jul. 2018, 3541–3544.

39. L. L. Baker, D. R. McNeal, L. A. Benton, B. R. Bowman, and R. L. Waters, Ed., Neuromuscular Electrical Stimulation a Practical Guide, 4th ed. Downey, CA, USA: Los Amigos Research & Education Institute, 2000.

40. A. d’Avella, A. Portone, L. Fernandez, and F. Lacquaniti, “Control of fast-reaching movements by muscle synergy combinations.,” J. Neurosci., vol. 26, no. 30, pp. 7791–7810, Jul. 2006.

41. R. D. Wilson, “Upper-limb recovery after stroke: A randomized controlled trial comparing EMG-triggered, cyclic, and sensory electrical stimulation,” Neurorehabilitation Neural Repair, vol. 30, no. 10, pp. 978–987, Nov. 2016.

42. A. J. Levine, “Identification of a cellular node for motor control pathways,” Nature Neurosci., vol. 17, no. 4, pp. 586–593, Apr. 2014.

43. S. B. Frost, S. Barbay, K. M. Friel, E. J. Plautz, and R. J. Nudo, “Reorganization of remote cortical regions after ischemic brain injury: A potential substrate for stroke recovery,” J. Neurophysiol., vol. 89, no. 6, pp. 3205–3214, Jun. 2003.

44. P. Langhorne, J. Bernhardt, and G. Kwakkel, “Stroke rehabilitation,” Lancet, vol. 377, no. 9778, pp. 1693–1702, May 2011.

45. M. D. Ellis, B. G. Holubar, A. M. Acosta, R. F. Beer, and J. P. A. Dewald, “Modifiability of abnormal isometric elbow and shoulder joint torque coupling after stroke,” Muscle Nerve, vol. 32, pp. 170–178, Aug. 2005.

46. J. P. A. Dewald, P. S. Pope, J. D. Given, T. S. Buchanan, and W. Z. Rymer, “Abnormal muscle coactivation patterns during isometric torque generation at the elbow and shoulder in hemiparetic subjects,” Brain, vol. 118, no. 2, pp. 495–510, 1995.

47. D. G. Kamper, A. N. McKenna-Cole, L. E. Kahn, and D. J. Reinkensmeyer, “Alterations in reaching after stroke and their relation to movement direction and impairment severity,” Arch. Phys. Med. Rehabil., vol. 83, no. 5, pp. 702–707, May 2002.

48. C. L. Massie, S. Fritz, and M. P. Malcolm, “Elbow extension predicts motor impairment and performance after stroke,” Rehabil. Res. Pract., vol. 2011, pp. 1–7, 2011.

49. V. C. K. Cheung, L. Piron, M. Agostini, S. Silvoni, A. Turolla, and E. Bizzi, “Stability of muscle synergies for voluntary actions after cortical stroke in humans,” Proc. Nat. Acad. Sci. USA, vol. 106, no. 46, pp. 19563–19568, Nov. 2009.

50. J. Roh, W. Z. Rymer, and R. F. Beer, “Robustness of muscle synergies underlying three-dimensional force generation at the hand in healthy humans,” J. Neurophysiol., vol. 107, no. 8, pp. 2123–2142, Apr. 2012.

51. J. Roh, W. Z. Rymer, and R. F. Beer, “Evidence for altered upper extremity muscle synergies in chronic stroke survivors with mild and moderate impairment,” Frontiers Hum. Neurosci., vol. 9, p. 6, Feb. 2015.

52. J. Roh, W. Z. Rymer, E. J. Perreault, S. B. Yoo, and R. F. Beer, “Alterations in upper limb muscle synergy structure in chronic stroke survivors,” J. Neurophysiol., vol. 109, no. 3, pp. 768–781, Feb. 2013.

53. W. H. Backes, W. H. Mess, V. van Kranen-Mastenbroek, and J. P. H. Reulen, “Somatosensory cortex responses to median nerve stimulation: fMRI effects of current amplitude and selective attention,” Clin. Neurophysiol., vol. 111, no. 10, pp. 1738–1744, Oct. 2000.

54. G. Francisco, “Electromyogram-triggered neuromuscular stimulation for improving the arm function of acute stroke survivors: A randomized pilot study,” Arch. Phys. Med. Rehabil., vol. 79, no. 5, pp. 570–575, May 1998.

55. S. K. Sabut, C. Sikdar, R. Kumar, and M. Mahadevappa, “Functional electrical stimulation of dorsiflexor muscle: Effects on dorsiflexor strength, plantarflexor spasticity, and motor recovery in stroke patients,” Neurorehabilitation, vol. 29, no. 4, pp. 393–400, 2011.

56. Y.-H. Wang, F. Meng, Y. Zhang, M.-Y. Xu, and S.-W. Yue, “Full-movement neuromuscular electrical stimulation improves plantar flexor spasticity and ankle active dorsiflexion in stroke patients: A randomized controlled study,” Clin. Rehabil., vol. 30, no. 6, pp. 577–586, Jun. 2016.

57. W. H. Chang and Y.-H. Kim, “Robot-assisted therapy in stroke rehabilitation,” J. Stroke, vol. 15, no. 3, p. 174, 2013.

58. H. G. Wu, Y. R. Miyamoto, L. N. G. Castro, B. P. Ölveczky, and M. A. Smith, “Temporal structure of motor variability is dynamically regulated and predicts motor learning ability,” Nature Neurosci., vol. 17, no. 2, pp. 312–321, Jan. 2014.

59. J. Frère and F. Hug, “Between-subject variability of muscle synergies during a complex motor skill,” Frontiers Comput. Neurosci., vol. 6, p. 99, Dec. 2012.

60. S. Muceli, A. T. Boye, A. d’Avella, and D. Farina, “Identifying representative synergy matrices for describing muscular activation patterns during multidirectional reaching in the horizontal plane,” J. Neurophysiol., vol. 103, no. 3, pp. 1532–1542, Mar. 2010.

61. J. F. Soechting and F. Lacquaniti, “Invariant characteristics of a pointing movement in man,” J. Neurosci., vol. 1, no. 7, pp. 710–720, Jul. 1981.

62. B. Cesqui, A. d’Avella, A. Portone, and F. Lacquaniti, “Catching a ball at the right time and place: Individual factors matter,” PLoS ONE, vol. 7, no. 2, p. e31770, Feb. 2012.


via Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions – IEEE Journals & Magazine

, , , , , , , , , , , ,

Leave a comment

[BLOG POST] Antidepressants help us understand why we get fatigued during exercise

In general, the term ‘fatigue’ is used to describe any exercise-induced decline in the ability of a muscle to generate force. To identify the causes of fatigue, it is common to examine two divisions of the body that might be affected during exercise. The central component of fatigue includes the many nerves that travel throughout the brain to the spinal cord. The peripheral component predominantly reflects elements in the muscle itself. If there is a problem with either of these components, the ability to contract a muscle might be compromised. For many years, there has been suggestion that central fatigue is heavily influenced by neurotransmitters that get released in the central nervous system (such as dopamine and serotonin). However, little research has been performed in this area.

Serotonin is a chemical that can improve mood, and increasing the amount of serotonin that circulates in the brain is a common therapy for depression. However, serotonin also plays a vital role in activating neurons in the spinal cord which tell the muscle to contract. With the correct amount of serotonin release, a muscle will activate efficiently. However, if too much serotonin is released, there is a possibility that the muscle will rapidly fatigue. Recent animal studies indicate that moderate amounts of serotonin release, which are common during exercise, can promote muscle contractions (Cotel et al. 2013). However, massive serotonin release, which may occur with very large bouts of exercise, could further exacerbate the already fatigued muscle (Perrier et al. 2018).

Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed antidepressants. These medications keep serotonin levels high in the central nervous system by stopping the chemical from being reabsorbed by nerves (reuptake inhibition). Instead of using SSRIs to relieve symptoms of depression, we used them in our recent study (Kavanagh et al. 2019) to elevate serotonin in the central nervous system, and then determine if characteristics of fatigue are enhanced when serotonin is elevated. We performed three experiments that used maximal voluntary contractions of the biceps muscle to cause fatigue in healthy young individuals. Our main goal was to determine if excessive serotonin limits the amount of exercise that can be performed, and then determine which central or peripheral component was compromised by excessive serotonin.


Given that SSRIs influence neurotransmitters in the central nervous system, it was not surprising that peripheral fatigue was unaltered by the medication. However, central fatigue was influenced with enhanced serotonin. The time that a maximum voluntary contraction could be held was reduced with enhanced serotonin, whereby the ability of the central nervous system to drive the muscle was compromised by 2-5%. We further explored the location of dysfunction and found that the neurons in the spinal cord that activate the muscle were 4-18% less excitable when fatiguing contractions were performed in the presence of enhanced serotonin.


The central nervous system is diverse, and the fatigue that is experienced during exercise is not just restricted to the brain. Instead, the spinal cord plays an integral role in activating muscles, and mechanisms of fatigue also occur in these lower, often overlooked, neural circuits. This is the first study to provide evidence that serotonin released onto the motoneurones contributes to central fatigue in humans.


Kavanagh JJ, McFarland AJ, Taylor JL. Enhanced availability of serotonin increases activation of unfatigued muscle but exacerbates central fatigue during prolonged sustained contractions. J Physiol. 597:319-332, 2019.

If you cannot access the paper, please click here to request a copy.


Cotel F, Exley R, Cragg SJ, Perrier JF. Serotonin spillover onto the axon initial segment of motoneurons induces central fatigue by inhibiting action potential initiation. Proc Natl Acad Sci U S A. 110:4774-4779, 2013.

Perrier JF, Rasmussen HB, Jørgensen LK, Berg RW. Intense activity of the raphe spinal pathway depresses motor activity via a serotonin dependent mechanism. Front Neural Circuits. 11:111, 2018.


Associate Professor Justin Kavanagh is a researcher and lecturer at Griffith University. His team explores how the central nervous system controls voluntary and involuntary movement, and he has particular interests in understanding how medications can be used to study mechanisms of human movement.

via Antidepressants help us understand why we get fatigued during exercise – Motor Impairment


, , , , , , , , ,

Leave a comment

[Abstract] EMG Feature Extractions for Upper-Limb Functional Movement During Rehabilitation


Rehabilitation is important treatment for post stroke patient to regain their muscle strength and motor coordination as well as to retrain their nervous system. Electromyography (EMG) has been used by researcher to enhance conventional rehabilitation method as a tool to monitor muscle electrical activity however EMG signal is very stochastic in nature and contains some noise. Special technique is yet to be researched in processing EMG signal to make it useful and effective both to researcher and to patient in general. Feature extraction is among the signal processing technique involved and the best method for specific EMG study needs to be applied. In this works, nine feature extractions techniques are applied to EMG signals recorder from subjects performing upper limb rehabilitation activity based on suggested movement sequence pattern. Three healthy subjects perform the experiment with three trials each and EMG data were recorded from their bicep and deltoid muscle. The applied features for every trials of each subject were analyzed statistically using student T-Test their significant of p-value. The results were then totaled up and compared between the nine features applied and Auto Regressive coefficient (AR) present the best result and consistent with each subjects’ data. This feature will be used later in our future research work of Upper-limb Virtual Reality Rehabilitation.

via EMG Feature Extractions for Upper-Limb Functional Movement During Rehabilitation – IEEE Conference Publication

, , , , , , , , , , , ,

Leave a comment

[Abstract + References] A Tendon-driven Upper-limb Rehabilitation Robot – IEEE Conference Publication


Rehabilitation robots are playing an increasingly important role in daily rehabilitation of patients. In recent years, exoskeleton rehabilitation robots have become a research hotspot. However, the existing exoskeleton rehabilitation robots are mainly rigid exoskeletons. During rehabilitation training using such exoskeletons, the patient’s joint rotation center is fixed, which cannot adapt to the actual joint movements, resulting in secondary damage to the patients. Therefore, in this paper, a tendon-driven flexible upper-limb rehabilitation robot is proposed; the structure and connectors of the rehabilitation robot are designed considering the physiological structure of human upper limbs; we also built the prototype and performed experiments to validate the designed robot. The experimental results show that the proposed upper-limb rehabilitation robot can assist the human subject to conduct upper-limb rehabilitation training.

I. Introduction

Central nervous system diseases, such as stroke, spinal cord injury and traumatic brain injury, tend to cause movement disorder [1]. Clinical studies have shown that intensive rehabilitation training after cerebral injury help patients recover motoric functions because of the brain plasticity [1], [2]. Traditional movement therapy is highly dependent on physiotherapists and the efficacy is limited by professional knowledge and skill levels of physiotherapists [3]. Upper-limbs recover more slowly than lower limbs because of the complex function of neurons. Meanwhile, the rehabilitation therapies are unaffordable for most patients. Robotic rehabilitation opened another way of rehabilitation training and its efficacy has been validated in clinical trials [3], [4]. Many upper-limb robot devices have been developed for rehabilitation or assistance in various forms. One of the famous devices was MIT-MANUS developed by MIT. This kind of devices are stationary external system where the patient inserts their hand or arm and is robotically assisted or resisted in completing predetermined tasks [3], [5]. Other examples of this type of devices include Lum et al.^{\prime}s MIME [6], Kahn et al.’s ARM Guide [7] and a 2-DOF upper-limb rehabilitation robot developed by Tsinghua



1. M. Hallett, “Plasticity of the human motor cortex and recovery from stroke”, Brain Research Reviews, vol. 36, pp. 169-174, 2001.

2. J. D. Schaechter, “Motor rehabilitation and brain plasticity after hemiparetic stroke”, Progress in Neurobiology, vol. 73, pp. 61-72, 2004.

3. Q. Yang, D. Cao, J. Zhao, “Analysis on State of the Art of upper-limb Rehabilitation Robots”, Jiqiren/robot, vol. 35, pp. 630, 2013.

4. P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn, A. Jansen-Troy, S. Leonhardt, “A survey on robotic devices for upper-limb rehabilitation”, Journal of Neuroengineering & Rehabilitation, vol. 11, pp. 3, 2014.

5. C. J. Nycz, M. A. Delph, G. S. Fischer, “Modeling and design of a tendon actuated flexible robotic exoskeleton for hemiparetic upper-limb rehabilitation”, International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3889-3892, 2015.

6. P. S. Lum, C. G. Burgar, P. C. Shor, “Use of the MIME robotic system to retrain multijoint reaching in post-stroke hemiparesis: why some movement patterns work better than others”, Engineering in Medicine and Biology Society 2003. Proceedings of the International Conference of the IEEE, vol. 2, pp. 1475-1478, 2003.

7. D. J. Reinkensmeyer, L. E. Kahn, M. Averbuch, A. Mckenna-Cole, B. D. Schmit, W. Z. Rymer, “Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide”, Journal of Rehabilitation Research & Development, vol. 37, pp. 653-662.

8. Y. Zhang, Z. Wang, L. Ji, S. Bi, “The clinical application of the upper extremity compound movements rehabilitation training robot”, International Conference on Rehabilitation Robotics, pp. 91-94, 2005.

9. H. Fukushima, “Health and wellbeing in the 21st century (No. 4): Early rehabilitation and conditions for which it is appropriate [J]” in Social-human environmentology, pp. 6, 2004.

10. T. G. Sugar, J. He, E. J. Koeneman, J. B. Koeneman, R. Herman, H. Huang et al., “Design and control of RUPERT: a device for robotic upper extremity repetitive therapy”, IEEE Transactions on Neural Systems & Rehabilitation Engineering a Publication of the IEEE Engineering in Medicine & Biology Society, vol. 15, no. 3, pp. 336-46, 2007.

11. J. C Perry, J. Rosen, S. Burns, “Upper-Limb Powered Exoskeleton Design”, Mechatronics IEEE/ASME Transactions on, vol. 12, pp. 408-417, 2007.

12. A. U. Pehlivan, O. Celik, M. K. O’Malley, “Mechanical design of a distal arm exoskeleton for stroke and spinal cord injury rehabilitation”, IEEE International Conference on Rehabilitation Robotics IEEE Int Conf Rehabil Robot, pp. 5975428, 2011.

13. S Koo, T. P. Andriacchi, “The Knee Joint Center of Rotation is Predominantly on the Lateral Side during Normal Walking[J]”, Journal of Biomechanics, vol. 41, no. 6, pp. 1269, 2008.

14. Y. Mao, S. K. Agrawal, “Transition from mechanical arm to human arm with CAREX: A cable driven ARm EXoskeleton (CAREX) for neural rehabilitation”, Proc. IEEE Int. Conf. Robot. Autom., pp. 2457-2462, 2012.

15. Y. Mao, X. Jin, G. G. Dutta, J. P. Scholz, S. K. Agrawal, “Human movement training with a cable driven ARm EXoskeleton (CAREX)”, IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 1, pp. 84-92, Jan. 2015.

16. DJ Reinkensmeyer, JL Emken, SC. Cramer, “Robotics motor learning and neurologic recovery”, Annual Review of Biomedical Engineering, vol. 6, no. 1, pp. 497-525, 2004.

17. QZ Yang, CF Cao, JH. Zhao, “Analysis of the status of the research of the upper-limb rehabilitative robot”, Robot, vol. 35, no. 5, pp. 630-640, 2013.

18. XZ Jiang, XH Huang, CH Xiong et al., “Position Control of a Rehabilitation Robotic Joint Based on Neuron Proportion-Integral and Feedforward Control”, Journal of Computational & Nonlinear Dynamics, vol. 7, no. 2, pp. 024502, 2012.

19. ZC Chen, Z. Huang, “Motor relearning in the application of the rehabilitation therapy for stroke”, Chinese Journal of Rehabilitation Medicine, vol. 22, no. 11, pp. 1053-1056, 2007.

20. JC Perry, J Rosen, S. Burns, “Upper-Limb Powered Exoskeleton Design[J]”, IEEE/ASME Transactions on Mechatronics, vol. 12, no. 4, pp. 408-417, 2007.

21. C LV, Research on rehabilitation robot for upper-limb hemiplegia, Shanghai China:, 2011.

22. Y K Woo, G H Cho, E Y. Yoo, Effect of PNF Applied to the Unaffected Side on Muscle Tone of Affected Side in Patients with Hemiplegia[J], vol. 9, no. 2, 2002.

23. JH Liang, JP Tong, X. Li, “Observation of the curative effect of continuous passive movement of joints in the treatment of lower limb spasticity”, Theory and practice of rehabilitation in China, vol. 14, no. 11, pp. 1067-1067, 2008.


via A Tendon-driven Upper-limb Rehabilitation Robot – IEEE Conference Publication

, , , , , , , , ,

Leave a comment

%d bloggers like this: