Online subject-independent modeling of sEMG signals for the motion of a single robot joint

Stival, Francesca; Michieletto, Stefano; Pagello, Enrico

doi:10.1109/biorob.2016.7523780

Cited by 11 publications

(20 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Seven electrodes were placed on specific finger and thumb muscles and data analysis was done with simple RMS feature and SVM classifier. With the combination of classic and novel processing methods, the additional data from accelerometers [65] and motion capture [66] contributed to very high classification accuracies of over 90%, even for a subject base of 40 individuals.…”

Section: User Independencementioning

confidence: 99%

See 1 more Smart Citation

Review on EMG Acquisition and Classification Techniques: Towards Zero Retraining in the Influence of User and Arm Position Independence

Fu¹,

Hashim²,

Jamaludin³

et al. 2021

IJIE

View full text Add to dashboard Cite

The surface electromyogram (EMG) is widely studied and applied in machine control. Recent methods of classifying hand gestures reported classification rates of over 95%. However, the majority of the studies made were performed on a single user, focusing solely on the gesture classification. These studies are restrictive in practical sense: either focusing on just gestures, multi-user compatibility, or rotation independence. The variations in EMG signals due to these conditions present a challenge to the practical application of EMG devices, often requiring repetitious training per application. To the best of our knowledge, there is little comprehensive review of works done in EMG classification in the combined influence of user-independence, rotation and hand exchange. Therefore, in this paper we present a review of works related to the practical issues of EMG with a focus on the EMG placement, and recent acquisition and computing techniques to reduce training. First, we provided an overview of existing electrode placement schemes. Secondly, we compared the techniques and results of single-subject against multi-subject, multi-position settings. As a conclusion, the study of EMG classification in this direction is relatively new. However the results are encouraging and strongly indicate that EMG classification in a broad range of people and tolerance towards arm orientation is possible, and can pave way for more flexible EMG devices.

show abstract

Section: User Independencementioning

confidence: 99%

“…Classification accuracy could be improved by Their method effectively reduced classification error from 18% to 5% [65,66] & [68]. The method was applied to 10 able-bodied subjects, using 8 channels of wet electrodes.…”

Section: Rotation and Position Independencementioning

confidence: 99%

Review on EMG Acquisition and Classification Techniques: Towards Zero Retraining in the Influence of User and Arm Position Independence

Fu¹,

Hashim²,

Jamaludin³

et al. 2021

IJIE

View full text Add to dashboard Cite

show abstract

“…In order to test how a classifier behaves for different users most research uses the leave-one-out approach where information is gathered from many subjects and subsequently the classifier is trained with data from all but one subject and tested on this specific subject (Matsubara et al, 2011 ; Gibson et al, 2013 ; Ison and Artemiadis, 2013 ; Matsubara and Morimoto, 2013 ; Guo et al, 2015 ; Park et al, 2016 ; Stival et al, 2016 ).…”

Section: Emg Variability Between Subjectsmentioning

confidence: 99%

“…Stival et al ( 2016 ) proposed an online Gaussian Mixture Model framework, in order to adapt a model constructed from the pooled data from multiple users to a new user. They were able to provide good results when tested on a new user and proved that by updating the existing model by adding information gathered from the new subject improves the performance of their system.…”

Section: Emg Variability Between Subjectsmentioning

confidence: 99%

See 1 more Smart Citation

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Kyranou¹,

Vijayakumar

Erden

2018

Front. Neurorobot.

View full text Add to dashboard Cite

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.

show abstract

Comparing the efficiency of artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics

Batayneh

Abdulhay

Alothman

2022

Digital Communications and Networks

View full text Add to dashboard Cite

Online subject-independent modeling of sEMG signals for the motion of a single robot joint

Cited by 11 publications

References 11 publications

Review on EMG Acquisition and Classification Techniques: Towards Zero Retraining in the Influence of User and Arm Position Independence

Review on EMG Acquisition and Classification Techniques: Towards Zero Retraining in the Influence of User and Arm Position Independence

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Comparing the efficiency of artificial neural networks in sEMG-based simultaneous and continuous estimation of hand kinematics

Contact Info

Product

Resources

About