Posts Tagged Muscles

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

Abstract

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.

References

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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 

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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 

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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 

Source: https://ieeexplore.ieee.org/abstract/document/9189174

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[Guide] BIOFEEDBACK AT FOR DEPRESSION – Full Text PDF

Abstract

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.

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

Abstract

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%.

Introduction

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.

 

 

 

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

Abstract

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

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

Abstract

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.
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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 .

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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 .

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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 .

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

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19. “ PAULA 1.2 | Myo Software | Myo Hands and Components |Upper Limb Prosthetics | Prosthetics | Ottobock US Healthcare .”[Online]. Available: https://professionals.ottobockus.com/Prosthetics/Upper-Limb-Prosthetics/Myo-Hands-and-Components/Myo-Software/PAULA-1-2/p/646C52~5V1~82 [Accessed: 21-Jan-2019 ].

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

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

Abstract

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.
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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.

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

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

Abstract

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.

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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.

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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.

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[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.

WHAT DID WE FIND?

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.

SIGNIFICANCE AND IMPLICATIONS

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.

PUBLICATION REFERENCE

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.

KEY REFERENCES

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.

AUTHOR BIO

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

 

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

Abstract

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.

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

Abstract

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

 

References

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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.

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