Posts Tagged Electromyography

[Abstract + References] Arm Games for Virtual Reality Based Post-stroke Rehabilitation – Conference paper


Stroke is a leading cause of serious long-term disability. World Health Organization (WHO) published that the second leading of death is stroke accident and every year, 15 million people worldwide suffer from stroke attack, two-thirds of them have a permanent disability. Muscle impairment can be treated by intensive movements involving repetitive task, task-oriented and task-variegated. Conventional stroke rehabilitation is expensive, less engaging and at the same time need more time for the rehabilitation process and need more energy and time for the therapist to guide the stroke-survivor. Modern stroke rehabilitation is more promising and more effective with modern rehabilitation aids allowing the rehabilitation process to be faster, however, this therapist method can be obtained in the big cities. To cover the lack of rehabilitation process in this research will develop and improve post-stroke rehabilitation using games. This research using electromyography (EMG) device to analyze the muscle contraction during the rehabilitation process and using Kinect XBOX to record trajectory hands movements. Five games from movements sequence have designed and will be examined in this research. This games obtained two results, the first is the EMG signal and the second is trajectory data. EMG signal can recognize muscle contractions during playing game and the trajectory data can save the pattern of movements and showed the pattern to the monitor. EMG signal processing using time or frequency feature extractions is a good idea to obtain more information from muscle contractions, also velocity, similarities and error movements can be obtained by study the possible approaches.


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[ARTICLE] FarMyo: A Serious Game for Hand and Wrist Rehabilitation Using a Low-Cost Electromyography Device – Full Text PDF


One of the strategies used in recent years to increase the commitment and motivation of patients undergoing rehabilitation is the use of graphical systems, such as virtual environments and serious games. In addition to contributing to the motivation, these systems can simulate real life activities and provide means to measure and assess user performance. The use of natural interaction devices, originally conceived for the game market, has allowed the development of low cost and minimally invasive rehabilitation systems. With the advent of natural interaction devices based on electromyography, the user’s electromyographic data can also be used to build these systems. This paper shows the development of a serious game focused on aiding the rehabilitation process of patients with hand motor problems, targeting to solve problems related to cost, adaptability and patient motivation in this type of application. The game uses an electromyography device to recognize the gestures being performed by the user. A gesture recognition system was developed to detect new gestures, complementing the device’s own recognition system, which is responsible for interpreting the signals. An initial evaluation of the game was conducted with professional physiotherapists.

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[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.
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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

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[ARTICLE] Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control – Full Text



Ankle exoskeletons offer a promising opportunity to offset mechanical deficits after stroke by applying the needed torque at the paretic ankle. Because joint torque is related to gait speed, it is important to consider the user’s gait speed when determining the magnitude of assistive joint torque. We developed and tested a novel exoskeleton controller for delivering propulsive assistance which modulates exoskeleton torque magnitude based on both soleus muscle activity and walking speed. The purpose of this research is to assess the impact of the resulting exoskeleton assistance on post-stroke walking performance across a range of walking speeds.


Six participants with stroke walked with and without assistance applied to a powered ankle exoskeleton on the paretic limb. Walking speed started at 60% of their comfortable overground speed and was increased each minute (n00, n01, n02, etc.). We measured lower limb joint and limb powers, metabolic cost of transport, paretic and non-paretic limb propulsion, and trailing limb angle.


Exoskeleton assistance increased with walking speed, verifying the speed-adaptive nature of the controller. Both paretic ankle joint power and total limb power increased significantly with exoskeleton assistance at six walking speeds (n00, n01, n02, n03, n04, n05). Despite these joint- and limb-level benefits associated with exoskeleton assistance, no subject averaged metabolic benefits were evident when compared to the unassisted condition. Both paretic trailing limb angle and integrated anterior paretic ground reaction forces were reduced with assistance applied as compared to no assistance at four speeds (n00, n01, n02, n03).


Our results suggest that despite appropriate scaling of ankle assistance by the exoskeleton controller, suboptimal limb posture limited the conversion of exoskeleton assistance into forward propulsion. Future studies could include biofeedback or verbal cues to guide users into limb configurations that encourage the conversion of mechanical power at the ankle to forward propulsion.

Trial registration



Walking after a stroke is more metabolically expensive, leading to rapid exhaustion, limited mobility, and reduced physical activity [1]. Hemiparetic walking is slow and asymmetric compared to unimpaired gait. Preferred walking speeds following stroke range between < 0.2 m s− 1 and ~ 0.8 m s− 1 [2] compared to ~ 1.4 m s− 1 in unimpaired adults, and large interlimb asymmetry has been documented in ankle joint power output [34]. The ankle plantarflexors are responsible for up to 50% of the total positive work needed to maintain forward gait [56]; therefore, weakness of the paretic plantarflexors is especially debilitating, and as a result, the paretic ankle is often a specific target of stroke rehabilitation [78910]. In recent years, ankle exoskeletons have emerged as a technology capable of improving ankle power output by applying torque at the ankle joint during walking in clinical populations [78] and healthy controls [11121314]. Myoelectric exoskeletons offer a user-controlled approach to stroke rehabilitation by measuring and adapting to changes in the user’s soleus electromyography (EMG) when generating torque profiles applied at the ankle [15]. For example, a proportional myoelectric ankle exoskeleton was shown to increase the paretic plantarflexion moment for persons post-stroke walking at 75% of their comfortable overground (OVG) speed [8]; despite these improvements, assistance did not reduce the metabolic cost of walking or improve percent paretic propulsion. The authors suggested exoskeleton performance could be limited because the walking speed was restricted to a pace at which exoskeleton assistance was not needed.

Exoskeleton design for improved function following a stroke would benefit from understanding the interaction among exoskeleton assistance, changes in walking speed, and measured walking performance. Increases in walking speed post-stroke are associated with improvements in forward propulsion and propulsion symmetry [16], trailing limb posture [1718], step length symmetries [1719], and greater walking economies [1719]. This suggests that assistive technologies need to account for variability in walking speeds to further improve post-stroke walking outcomes. However, research to date has evaluated exoskeleton performance at only one walking speed, typically set to either the participant’s comfortable OVG speed or a speed below this value [78]. At constant speeds, ankle exoskeletons have been shown to improve total ankle power in both healthy controls [11] and persons post-stroke [8], suggesting the joint powers and joint power symmetries could be improved by exoskeleton technology. Additionally, an exosuit applying assistance to the ankle was able to improve paretic propulsion and metabolic cost in persons post-stroke walking at their comfortable OVG speed [7]. Assessing the impact of exoskeleton assistance on walking performance across a range of speeds is the next logical step toward developing exoskeleton intervention strategies targeted at improving walking performance and quality of life for millions of persons post-stroke.

In order to assess the impact of exoskeleton assistance across a range of walking speeds in persons post-stroke, we developed a novel, speed-adaptive exoskeleton controller that automatically modulates the magnitude of ankle torque with changes in walking speed and soleus EMG. We hypothesized that: 1) Our novel speed-adaptive controller will scale exoskeleton assistance with increases in walking speed as intended. 2) Exoskeleton assistance will lead to increases in total average net paretic ankle power and limb power at all walking speeds. 3) Exoskeleton assistance will lead to metabolic benefits associated with improved paretic average net ankle and limb powers.


Exoskeleton hardware

We implemented an exoskeleton emulator comprised of a powerful off-board actuation and control system, a flexible Bowden cable transmission, and a lightweight exoskeleton end effector [20]. The exoskeleton end effector includes shank and foot carbon fiber components custom fitted to participants and hinged at the ankle. The desired exoskeleton torque profile was applied by a benchtop motor (Baldor Electric Co, USA) to the carbon-fiber ankle exoskeleton through a Bowden-cable transmission system. An inline tensile load cell (DCE-2500 N, LCM Systems, Newport, UK) was used to confirm the force transmitted by the exoskeleton emulator during exoskeleton assistance.

Speed-adaptive proportional myoelectric exoskeleton controller

Our exoskeleton controller alters the timing and magnitude of assistance with the user’s soleus EMG signal and walking speed (Fig. 1). The exoskeleton torque is determined from Eq. 1, in which participant mass (mparticipant) is constant across speeds, treadmill speed (V) is measured in real-time, the speed gain (Gspeed) is constant for all subjects and across speeds, the adaptive gain (Gadp) is constant for a gait cycle and calculated anew for each gait cycle, and the force-gated and normalized EMG (EMGGRFgated) is a continuously changing variable.

τexo (t)=mparticipant×V×Gspeed×Gadp×EMGGRFgatedτexo (t)=mparticipant×V×Gspeed×Gadp×EMGGRFgated
Fig. 1
Fig. 1

Novel speed-adaptive myoelectric exoskeleton controller measures and adapts to users’ soleus EMG signal as well as their walking speed in order to generate the exoskeleton torque profile. Raw soleus EMG signal is filtered and rectified to create an EMG envelope, and the created EMG envelope is then gated by anterior GRFs to ensure assistance is only applied during forward propulsion. The adaptive EMG gain is calculated as a moving average of peak force-gated EMG from the last five paretic gait cycles. The pre-speed gain control signal is the product of the force-gated EMG and the adaptive EMG gain. The speed gain is determined using real-time walking speed and computed as 25% of the maximum biological plantarflexion torque at that given walking speed. Exoskeleton torque is the result of multiplying the speed gain with the pre-speed gain control signal


Continue —> Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control | Journal of NeuroEngineering and Rehabilitation | Full Text

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[ARTICLE] Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks – Full Text



To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the user’s movement intentions. Surface electromyographic (sEMG) signals could be suitable for this purpose, but their applicability in shared control schemes for real-time operation of assistive devices in daily-life activities is limited due to high inter-subject variability, which requires custom calibrations and training. Here, we developed a machine-learning-based algorithm for detecting the user’s motion intention based on electromyographic signals, and discussed its applicability for controlling an upper-limb exoskeleton for people with severe arm disabilities.


Ten healthy participants, sitting in front of a screen while wearing the exoskeleton, were asked to perform several reaching movements toward three LEDs, presented in a random order. EMG signals from seven upper-limb muscles were recorded. Data were analyzed offline and used to develop an algorithm that identifies the onset of the movement across two different events: moving from a resting position toward the LED (Go-forward), and going back to resting position (Gobackward). A set of subject-independent time-domain EMG features was selected according to information theory and their probability distributions corresponding to rest and movement phases were modeled by means of a two-component Gaussian Mixture Model (GMM). The detection of movement onset by two types of detectors was tested: the first type based on features extracted from single muscles, whereas the second from multiple muscles. Their performances in terms of sensitivity, specificity and latency were assessed for the two events with a leave one-subject out test method.


The onset of movement was detected with a maximum sensitivity of 89.3% for Go-forward and 60.9% for Go-backward events. Best performances in terms of specificity were 96.2 and 94.3% respectively. For both events the algorithm was able to detect the onset before the actual movement, while computational load was compatible with real-time applications.


The detection performances and the low computational load make the proposed algorithm promising for the control of upper-limb exoskeletons in real-time applications. Fast initial calibration makes it also suitable for helping people with severe arm disabilities in performing assisted functional tasks.


Exoskeletons are wearable robots exhibiting a close physical and cognitive interaction with the human users. Over the last years, several exoskeletons have been developed for different purposes, such as augmenting human strength [1], rehabilitating neurologically impaired individuals [2] or assisting people affected by many neuro-musculoskeletal disorders in activities of daily life [3]. For all these applications, the design of cognitive Human-Robot Interfaces (cHRIs) is paramount [4]; indeed, understanding the users’ intention allows to control the device with the final goal to facilitate the execution of the intended movement. The flow of information from the human user to the robot control unit is particularly crucial when exoskeletons are used to assist people with compromised movement capabilities (e.g. post-stroke or spinal-cord-injured people), by amplifying their movements with the goal to restore functions.

In recent years, different approaches have been pursued to design cHRIs, based on invasive and non-invasive approaches. Implantable electrodes, placed directly into the brain or other electrically excitable tissues, record signals directly from the peripheral or central nervous system or muscles, with high resolution and high precision [5]. Non-invasive approaches exploit different bio-signals: some examples are electroencephalography (EEG) [6], electrooculography (EOG) [7], and brain-machine interfaces (BMI) combining the two of them [8910]. In addition, a well-consolidated non-invasive approach is based on surface electromyography (sEMG) [11], which has been successfully used for controlling robotic prostheses and exoskeletons due to their inherent intuitiveness and effectiveness [121314]. Compared to EEG signals, sEMG signals are easy to be acquired and processed and provide effective information on the movement that the person is executing or about to start executing. Despite the above-mentioned advantages, the use of surface EMG signals still has several drawbacks, mainly related to their time-varying nature and the high inter-subject variability, due to differences in the activity level of the muscles and in their activation patterns [1115], which requires custom calibrations and specific training for each user [16]. For these reasons, notwithstanding the intuitiveness of EMG interfaces, it is still under discussion their efficacy and usability in shared human-machine control schemes for upper-limb exoskeletons. Furthermore, the need for significant signal processing can limit the use of EMG signals in on-line applications, for which fast detection is paramount. In this scenario, machine learning methods have been employed to recognize the EMG onset in real time, using different classifiers such as Support Vector Machines, Linear Discriminant Analysis, Hidden Markov Models, Neural Networks, Fuzzy Logic and others [151617]. In this process, a set of features is previously selected in time, frequency, or time-frequency domains [18]. Time-domain features extract information associated to signal amplitude in non-fatiguing contractions; when fatigue effects are predominant, frequency-domain features are more representative; finally, time-frequency domain features better elicit transient effects of muscular contractions. Before feeding the features into the classifier, dimensionality reduction is usually performed, to increase classification performances while reducing complexity [19]. The most common strategies for reduction are: i) feature projection, to map the set of features into a new set with reduced dimensionality (e.g., linear mapping through Principal Component Analysis); ii) feature selection, in which a subset of features is selected according to specific criteria, aimed at optimizing a chosen objective function. All the above-mentioned classification approaches ensure good performance under controlled laboratory conditions. Nevertheless, in order to be used effectively in real-life scenarios, smart algorithms must be developed, which are able to adapt to changes in the environmental conditions and intra-subject variability (e.g. changes of background noise level of the EMG signals), as well as to the inter-subject variability [20].

In this paper, we exploited a cHRI combining sEMG and an upper-limb robotic exoskeleton, to fast detect the users’ motion intention. We implemented offline an unsupervised machine-learning algorithm, using a set of subject-independent time-domain EMG features, selected according to information theory. The probability distributions of rest and movement phases of the set of features were modelled by means of a two-component Gaussian Mixture Model (GMM). The algorithm simulates an online application and implements a sequential method to adapt GMM parameters during the testing phase, in order to deal with changes of background noise levels during the experiment, or fluctuations in EMG peak amplitudes due to muscle adaptation or fatigue. Features were extracted from two different signal sources, namely onset detectors, which were tested offline and their performance in terms of sensitivity (or true positive rate), specificity (or true negative rate) and latency (delay on onset detection) were assessed for two different events, i.e. two transitions from rest to movement phases at different initial conditions. The two events were selected in order to replicate a possible application scenario of the proposed system. Based on the results we obtained, we discussed the applicability of the algorithm to the control of an upper-limb exoskeleton used as an assistive device for people with severe arm disabilities.

Materials and methods

Experimental setup

The experimental setup includes: (i) an upper-limb powered exoskeleton (NESM), (ii) a visual interface, and (iii) a commercial EMG recording system (TeleMyo 2400R, Noraxon Inc., AZ, US).

NESM upper-limb exoskeleton

NESM (Fig. 1a) is a shoulder-elbow powered exoskeleton designed for the mobilization of the right upper limb [2122], developed at The BioRobotics Institute of Scuola Superiore Sant’Anna (Italy). The exoskeleton mechanical structure hangs from a standing structure and comprises four active and eight passive degrees of freedom (DOFs), along with different mechanisms for size regulations to improve comfort and wearability of the device.
Fig. 1

Fig. 1a Experimental setup, comprising NESM, EMG electrodes and the visual interface; b Location of the electrodes for EMG acquisition; c Timing and sequence of action performed by the user during a single trial


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[ARTICLE] Physiological and kinematic effects of a soft exosuit on arm movements – Full Text



Soft wearable robots (exosuits), being lightweight, ergonomic and low power-demanding, are attractive for a variety of applications, ranging from strength augmentation in industrial scenarios, to medical assistance for people with motor impairments. Understanding how these devices affect the physiology and mechanics of human movements is fundamental for quantifying their benefits and drawbacks, assessing their suitability for different applications and guiding a continuous design refinement.


We present a novel wearable exosuit for assistance/augmentation of the elbow and introduce a controller that compensates for gravitational forces acting on the limb while allowing the suit to cooperatively move with its wearer. Eight healthy subjects wore the exosuit and performed elbow movements in two conditions: with assistance from the device (powered) and without assistance (unpowered). The test included a dynamic task, to evaluate the impact of the assistance on the kinematics and dynamics of human movement, and an isometric task, to assess its influence on the onset of muscular fatigue.


Powered movements showed a low but significant degradation in accuracy and smoothness when compared to the unpowered ones. The degradation in kinematics was accompanied by an average reduction of 59.20±5.58% (mean ± standard error) of the biological torque and 64.8±7.66% drop in muscular effort when the exosuit assisted its wearer. Furthermore, an analysis of the electromyographic signals of the biceps brachii during the isometric task revealed that the exosuit delays the onset of muscular fatigue.


The study examined the effects of an exosuit on the characteristics of human movements. The suit supports most of the power needed to move and reduces the effort that the subject needs to exert to counteract gravity in a static posture, delaying the onset of muscular fatigue. We interpret the decline in kinematic performance as a technical limitation of the current device. This work suggests that a powered exosuit can be a good candidate for industrial and clinical applications, where task efficiency and hardware transparency are paramount.


In the never-ending quest to push the boundaries of their motor performance, humans have designed a wealth of wearable robotic devices. In one of the earliest recorded attempts to do so, in 1967, Mosher aspired to create a symbiotic unit that would have the “…alacrity of man’s information and control system coupled with the machine’s power and ruggedness” [1]. His design of the Hardiman, although visionary, ran into fundamental technological limitations.

Advances in materials science, electronics and energy storage have since enabled an exponential growth of the field, with state-of-the-art exoskeletons arguably accomplishing Mosher’s vision [2]. Wearable robotic technology has been successful in augmenting human strength during locomotion [3], reducing the metabolic cost of human walking [45], restoring ambulatory capabilities to paraplegic patients [6], assisting in rehabilitating stroke patients [789], harvesting energy from human movements [10] and helping to study fundamental principles underlying human motor control [1112].

These feats were achieved with machines made of rigid links of metal and capable of accurately and precisely delivering high forces to their wearer. While this is undeniably an advantage, it comes at a cost: 1) a significant inertia, which affects both the kinematics of human movement and the power requirements of the device; 2) the need for the joints of the robot to be aligned with the biological joints [13], resulting in increased mechanical complexity and size [14]; 3) a strong cosmetic impact, shown to be linked with psychological health and well-being [15].

The recent introduction of soft materials to transmit forces and torques to the human body [16] has allowed to design wearable robotic devices on the other side of the spectrum: lightweight, low-profile and compliant machines that sacrifice accuracy and magnitude of assistance for the sake of portability and svelteness.

Soft exoskeletons, or exosuits, are clothing-like devices made of fabric or elastomers that wrap around a person’s limb and work in parallel with his/her muscles [1718]. Characteristic of exosuits is that they rely on the structural integrity of the human body to transfer reaction forces between body segments, rather than having their own frame, thus acting more like external muscles than an external skeleton. Their intrinsic compliance removes the need for alignment with the joints and their low-profile allows to wear them underneath everyday clothing.

Exosuits actively transmit power to the human body either using cables, moved by electric motors, or soft pneumatic actuators, embedded in the garment. The latter paradigm was probably among the first to be proposed [19] and has been explored to assist stroke patients during walking [20], to increase shoulder mobility in subjects with neuromuscular conditions [21], to help elbow movements [22] and for rehabilitation purposes to train and aid grasping [232425].

Cable-driven exosuits, instead, include a DC motor that transmits power to the suit using Bowden cables. This flexible transmission allows to locate the actuation stage where its additional weight has the least metabolic impact on its wearer. Using this paradigm to provide assistance to the lower limbs has resulted in unprecedented levels of walking economy in healthy subjects [26] and improved symmetry and efficiency of mobility in stroke patients [27]. Similar principles were used to provide active support to hip and knee extension, reducing activation of the gluteus maximus in sit-to-stand and stand-to-sit transitions [28].

Cable-driven exosuits seem to work particularly well for lower-limbs movements, where small bursts of well-timed assistance can have a big impact on the dynamics and metabolic cost of locomotion [29]. Yet, Park et al. have shown that they have the potential for assisting the upper-limbs in quasi-static movements too: using a tendon-driving mechanism, a textile interface and an elastic component they found a significant reduction in the activity of the deltoid muscle when supporting the weight of the arm [30].

Similar results were reported by Chiaradia et al., where a soft exosuit for the elbow was shown to reduce the activation of the biceps brachii muscle in dynamic movements [31], and by Khanh et al., where the same device was used to improve the range of motion of a patient suffering from bilateral brachial plexus injury [32].

While there is extensive work on the analysis of the effects of wearing a soft exosuit on the kinematics, energetics and muscular activation during walking [33], the authors are unaware of comparable studies on movements of the upper limbs, whose variety of volitional motions is fundamentally different from the rhythmic nature of walking.

Understanding how these devices affect the physiology and mechanics of human movements is fundamental for quantifying their benefits and drawbacks, assessing their suitability for different applications and guiding a continuous data-driven design refinement.

In this study we investigate the kinematic and physiological effects of wearing a cable-driven exosuit to support elbow movements. We hypothesize that the low inertia and soft nature of the exosuit will allow it to work in parallel with the user’s muscles, delaying the onset of fatigue while having little to no impact on movement kinematics.

We propose a variation of the design and controller presented in [3234] and introduce a controller that both detects the wearer’s intention, allowing the suit to quickly shadow the user’s movements, and compensates for gravitational forces acting on the limb, thus reducing the muscular effort required for holding a static posture. We collect kinematic, dynamic and myoelectric signals from subjects wearing the device, finding that the exosuit affects motion smoothness, significantly reduces muscular effort and delays the onset of fatigue. The analysis offers interesting insights on the viability of using this technology for human augmentation/assistance and medical purposes.


Exosuit design

An exosuit is a device consisting of a frame made of soft material that wraps around the human body and transmits forces to its wearer’s skeletal structure. In a cable-driven exosuit, artificial tendons are routed along a targeted joint and attached to anchor points on both of its sides. When the tendons are tensioned they deliver an assistive moment to the joint.


Continue —>  Physiological and kinematic effects of a soft exosuit on arm movements | Journal of NeuroEngineering and Rehabilitation | Full Text

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[Abstract] Investigation of multi-joint coordinated upper limb rehabilitation assisted with electromyography (EMG)-driven neuromuscular electrical stimulation (NMES)-robot after stroke


More than 80% of stroke survivors worldwide suffer from permanent upper limb motor deficits. Restoration of upper limb motor functions in conventional rehabilitation remains challenging; the main difficulties are as follows: 1) lack of intensive, repetitive practice in manually delivered treatment; 2) lack of coordination management of upper limb motor tasks, particularly those involving the distal joints, e.g., the wrist and the hand; and 3) lack of understanding of the optimal joint supportive scheme in task-oriented upper limb training. More effective training strategies are necessary for upper limb rehabilitation following stroke. Robots have proved to be valuable assistants in labour-demanding post-stroke rehabilitation, with a controllable mechanical design and repeatable dynamic support in physical training. A series of rehabilitation robots for multi-joint practices were successfully designed in our previous works. In this work, we proposed a device-assisted multi-joint coordinated strategy for post-stroke upper limb training. The objectives of the study were as follows: 1) To evaluate the rehabilitation effectiveness of multi-joint coordinated upper limb practice assisted by an electromyography (EMG)-driven neuromuscular electric stimulation (NMES)-robot for stroke survivors in both the subacute and chronic stages. 2) To compare different joint supportive schemes using NMES-robots and identify the optimized scheme for upper limb rehabilitation. The objectives were achieved through three independent clinical trials using common clinical assessments, namely, the Fugl-Meyer Assessment (FMA), Modified Ashworth Scales (MAS), Action Research Arm Test (ARAT), and Functional Independence Measurement (FIM), and cross-session EMG evaluations to trace the recovery progress of individual muscle activities (i.e. EMG activation level) and muscular coordination (i.e. Co-contraction Index, CI) between a pair of muscles.
The first clinical randomized controlled trial (RCT) was conducted to investigate the clinical effects and rehabilitation effectiveness of the new training strategy in the subacute stroke period. Subjects were randomly assigned to two groups and received either 20 sessions of NMES-robot-assisted training (NMES-robot group, n=14) or time-matched conventional treatments (control group, n=10). Significant improvements were achieved in FMA (full score and shoulder/elbow), ARAT, and FIM for both groups [P<0.001, effect sizes (EFs)>0.279], whereas significant improvements in FMA (wrist/hand) and MAS (wrist) after treatment were only observed in the NMES-robot group (P<0.05, EFs>0.145), with the outcomes maintained for 3 months. In the NMES-robot group, CIs of the muscle pairs of biceps brachii and flexor carpi radialis (BIC&FCR) and biceps brachii and triceps brachii (BIC&TRI) were significantly reduced and the EMG activation level of the FCR decreased significantly. The result indicated comparable proximal motor improvements in both groups and better distal motor outcomes and more effective release of muscle spasticity across the whole upper limb in the NMES-robot group. The second part of the work was a clinical trial with a single-group design. Recruited chronic stroke patients (n=17) received 20 sessions of NMES-robot-assisted multi-joint coordinated upper limb training. Significant improvements were observed in FMA (full score and shoulder/elbow), ARAT, and FIM (P<0.05, EFs>0.157) and maintained for 3 months. CIs of the FCR&TRI and BIC&TRI muscle pairs and EMG activation levels of the FCR and BIC significantly decreased. The results indicated that the new training strategy was effective for upper limb recovery in the chronic stroke, with the long sustainability of the motor outcomes. In the third trial, another clinical RCT was conducted to investigate the training effects of different joint supportive schemes. The recruited chronic subjects were randomly assigned to receive task-oriented multi-joint practices with NMES-robotic support either to the finger-palm (hand group, n=15) or to the wrist-elbow (sleeve group, n=15). Significant improvements in FMA (full score and shoulder/elbow) and ARAT (P<0.05, EFs>0.147) were observed in both groups, whereas significant improvements in FMA (wrist/hand) and MAS (finger, wrist, and elbow) (P<0.05, EFs>0.149) were only observed in the hand group. These results indicated that the distal supportive scheme was more effective in distal motor recovery and whole arm spasticity control than the proximal supportive one under the same training strategy. In conclusion, NME-robot-assisted multi-joint coordinated training was able to achieve significant motor outcomes and effective muscle spasticity control in the entire upper limb, especially at the distal segments, i.e., the wrist and the fingers, in both subacute and chronic stroke patients. Moreover, the distal supportive scheme proved more effective than the proximal supportive scheme in multi-joint coordinated upper limb training.

via Investigation of multi-joint coordinated upper limb rehabilitation assisted with electromyography (EMG)-driven neuromuscular electrical stimulation (NMES)-robot after stroke | PolyU Institutional Research Archive


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[ARTICLE] Effectiveness of Robot-Assisted Upper Limb Training on Spasticity, Function and Muscle Activity in Chronic Stroke Patients Treated With Botulinum Toxin: A Randomized Single-Blinded Controlled Trial


Background: The combined use of Robot-assisted UL training and Botulinum toxin (BoNT) appear to be a promising therapeutic synergism to improve UL function in chronic stroke patients.

Objective: To evaluate the effects of Robot-assisted UL training on UL spasticity, function, muscle strength and the electromyographic UL muscles activity in chronic stroke patients treated with Botulinum toxin.

Methods: This single-blind, randomized, controlled trial involved 32 chronic stroke outpatients with UL spastic hemiparesis. The experimental group (n = 16) received robot-assisted UL training and BoNT treatment. The control group (n = 16) received conventional treatment combined with BoNT treatment. Training protocols lasted for 5 weeks (45 min/session, two sessions/week). Before and after rehabilitation, a blinded rater evaluated patients. The primary outcome was the Modified Ashworth Scale (MAS). Secondary outcomes were the Fugl-Meyer Assessment Scale (FMA) and the Medical Research Council Scale (MRC). The electromyographic activity of 5 UL muscles during the “hand-to-mouth” task was explored only in the experimental group and 14 healthy age-matched controls using a surface Electromyography (EMGs).

Results: No significant between-group differences on the MAS and FMA were measured. The experimental group reported significantly greater improvements on UL muscle strength (p = 0.004; Cohen’s d = 0.49), shoulder abduction (p = 0.039; Cohen’s d = 0.42), external rotation (p = 0.019; Cohen’s d = 0.72), and elbow flexion (p = 0.043; Cohen’s d = 1.15) than the control group. Preliminary observation of muscular activity showed a different enhancement of the biceps brachii activation after the robot-assisted training.

Conclusions: Robot-assisted training is as effective as conventional training on muscle tone reduction when combined with Botulinum toxin in chronic stroke patients with UL spasticity. However, only the robot-assisted UL training contributed to improving muscle strength. The single-group analysis and the qualitative inspection of sEMG data performed in the experimental group showed improvement in the agonist muscles activity during the hand-to-mouth task.

Clinical Trial Registration:, identifier: NCT03590314


Upper limb (UL) sensorimotor impairments are one of the major determinants of long-term disability in stroke survivors (). Several disturbances are the manifestation of UL impairments after stroke (i.e., muscle weakness, changes in muscle tone, joint disturbances, impaired motor control). However, spasticity and weakness are the primary reason for rehabilitative intervention in the chronic stages (). Historically, spasticity refers to a velocity-dependent increase in tonic stretch reflexes with exaggerated tendon jerks resulting from hyperexcitability of the stretch reflex () while weakness is the loss of the ability to generate the normal amount of force.

From 7 to 38% of post-stroke patients complain of UL spasticity in the first year (). The pathophysiology of spasticity is complicated, and new knowledge has progressively challenged this definition. Processes involving central and peripheral mechanisms contribute to the spastic movement disorder resulting in abnormal regulation of tonic stretch reflex and increased muscle resistance of the passively stretched muscle and deficits in agonist and antagonist coactivation (). The resulting immobilization of the muscle at a fixed length for a prolonged time induces secondary biomechanical and viscoelastic properties changes in muscles and soft tissues, and pain (). These peripheral mechanisms, in turn, leads to further stiffness, and viscoelastic muscle changes (). Whether the muscular properties changes may be adaptive and secondary to paresis are uncertain. However, the management of UL spasticity should combine treatment of both the neurogenic and peripheral components of spasticity ().

UL weakness after stroke is prevalent in both acute and chronic phases of recovery (). It is a determinant of UL function in ADLs and other negative consequences such as bone mineral content (), atrophy and altered muscle pattern of activation. Literature supports UL strengthening training effectiveness for all levels of impairment and in all stages of recovery (). However, a small number of trials have been performed in chronic subgroup patients, and there is still controversy in including this procedure in UL rehabilitation ().

Botulinum toxin (BoNT) injection in carefully selected muscles is a valuable treatment for spastic muscles in stroke patients improving deficits in agonist and antagonist coactivation, facilitating agonist recruitment and increasing active range of motion (). However, improvements in UL activity or performance is modest (). With a view of improving UL function after stroke, moderate to high-quality evidence support combining BoNT treatment with other rehabilitation procedures (). Specifically, the integration of robotics in the UL rehabilitation holds promise for developing high-intensity, repetitive, task-specific, interactive treatment of upper limb (). The combined use of these procedures to compensate for their limitations has been studied in only one pilot RCT reporting positive results in UL function (Fugl-Meyer UL Assessment scale) and muscular activation pattern (). With the limits of the small sample, the results support the value of combining high-intensity UL training by robotics and BoNT treatment in patients with UL spastic paresis.

Clinical scales are currently used to assess the rehabilitation treatment effects, but these outcome measures may suffer from some drawbacks that can be overcome by instrumental assessment as subjectivity, limited sensitivity, and the lack of information on the underlying training effects on motor control (). Instrumental assessment, such as surface electromyography (sEMG) during a functional task execution allows assessing abnormal activation of spastic muscles and deficits of voluntary movements in patients with stroke.

Moreover, the hand-to-mouth task is representative of Activities of Daily Life (ADL) such as eating and drinking. Kinematic analysis of the hand-to-mouth task has been widely used to assess UL functions in individuals affected by neurological diseases showing adequate to more than adequate test-retest reliability in healthy subjects (). The task involves flexing the elbow a slightly flexing the shoulder against gravity, and it is considered to be a paradigmatic functional task for the assessment of spasticity and strength deficits on the elbow muscles (). Although sEMG has been reported to be a useful assessment procedure to detect muscle activity improvement after rehabilitation, limited results have been reported ().

The primary aim of this study was to explore the therapeutic synergisms of combined robot-assisted upper limb training and BoNT treatment on upper limb spasticity. The secondary aim was to evaluate the treatment effects on UL function, muscle strength, and the electromyographic activity of UL muscles during a functional task.

The combined treatment would contribute to decrease UL spasticity and improve function through a combination of training effects between BoNT neurolysis and the robotic treatment. A reduction of muscle tone would parallel improvement in muscle strength ought to the high-intensity, repetitive and task-specific robotic training. Since spasticity is associated with abnormal activation of shortening muscles and deficits in voluntary movement of the UL, the sEMG assessment would target these impairments ().

Materials and Methods

Trial Design

A single-blind RCT with two parallel group is reported. The primary endpoint was the changes in UL spasticity while the secondary endpoints were changes in UL function, muscle strength and the electromyographic activity of UL muscles during a functional task. The study was conducted according to the tenets of the Declaration of Helsinki, the guidelines for Good Clinical Practice, and the Consolidated Standards of Reporting Trials (CONSORT), approved by the local Ethics Committee “Nucleo ricerca clinica–Research and Biostatistic Support Unit” (prog n.2366), and registered at clinical trial (NCT03590314).


Chronic post-stroke patients with upper-limb spasticity referred to the Neurorehabilitation Unit (AOUI Verona) and the Physical Medicine and Rehabilitation Section, “OORR” Hospital (University of Foggia) were assessed for eligibility.

Inclusion criteria were: age > 18 years, diagnosis of ischemic or hemorrhagic first-ever stroke as documented by a computerized tomography scan or magnetic resonance imaging, at least 6 months since stroke, Modified Ashworth Scale (MAS) score (shoulder and elbow) ≤ 3 and ≥1+ (), BoNT injection within the previous 12 weeks of at least one of muscles of the affected upper limb, Mini-Mental State Examination (MMSE) score ≥24 () and Trunk Control Test score = 100/100 ().

Exclusion criteria were: any rehabilitation intervention in the 3 months before recruitment, bilateral cerebrovascular lesion, severe neuropsychologic impairment (global aphasia, severe attention deficit or neglect), joint orthopedic disorders.

All participants were informed regarding the experimental nature of the study. Informed consent was obtained from all subjects. The local ethics committee approved the study.


Each patient underwent a BoNT injection in the paretic limb. The dose of BoNT injected into the target muscle was based on the severity of spasticity in each case. Different commercial formulations of BoNT were used according to the pharmaceutical portfolio contracts of our Hospitals (Onabotulinumtoxin A, Abobotulinumtoxin A, and Incobotulinumtoxin A). The dose, volume and number of injection sites were set accordingly. A Logiq ® Book XP portable ultrasound system (GE Healthcare; Chalfont St. Giles, UK) was used to inject BoNT into the target muscle.

Before the start of the study authors designed the experimental (EG) and the control group (CG) protocols. Two physiotherapists, one for each group, carried out the rehabilitation procedures. Patients of both groups received ten individual sessions (45 min/session, two sessions/week, five consecutive weeks). Treatments were performed in the rehabilitative gym of the G. B. Rossi University Hospital Neurological Rehabilitation Unit, or “OORR” Hospital.

Robot-Assisted UL Training

The Robot-assisted UL Training group was treated using the electromechanical device Armotion (Reha Technology, Olten, Switzerland). It is an end-effector device that allows goal-directed arm movements in a bi-dimensional space with visual feedback. It offers different training modalities such as passive, active, passive-active, perturbative, and assistive modes. The robot can move, drive or oppose the patient’s movement and allows creating a personalized treatment, varying parameters such as some repetitions, execution speed, resistance degree of motion. The exercises available from the software are supported by games that facilitate the functional use of the paretic arm (). The robot is equipped with a control system called “impedance control” that modulates the robot movements for adapting to the motor behavior of the patient’s upper limb. The joints involved in the exercises were the shoulder and the elbow, is the wrist fixed to the device.

The Robot-assisted UL Training consisted of passive mobilization and stretching exercises for affected UL (10 min) followed by robot-assisted exercises (35 min). Four types of exercises contained within the Armotion software and amount of repetitions were selected as follows: (i) “Collect the coins” (45–75 coins/10 min), (ii) “Drive the car” (15–25 laps/10 min), (iii) “Wash the dishes” (40–60 repetitions/10 min), and (iv) “Burst the balloons” (100–150 balloons/5 min) (Figure 1). All exercises were oriented to achieving several goals in various directions, emphasizing the elbow flexion-extension and reaching movement. The robot allows participants to execute the exercises through an “assisted as needed” control strategy. For increment the difficulty, we have varied the assisted and non-assisted modality, increasing the number of repetitions over the study period.

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Figure 1
The upper limb robot-assisted training setting.

Conventional Training

The conventional training consisted of UL passive mobilization and stretching (10 min) followed by UL exercises (35 min) that incorporated single or multi-joint movements for the scapula, shoulder, and elbow, performed in different positions (i.e., supine and standing position). The increase of difficulty and progression of intensity were obtained by increasing ROM, repetitions and performing movements against gravity or slight resistance (). Training parameters were recorded on the patient’s log. […]


Continue —>  Effectiveness of Robot-Assisted Upper Limb Training on Spasticity, Function and Muscle Activity in Chronic Stroke Patients Treated With Botulinum Toxin: A Randomized Single-Blinded Controlled Trial

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[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.

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

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[Abstract] How effective is physical therapy for gait muscle activity in hemiparetic patients who receive botulinum toxin injections?


BACKGROUND: Administration of botulinum neurotoxin A (BoNT-A) to the ankle plantar flexors in patients with hemiplegia reduces the strength of knee extension, which may decrease their walking ability. Studies have reported improvements in walking ability with physical therapy following BoNT-A administration. However, no previous studies have evaluated from an exercise physiology perspective the efficacy of physical therapy after BoNT-A administration for adult patients with hemiplegia.

AIM: To investigate the effects of physical therapy following BoNT-A administration on gait electromyography for patients with hemiparesis secondary to stroke.

DESIGN: Non-randomized controlled trial.

SETTING: Single center.

POPULATION: Thirty-five patients with chronic stroke with spasticity were assigned to BoNT-A monotherapy (N.=18) or BoNT-A plus physical therapy (PT) (N.=17).

METHODS: On the paralyzed side of the body, 300 single doses of BoNT-A were administered intramuscularly to the ankle plantar flexors. Physical therapy was performed for 2 weeks, starting from the day after administration. Gait electromyography was performed and gait parameters were measured immediately before and 2 weeks after BoNT-A administration. Relative muscle activity, coactivation indices, and walking time/distance were calculated for each phase.

RESULTS: For patients who received BoNT-A monotherapy, soleus activity during the loading response decreased 2 weeks after the intervention (P<0.01). For those who received BoNT-A+PT, biceps femoris activity and knee coactivation index during the loading response and tibialis anterior activity during the pre-swing phases increased, whereas soleus and rectus femoris activities during the swing phase decreased 2 weeks after the intervention (P<0.05). These rates of change were significantly greater than those for patients who received BoNT-A monotherapy (P<0.05).
CONCLUSIONS: Following BoNT-A monotherapy, soleus activity during the stance phase decreased and walking ability either remained unchanged or deteriorated. Following BoNT-A+PT, muscle activity and knee joint stability increased during the stance phase, and abnormal muscle activity during the swing phase was suppressed.

CLINICAL REHABILITATION IMPACT: If botulinum treatment of the ankle plantar flexors in stroke patients is targeted to those with low knee extension strength, or if it aims to improve leg swing on the paralyzed side of the body, then physical therapy following BoNT-A administration could be an essential part of the treatment strategy.


via How effective is physical therapy for gait muscle activity in hemiparetic patients who receive botulinum toxin injections? – European Journal of Physical and Rehabilitation Medicine 2019 February;55(1):8-18 – Minerva Medica – Journals

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