EMG Based Control of a Robotic Exoskeleton for Shoulder and Elbow Motion Assist

Rahman, Mohammad Habibur; Ochoa-Luna, Cristóbal; Saad, Maarouf

doi:10.12720/joace.3.4.270-276

Cited by 43 publications

(22 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…By using this prediction, a reference joint position trajectory was computed and fed to a PD controller with gravity and friction compensation. In [66] an EMG-based version of the SMERL controller was provided. In this case, EMG signals, transformed to a desired position reference through a muscle model, acted as the reference for the position feedback controller.…”

Section: A Assistive Modesmentioning

confidence: 99%

Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

Proietti

Crocher

Roby-Brami

et al. 2016

IEEE Rev. Biomed. Eng.

306

225

View full text Add to dashboard Cite

Since the late 1990s, there has been a burst of research on robotic devices for poststroke rehabilitation. Robot-mediated therapy produced improvements on recovery of motor capacity; however, so far, the use of robots has not shown qualitative benefit over classical therapist-led training sessions, performed on the same quantity of movements. Multidegree-of-freedom robots, like the modern upper-limb exoskeletons, enable a distributed interaction on the whole assisted limb and can exploit a large amount of sensory feedback data, potentially providing new capabilities within standard rehabilitation sessions. Surprisingly, most publications in the field of exoskeletons focused only on mechatronic design of the devices, while little details were given to the control aspects. On the contrary, we believe a paramount aspect for robots potentiality lies on the control side. Therefore, the aim of this review is to provide a taxonomy of currently available control strategies for exoskeletons for neurorehabilitation, in order to formulate appropriate questions toward the development of innovative and improved control strategies.

show abstract

Section: A Assistive Modesmentioning

confidence: 99%

Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

Proietti

Crocher

Roby-Brami

et al. 2016

IEEE Rev. Biomed. Eng.

306

225

View full text Add to dashboard Cite

show abstract

“…The same study also looked at a different control method that used the same sEMG signals but instead utilized machine learning, specifically linear discriminant analysis, to classify movements [ 12 ]. A study using the ETS-MARSE related sEMG signals to muscle forces and torques using a proportional constant for each muscle [ 13 ]. These various studies show the interest and promise of EMG, as well as the limitations of the current use of EMG input.…”

Section: Introductionmentioning

confidence: 99%

Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

Schabron

Desai

Yihun

2021

Sensors

View full text Add to dashboard Cite

Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user’s intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases.

show abstract

“…The implementation of this method for daily routine activities or factory tasks is not feasible, where the user carries objects of different attributes. To overcome this challenge, Electromyography (EMG) based estimation methods are used instead to determine joint torques and provide assistance through exoskeletons McDonald et al (2017); Mangukiya et al (2017); Leonardis et al (2015); Abdallah et al (2017); Mghames et al (2017); Tang et al (2014); Rahman et al (2015); Li et al (2013); Kiguchi and Hayashi (2012). In these methods the assistance to each joint is provided by analyzing the muscle activities of its prime mover muscle group.…”

Section: Introductionmentioning

confidence: 99%

Payload estimation using forcemyography sensors for control of upper-body exoskeleton in load carrying assistance

Islam¹,

Bai²

2019

MIC

View full text Add to dashboard Cite

In robotic assistive devices, the determination of required assistance is vital for proper functioning of assistive control. This paper presents a novel solution to measure conveniently and accurately carried payload in order to estimate the required assistance level. The payload is estimated using upper arm forcemyography (FMG) through a sensor band made of force sensitive resistors. The sensor band is worn on the upper arm and is able to measure the change of normal force applied due to muscle contraction. The readings of the sensor band are processed using support vector machine (SVM) regression technique to estimate the payload. The developed method was tested on human subjects, carrying a payload. Experiments were further conducted on an upper-body exoskeleton to provide the required assistance. The results show that the developed method is able to estimate the load carrying status, which can be used in exoskeleton control to provide effectively physical assistance needed.

show abstract

EMG Based Control of a Robotic Exoskeleton for Shoulder and Elbow Motion Assist

Cited by 43 publications

References 12 publications

Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies

Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

Payload estimation using forcemyography sensors for control of upper-body exoskeleton in load carrying assistance

Contact Info

Product

Resources

About