Dynamic training protocol improves the robustness of PR-based myoelectric control

Gu, Yikun; Yang, Dapeng; Osborn, Luke; Liu, Hong

doi:10.1016/j.bspc.2016.08.017

Cited by 29 publications

(23 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the classification results for our proposed approach are comparable with previous studies [31,36,37], it is different for two main reasons. Previous studies examine the classification performance of different hand gestures, including wrist motion, hand open/close, a small number of grasp types (2-4) and in some cases the resting condition [38][39][40]. Whereas, we focus only on grasping gestures with different finger configurations.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Decoding the grasping intention from electromyography during reaching motions

Batzianoulis

Krausz

Simon

et al. 2018

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

BackgroundActive upper-limb prostheses are used to restore important hand functionalities, such as grasping. In conventional approaches, a pattern recognition system is trained over a number of static grasping gestures. However, training a classifier in a static position results in lower classification accuracy when performing dynamic motions, such as reach-to-grasp. We propose an electromyography-based learning approach that decodes the grasping intention during the reaching motion, leading to a faster and more natural response of the prosthesis.Methods and ResultsEight able-bodied subjects and four individuals with transradial amputation gave informed consent and participated in our study. All the subjects performed reach-to-grasp motions for five grasp types, while the elecromyographic (EMG) activity and the extension of the arm were recorded. We separated the reach-to-grasp motion into three phases, with respect to the extension of the arm. A multivariate analysis of variance (MANOVA) on the muscular activity revealed significant differences among the motion phases. Additionally, we examined the classification performance on these phases. We compared the performance of three different pattern recognition methods; Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) with linear and non-linear kernels, and an Echo State Network (ESN) approach. Our off-line analysis shows that it is possible to have high classification performance above 80% before the end of the motion when with three-grasp types. An on-line evaluation with an upper-limb prosthesis shows that the inclusion of the reaching motion in the training of the classifier importantly improves classification accuracy and enables the detection of grasp intention early in the reaching motion.ConclusionsThis method offers a more natural and intuitive control of prosthetic devices, as it will enable controlling grasp closure in synergy with the reaching motion. This work contributes to the decrease of delays between the user’s intention and the device response and improves the coordination of the device with the motion of the arm.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0396-5) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionmentioning

confidence: 99%

“…This outcome is aligned with the findings of [38], where a classifier that takes into account different arm positions outperformed a single-position classifier. The benefits of a dynamic training protocol are also shown in [40]. Our work is complementary to these approaches in that it focuses on the timing of classification.…”

Section: Discussionmentioning

confidence: 99%

Decoding the grasping intention from electromyography during reaching motions

Batzianoulis

Krausz

Simon

et al. 2018

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

“…Static forearm orientation experiments have been conducted by many researchers [23,[119][120][121][122][123][124][125]. Within their protocols, shoulder and elbow joint angles are typically held constant throughout all collections.…”

Section: Static Forearm Orientationmentioning

confidence: 99%

“…The most commonly reported limb position experimental protocol in the literature is one that uses static limb positions [21,22,94,[113][114][115][116]119,[121][122][123][126][127][128][129][130][131][132][133][134][135][136][137][138]. Among the articles surveyed, these make up 50% of all experiments.…”

Section: Static Limb Positionmentioning

confidence: 99%

“…Contrary to static forearm orientation and static limb position experimental protocols, dynamic 2D space experimental protocols involve transitions between elbow and wrist joint angles within the collection window [22,116,122,123,130,132,[139][140][141][142]. Conventionally, these experiments require subjects to sustain wrist or finger motions while transitioning between two or three limb positions.…”

Section: Dynamic 2d Spacementioning

confidence: 99%

See 1 more Smart Citation

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

View full text Add to dashboard Cite

This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

show abstract

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Tchantchane,

Zhou,

Zhang

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human–human and human–computer or human–machine interactions. Noninvasive, on‐body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human–machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper‐limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor‐based HGR systems are also analyzed to provide perspectives for future research and progress.

show abstract

Dynamic training protocol improves the robustness of PR-based myoelectric control

Cited by 29 publications

References 41 publications

Decoding the grasping intention from electromyography during reaching motions

Decoding the grasping intention from electromyography during reaching motions

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Contact Info

Product

Resources

About