Posts Tagged EMG

[WEB SITE] One step at a time


PITTSBURGH (March 7, 2017) … The promise of exoskeleton technology that would allow individuals with motor impairment to walk has been a challenge for decades. A major difficulty to overcome is that even though a patient is unable to control leg muscles, a powered exoskeleton could still cause muscle fatigue and potential injury.

However, an award from the National Science Foundation’s Cyber-Physical Systems (CPS) program will enable researchers at the University of Pittsburgh to develop an ultrasound sensor system at the heart of a hybrid exoskeleton that utilizes both electrical nerve stimulation and external motors.

Principal investigator of the three year, $400,000 award is Nitin Sharma, assistant professor of mechanical engineering and materials science at Pitt’s Swanson School of Engineering. Co-PI is Kang Kim, associate professor of medicine and bioengineering. The Pitt team is collaborating with researchers led by Siddhartha Sikdar, associate professor of bioengineering and electrical and computer engineering at George Mason University, who also received a $400,000 award for the CPS proposal, “Synergy: Collaborative Research: Closed-loop Hybrid Exoskeleton utilizing Wearable Ultrasound Imaging Sensors for Measuring Fatigue.”

This latest funding furthers Dr. Sharma’s development of hybrid exoskeletons that combine functional electrical stimulation (FES), which uses low-level electrical currents to activate leg muscles, with powered exoskeletons, which use electric motors mounted on an external frame to move the wearer’s joints.

“One of the most serious impediments to developing a human exoskeleton is determining how a person who has lost gait function knows whether his or her muscles are fatigued. An exoskeleton has no interface with a human neuromuscular system, and the patient doesn’t necessarily know if the leg muscles are tired, and that can lead to injury,” Dr. Sharma explained. “Electromyography (EMG), the current method to measure muscle fatigue, is not reliable because there is a great deal of electrical “cross-talk” between muscles and so differentiating signals in the forearm or thigh is a challenge.”

To overcome the low signal-to-noise ratio of traditional EMG, Dr. Sharma partnered with Dr. Kim, whose research in ultrasound focuses on analyzing muscle fatigue.

“An exoskeleton biosensor needs to be noninvasive, but systems like EMG aren’t sensitive enough to distinguish signals in complex muscle groups,” Dr. Kim said. “Ultrasound provides image-based, real-time sensing of complex physical phenomena like neuromuscular activity and fatigue. This allows Nitin’s hybrid exoskeleton to switch between joint actuators and FES, depending upon the patient’s muscle fatigue.”

In addition to mating Dr. Sharma’s hybrid exoskeleton to Dr. Kim’s ultrasound sensors, the research group will develop computational algorithms for real-time sensing of muscle function and fatigue. Human subjects using a leg-extension machine will enable detailed measurement of strain rates, transition to fatigue, and full fatigue to create a novel muscle-fatigue prediction model. Future phases will allow the Pitt and George Mason researchers to develop a wearable device for patients with motor impairment.

“Right now an exoskeleton combined with ultrasound sensors is just a big machine, and you don’t want to weigh down a patient with a backpack of computer systems and batteries,” Dr. Sharma said. “The translational research with George Mason will enable us to integrate a wearable ultrasound sensor with a hybrid exoskeleton, and develop a fully functional system that will aid in rehabilitation and mobility for individuals who have suffered spinal cord injuries or strokes.”


Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Source: One step at a time | EurekAlert! Science News

, , , , , , , ,

Leave a comment

[ARTICLE] Long-Term Plasticity in Reflex Excitability Induced by Five Weeks of Arm and Leg Cycling Training after Stroke – Full Text HTML


Neural connections remain partially viable after stroke, and access to these residual connections provides a substrate for training-induced plasticity. The objective of this project was to test if reflex excitability could be modified with arm and leg (A & L) cycling training. Nineteen individuals with chronic stroke (more than six months postlesion) performed 30 min of A & L cycling training three times a week for five weeks. Changes in reflex excitability were inferred from modulation of cutaneous and stretch reflexes. A multiple baseline (three pretests) within-subject control design was used. Plasticity in reflex excitability was determined as an increase in the conditioning effect of arm cycling on soleus stretch reflex amplitude on the more affected side, by the index of modulation, and by the modulation ratio between sides for cutaneous reflexes. In general, A & L cycling training induces plasticity and modifies reflex excitability after stroke.

1. Introduction

The arms and the legs are coupled in the human nervous system such that activity in the arms affects activity in the legs and vice versa. In quadrupeds, forelimb–hindlimb coordination is well documented and has been attributed to propriospinal linkages between cervical and lumbosacral spinal central pattern-generating networks [1,2,3,4,5,6]. Bipedal human locomotion is likely built upon elements of quadrupedal coordination [2,5], where it involves coordination of all four limbs. Only indirect evidence for quadrupedal locomotor linkages exists, however.
The modulation of reflex amplitudes can be used to probe for changes in interlimb neural activity [4,7]. Investigations of soleus stretch and H-reflex modulation during rhythmic arm movement provide evidence of neuronal coupling between the arms and the legs [2,3,8,9,10]. Examining cutaneous reflexes during rhythmic movements can also probe for interactions between the limbs. In this context, a widespread interlimb network is revealed by the extensive distribution of reflexes across many muscles in both the arms and the legs regardless of which limb is directly stimulated [4,11,12]. In addition, phase-dependent modulation found in muscles of all four limbs during rhythmic movement is suggestive of coupling between segmental spinal networks [12,13,14,15,16]. Regulation of rhythmic arm and leg movement is supported by somatosensory linkages in the form of interlimb reflexes [12,17,18] and neural coupling between lumbar and cervical spinal cord networks [10,19,20,21,22]. …

Figure 1. Illustration of the testing and training protocols. A multiple baseline within-subject control design was used for this study. An A & L cycle ergometer (Sci-Fit Pro 2) was used for training. The setups for stretch reflex and cutaneous reflex testing are shown. Muscles of interest are shown with a gray oval, and electrical stimulation is shown with a black lightning bolt. For the stretch reflex setup, a brief vibration was delivered to the triceps surae tendon and the reflex was recorded from the soleus (SOL) muscle, separately for each side. For the cutaneous reflex setup, simultaneous electrical stimulation was applied to the superficial radial (SR) and the superficial peroneal (SP) nerves, and reflexes were recorded bilaterally from the soleus (SOL), tibialis anterior (TA), flexor carpi radialis (FCR), and the posterior deltoid (PD) muscles.

, , , , ,

Leave a comment

[ARTICLE] An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic Finger – Full Text 

In this paper, we present an electromyographic (EMG) control interface for a supernumerary robotic finger. This novel wearable robot can be used to compensate the missing grasping abilities in chronic stroke patients or to augment human healthy hand so to enhance its grasping capabilities and workspace. The proposed EMG interface controls the motion of the robotic extra finger and its joint compliance. In particular, we use a commercial EMG armband for gesture recognition to be associated with the motion control of the robotic device and surface one channel EMG electrodes interface to regulate the compliance of the robotic device. We also present an updated version of a robotic extra finger where the adduction/abduction motion is realized through ball bearing and spur gears mechanism. We validated the proposed interface with two sets of experiments related to compensation and augmentation. In the first set of experiments, different bi-manual tasks have been performed with the help of the robotic device and simulating a paretic hand. In the second set, the robotic extra finger is used to enlarge the workspace and manipulation capability of healthy hands. In both the sets, the same EMG control interface has been used. The obtained results demonstrate that the proposed control interface is intuitive and can successfully be used for both compensation and augmentation purposes. The proposed approach can be exploited also for the control of different wearable devices that has to actively cooperate with the human limbs.

Continue —> Frontiers | An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic Finger | Frontiers in Neurorobotics

Figure 1. On left, the exploded cad view, whereas on right, the prototype of the robotic extra finger. Four modules are used for the flexion/extension motion, while the revolute joint based on bearings and spur gears mechanism at the finger base is used for the adduction/abduction motion. The device can be worn on the forearm through an elastic band.

, , , , , ,

Leave a comment

[ARTICLE] Wearable System for Device Control using Bio-Electrical Signal – Full Text PDF


In today’s world, wearable devices are progressively being used for the enhancement of the nature of the life of individuals. Human Machine Interface (HMI) has been studied for dominant the mechanical device rehabilitation aids through biosignals like EOG and EMG etc., and so on. EMG signals have been studied in detail due to the occurrence of a definite signal pattern. The current proposal focuses on the advancement of a Wearable Device control by using EMG signals of hand movements for controlling the electronic devices. EMG signals are utilized for the production of the control indicators to develop the device control. Also, an EMG sign procurement framework was produced. To create different control signals relying on the sufficiency and length of time of signal segments, the obtained EMG signals were then prepared for device control.

1. Introduction

1.1 Need for Rehabilitation Techniques

A major a part of our society is littered with one or the opposite reasonably disabilities owing to accidents and neuro-logic disorders. These patients rely upon the members of the family or care takers for his or her day to day activities like quality, communication with atmosphere, mistreatment the home instrumentation, etc1,2.

Rehabilitation devices facilitate the patients with disabilities to measure, work, play or study severally. Moreover, they improve the standard of life led by these individuals and maintain their shallowness.

1.2 EMG based Methods

Electrical potentials generated during muscle contraction are measured by EMG. The contraction of somatic cell takes place once it receives associate degree impulse. The myogram ascertained is that the add of all the action potentials that occur round the conductor site. In most of the cases, the amplitude of the myogram will increase as a result of contraction. Myogram signals is used for a range of applications together with clinical applications, HCI and interactive gaming. They’re non-heritable simply and are comparatively high in magnitude than alternative bio-signals. On the opposite hand, myogram signals area unit simply liable to noise. myogram signals contain difficult styles of noise as a result of inherent instrumentation noise, non-particulate
radiation, motion artifacts, and therefore the interaction of various tissues. Hence, to filter the unwanted noise in myogram, preprocessing is critical3. The myogram signals even have completely different signatures counting on age, muscle development, motor unit ways, skin fat layer, and gesture designs. The external appearances of 2 individuals’ gestures would possibly look identical, however the characteristic myogram signals area unit completely different4.

Full Text PDF


, , , ,

Leave a comment

[Abstract] Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG


One of the recent trends in gait rehabilitation is to incorporate bio-signals, such as electromyography (EMG) or electroencephalography (EEG), for facilitating neuroplasticity, i.e. top-down approach. In this study, we investigated decoding stroke patients’ gait intention through a wireless EEG system. To overcome patient-specific EEG patterns due to impaired cerebral cortices, common spatial patterns (CSP) was employed. We demonstrated that CSP filter can be used to maximize the EEG signal variance-ratio of gait and standing conditions. Finally, linear discriminant analysis (LDA) classification was conducted, whereby the average accuracy of 73.2% and the average delay of 0.13 s were achieved for 3 chronic stroke patients. Additionally, we also found out that the inverse CSP matrix topography of stroke patients’ EEG showed good agreement with the patients’ paretic side.

Source: IEEE Xplore Document – Detecting voluntary gait intention of chronic stroke patients towards top-down gait rehabilitation using EEG

, , , , , , , , , , ,

Leave a comment

[REVIEW] Mobility and the Lower Extremity | EBRSR – Evidence-Based Review of Stroke Rehabilitation – Full Text PDF

Chapter 9

Mobility and the Lower Extremity

Rehabilitation techniques of sensorimotor complications post stroke fall loosely into one of two categories; the compensatory approach or the restorative approach. While some overlap exists, the underlying philosophies of care are what set them apart. The goal of the compensatory approach towards treatment is not necessarily on improving motor recovery or reducing impairments but rather on teaching patients a new skill, even if it only involves pragmatically using the non-involved side (Gresham et al. 1995). The restorative approach focuses on traditional physical therapy exercises and neuromuscular facilitation, which involves sensorimotor stimulation, exercises and resistance training, designed to enhance motor recovery and maximize brain recovery of the neurological impairment (Gresham et al. 1995).In this review, rehabilitation of mobility and lower extremity complications is assessed. An overview of literature pertaining to the compensatory approach and the restorative approach is provided. Treatment targets discussed include balance retraining, gait retraining, strength training, cardiovascular conditioning and treatment of contractures in the lower extremities. Technologies used to aid rehabilitation include assistive devices, electrical stimulation, and splints.

For evidence tables, please click here.

Source: Mobility and the Lower Extremity | EBRSR – Evidence-Based Review of Stroke Rehabilitation

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

Leave a comment

[Abstract] On the use of wearable sensors to enhance motion intention detection for a contralaterally controlled FES system.

During the last years, there has been a relevant progress in motor learning and functional recovery after the occurrence of a brain lesion. Rehabilitation of motor function has been associated to motor learning that occurs during repetitive, frequent and intensive training.

Contralaterally controlled functional electrical stimulation (CCFES) is a new therapy designed to improve the recovery of paretic limbs after stroke, that could provide repetitive training-based therapies and has been developed to control the upper and lower limbs movements in response to user’s intentionality.

Electromyography (EMG) signals reflect directly the human motion intention, so it can be used as input information to control a CCFES system. Implementation of the EMG-based pattern recognition is not easy to be accomplished due to some difficulties, among them that the activity level of each muscle for a certain motion is different between each person. Inertial Measurement Units (IMU) is a kind of wearable sensors that are used to gather movement data. IMUs could provide valuable kinematic information in an EMG-based pattern recognition process to improve classification.

This work describes the use of IMUS to improve detecting motion intention from EMG data. Results shows that myoelectric algorithm using information from IMUs was better in classification of seven movements at the upper-limb level that algorithm using only EMG data.

Source: IEEE Xplore Abstract (Abstract) – On the use of wearable sensors to enhance motion intention detection for a contralaterally controlle…

, , , , , , ,

Leave a comment

[Abstract] A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application. 


This paper presents a soft robotic glove designed to assist individuals with functional grasp pathologies in performing activities of daily living. The glove utilizes soft fabric-regulated pneumatic actuators that are low-profile and require lower pressure than previously developed actuators. They are able to support fingers and thumb motions during hand closure. Upon pressurization, the actuators are able to generate sufficient force to assist in hand closing and grasping during different manipulation tasks. In this work, experiments were conducted to evaluate the performances of the actuators as well as the glove in terms of its kinetic and kinematic assistance on a healthy participant. Additionally, surface electromyography and radio-frequency identification techniques were adopted to detect user intent to activate or deactivate the glove. Lastly, we present preliminary results of a healthy participant performing different manipulation tasks with the soft robotic glove controlled by surface electromyography and radio-frequency identification techniques.

Source: IEEE Xplore Abstract (Abstract) – A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assisti…

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

Leave a comment

[ARTICLE] Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site | Journal of NeuroEngineering and Rehabilitation – Full Text



Functional Electrical Stimulation (FES) is increasingly applied in neurorehabilitation. Particularly, the use of electrode arrays may allow for selective muscle recruitment. However, detecting the best electrode configuration constitutes still a challenge.


A multi-contact set-up with thirty electrodes was applied for combined FES and electromyography (EMG) recording of the forearm. A search procedure scanned all electrode configurations by applying single, sub-threshold stimulation pulses while recording M-waves of the extensor digitorum communis (EDC), extensor carpi radialis (ECR) and extensor carpi ulnaris (ECU) muscles. The electrode contacts with the best electrophysiological response were then selected for stimulation with FES bursts while capturing finger/wrist extension and radial/ulnar deviation with a kinematic glove.


The stimulation electrodes chosen on the basis of M-waves of the EDC/ECR/ECU muscles were able to effectively elicit the respective finger/wrist movements for the targeted extension and/or deviation with high specificity in two different hand postures.


A subset of functionally relevant stimulation electrodes could be selected fast, automatic and non-painful from a multi-contact array on the basis of muscle responses to subthreshold stimulation pulses. The selectivity of muscle recruitment predicted the kinematic pattern. This electrophysiologically driven approach would thus allow for an operator-independent positioning of the electrode array in neurorehabilitation.

Continue – Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site | Journal of NeuroEngineering and Rehabilitation | Full Text

, , , , , ,

Leave a comment

[Abstract] The Soft-SixthFinger: a Wearable EMG Controlled Robotic Extra-Finger for Grasp Compensation in Chronic Stroke Patients. – IEEE Xplore



This paper presents the Soft-SixthFinger, a wearable robotic extra-finger designed to be used by chronic stroke patients to compensate for the missing hand function of their paretic limb. The extra-finger is an underactuated modular structure worn on the paretic forearm by means of an elastic band. The device and the paretic hand/arm act like the two parts of a gripper working together to hold an object. The patient can control the flexion/extension of the robotic finger through the eCap, an Electromyography-based (EMG) interface embedded in a cap. The user can control the device by contracting the frontalis muscle. Such contraction can be achieved simply moving his or her eyebrows upwards. The Soft-SixthFinger has been designed as tool that can be used by chronic stroke patients to compensate for grasping in many Activities of Daily Living (ADL). It can be wrapped around the wrist and worn as a bracelet when not used. The light weight and the complete wireless connection with the EMG interface guarantee a high portability and wearability. We tested the device with qualitative experiments involving six chronic stroke patients. Results show that the proposed system significantly improves the performances of the patients in the proposed tests and, more in general, their autonomy in ADL.

Source: IEEE Xplore Abstract – The Soft-SixthFinger: a Wearable EMG Controlled Robotic Extra-Finger for Grasp Compensation in Chron…

, , , , , , , , ,

Leave a comment

%d bloggers like this: