Recent Trends in EMG-Based Control Methods for Assistive Robots

Gopura, R. A. R. C.; Bandara, D. S. V.; Gunasekara, J. M. P.; Jayawardane, Tss

doi:10.5772/56174

Cited by 47 publications

(42 citation statements)

References 31 publications

(78 reference statements)

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“…Furthermore, defining the relationship between electromyography (EMG) signals from a muscle group to its corresponding joint movements can lead to advancements in methods for controlling prostheses based on the amputee's intensions (Gopura et al 2013;Kearney and Hunter 1990). Previous work has used EMG to determine the linear relation between upper extremity muscle activation levels and joint stiffness for both static and dynamic conditions (Osu and Hiroaki 1999).…”

Section: Introductionmentioning

confidence: 99%

Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction

Dallali

Knop

Castelino

et al. 2017

Int J Intell Robot Appl

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The human ankle has a critical role in locomotion and estimating its impedance is essential for human gait rehabilitation. The ankle is the first major joint that regulates the contact forces between the human body and the environment, absorbing shocks during the stance, and providing propulsion during walking. Its impedance varies with the level of the muscle activation. Hence, characterizing the complex relation between the ankle impedance and the lower leg's muscle activation levels may improve our understanding of the neuromuscular characteristics of the ankle. Most ankle-foot prostheses do not have a degree of freedom in the transverse plane, which can cause high amounts of shear stress to be applied to the socket and can lead to secondary injuries. Quantifying the ankle impedance in the transverse plane can guide the design for a variable impedance ankle-foot prosthesis that can significantly reduce the shear stress on the socket. This paper presents the results of applying artificial neural networks (ANN) to learn and estimate the relation between the ankle impedance in the transverse plane under non-load bearing condition using electromyography signals (EMG) from the lower leg muscles. The Anklebot was used to apply pseudorandom perturbations to the human ankle in the transverse plane while the other degrees of freedom (DOF) in the sagittal and frontal planes were constrained. The mechanical impedance of the ankle was estimated using a previously proposed stochastic identification method that describes the ankle impedance as a function of the applied disturbances torques and the ankle motion output. The ankle impedance with relaxed muscles and with the lower leg's muscle activations at 10 and 20% of the maximum voluntary contraction were estimated. The proposed ANN effectively predicts the ankle impedance within 85% accuracy (±5 Nm/rad absolute) for nine out of ten subjects given the root-mean-squared (rms) of the EMG signals. The main contribution of this paper is to quantify the relationship between lower leg muscle EMG signals and the ankle impedance in the transverse plane to pave the way towards designing and controlling this degree of freedom in a future ankle-foot prosthesis.

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Section: Introductionmentioning

confidence: 99%

Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction

Dallali

Knop

Castelino

et al. 2017

Int J Intell Robot Appl

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“…To generate control signals, commercial systems use robust threshold approaches [10], whereas experimental systems use pattern recognition techniques [11]. A calibration is necessary to adapt the system to the user.…”

Section: Introductionmentioning

confidence: 99%

Control scheme selection in human-machine- interfaces by analysis of activity signals

Doneit

Mikut

Liebetanz

et al. 2016

Current Directions in Biomedical Engineering

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Human-Machine Interfaces in rehabilitation engineering often use activity signals. Examples are electrical wheelchairs or prostheses controlled by means of muscle contractions. Activity signals are user-dependent and often reflect neurological weaknesses. Thus, not all users are able to operate the same control scheme in a robust manner. To avoid under-and overstraining, the interface ideally uses a control scheme which reflects the user's control ability best. Therefore, we explored typical phenomena of activation signals. We derive criteria to quantify the user's performance and abilities and present a routine which automatically selects and adapts one of three control schemes being best suited.

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“…Many EMG control systems are currently available that are capable of controlling a precise robotic prosthetic limb movements [16] such as a hand fingers, an elbow, or a wrist. These systems can extract the control information from the evaluated EMG signals based on amplitude or the rate of change of time varying EMG signal pattern.…”

Section: Introductionmentioning

confidence: 99%

Subject-specific EMG pattern classification ofactive hand movements for prosthesis applications

Bansod¹,

Raurale²

2014

2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies

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The prosthesis hand amputees are highly helpful for various active hand movements based on wrist and elbow mobility for specific subject. In the field of rehabilitation, development of an advanced human-machine interface has been an interesting research topic in which biomedical electromyography (EMG) signals, play a vital role. Capturing, pre-processing, feature extraction and classification of EMG is very desirable which allows more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological advancements in prosthetic applications. This paper concerns with the capturing of real-time active hand movements EMG signals based on wrist-elbow mobility for simultaneous classification of features. The Anterior and Posterior forearm muscles are considered for proficient manipulation of EMG signals. The Feature is extracted using statistical first order time-frequency scaling analysis with pattern classification via linear discriminant analysis (LDA) which estimates the classification rate of about (89-91)%.

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Recent Trends in EMG-Based Control Methods for Assistive Robots

Cited by 47 publications

References 31 publications

Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction

Using lower extremity muscle activity to obtain human ankle impedance in the external–internal direction

Control scheme selection in human-machine- interfaces by analysis of activity signals

Subject-specific EMG pattern classification ofactive hand movements for prosthesis applications

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