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

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1613 ◽  
Author(s):  
Evan Campbell ◽  
Angkoon Phinyomark ◽  
Erik Scheme
Keyword(s):  
Dimensional Space ◽  
Position Effect ◽  
Myoelectric Control ◽  
Assistive Technologies ◽  
Experimental Designs ◽  
Systematic Research ◽  
Learning Approaches ◽  
Intensity Effect ◽  
Limb Position ◽  
Nonlinear Complexity

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.

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

2020 ◽  
Author(s):  
Evan Campbell ◽  
Angkoon Phinyomark ◽  
Erik Scheme
Keyword(s):  
Dimensional Space ◽  
Position Effect ◽  
Myoelectric Control ◽  
Assistive Technologies ◽  
Experimental Designs ◽  
Learning Approaches ◽  
Intensity Effect ◽  
Limb Position ◽  
Nonlinear Complexity ◽  
Controlled Environments

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 performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic 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 performance was 13% and 20% lower in realistic environments compared to controlled environments 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 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.

scholarly journals An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7404
Author(s):  
Veronika Spieker ◽  
Amartya Ganguly ◽  
Sami Haddadin ◽  
Cristina Piazza
Keyword(s):  
Pattern Recognition ◽  
Position Effect ◽  
Adaptive Method ◽  
Completion Rate ◽  
Hand Motion ◽  
Limb Position ◽  
Upper Limb Prostheses ◽  
New Information ◽  
Classical Pattern ◽  
External Loads

Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development.

Resolving the Limb Position Effect in Myoelectric Pattern Recognition

2011 ◽  
Vol 19 (6) ◽  
pp. 644-651 ◽  
Author(s):  
A. Fougner ◽  
E. Scheme ◽  
A. D. C. Chan ◽  
K. Englehart ◽  
Ø. Stavdahl
Keyword(s):  
Pattern Recognition ◽  
Position Effect ◽  
Limb Position

scholarly journals A transdiagnostic study of children with problems of attention, learning and memory (CALM)

2018 ◽  
Author(s):  
Joni Holmes ◽  
Annie Bryant ◽  
Susan E. Gathercole ◽  
Keyword(s):  
Learning And Memory ◽  
Dimensional Space ◽  
Learning Difficulties ◽  
Cognitive Learning ◽  
Learning Problems ◽  
Learning Approaches ◽  
Struggling Learners ◽  
Effective Prevention ◽  
Complex Needs ◽  
Free Data

AbstractBackgroundA substantial proportion of the school-age population experience cognitive-related learning difficulties. Not all children who struggle at school receive a diagnosis, yet their problems are sufficient to warrant additional support. Understanding the causes of learning difficulties is the key to developing effective prevention and intervention strategies for struggling learners. The aim of this project is to apply a transdiagnostic approach to children with cognitive developmental difficulties related to learning to discover the underpinning mechanisms of learning problems.Methods / DesignA cohort of 1000 children aged 5 to 18 years is being recruited. The sample consists of 800 children with problems in attention, learning and / memory, as identified by a health or educational professional, and 200 typically-developing children recruited from the same schools as those with difficulties. All children are completing assessments of cognition, including tests of phonological processing, short-term and working memory, attention, executive function and processing speed. Their parents/ carers are completing questionnaires about the child’s family history, communication skills, mental health and behaviour. Children are invited for an optional MRI brain scan and are asked to provide an optional DNA sample (saliva).Hypothesis-free data-driven methods will be used to identify the cognitive, behavioural and neural dimensions of learning difficulties. Machine-learning approaches will be used to map the multi-dimensional space of the cognitive, neural and behavioural measures to identify clusters of children with shared profiles. Finally, group comparisons will be used to test theories of development and disorder.DiscussionOur multi-systems approach to identifying the causes of learning difficulties in a heterogeneous sample of struggling learners provides a novel way to enhance our understanding of the common and complex needs of the majority of children who struggle at school. Our broad recruitment criteria targeting all children with cognitive learning problems, irrespective of diagnoses and comorbidities, are novel and make our sample unique. Our dataset will also provide a valuable resource of genetic, imaging and cognitive developmental data for the scientific community.

A Case Study of a Force-myography Controlled Bionic Hand Mitigating Limb Position Effect

2017 ◽  
Vol 14 (4) ◽  
pp. 692-705 ◽  
Author(s):  
Diego Ferigo ◽  
Lukas-Karim Merhi ◽  
Brittany Pousett ◽  
Zhen Gang Xiao ◽  
Carlo Menon
Keyword(s):  
Position Effect ◽  
Limb Position ◽  
Force Myography

Robustness of Using Dynamic Motions and Template Matching to the Limb Position Effect in Myoelectric Classification

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Sungtae Shin ◽  
Reza Tafreshi ◽  
Reza Langari
Keyword(s):  
Template Matching ◽  
Human Subjects ◽  
Position Effect ◽  
Correlation Coefficients ◽  
Skin Condition ◽  
Prosthetic Devices ◽  
Linear Discriminant ◽  
Limb Position ◽  
External Conditions ◽  
Dynamic Time

Myoelectric classification has been widely studied for controlling prosthetic devices and human computer interface (HCI). However, it is still not robust due to external conditions: limb position changes, electrode shifts, and skin condition changes. These issues compromise the reliability of pattern recognition techniques in myoelectric systems. In order to increase the reliability in the limb position effect when a limb position is changed from the position in which the system is trained, this paper proposes a myoelectric system using dynamic motions. Dynamic time warping (DTW) technique was used for the alignment of two different time-length motions, and correlation coefficients were then calculated as a similarity metric to classify dynamic motions. On the other hand, Fisher's linear discriminant analysis was applied on static motions for the purpose of dimensionality reduction and Naïve Bayesian classifier for classifying the motions. To estimate the robustness to the limb position effect, static and dynamic motions were collected at four different limb positions from eight human subjects. The statistical analysis, t-test (p < 0.05), showed that, for all subjects, dynamic motions were more robust to the limb position effect than static motions when training and validation sets were extracted from different limb positions with the best classification accuracy of 97.59% and 3.54% standard deviation (SD) for dynamic motions compared with 71.85% with 12.62% SD for static motions.

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