Classification Performance and Feature Space Characteristics in Individuals with Upper Limb Loss Using Sonomyography

2021 ◽  
Author(s):  
Susannah Engdahl ◽  
Ananya Dhawan ◽  
Ahmed Bashatah ◽  
Guoqing Diao ◽  
Biswarup Mukherjee ◽  
...  
Keyword(s):  
Upper Limb ◽  
Classification Accuracy ◽  
Feature Space ◽  
Classification Performance ◽  
Limb Loss ◽  
User Performance ◽  
Initial Exposure ◽  
Upper Limb Prosthesis ◽  
Subsequent Exposure ◽  
Training Protocol

Abstract Objective: Sonomyography, or ultrasound-based sensing of muscle deformation, is an emerging modality for upper limb prosthesis control. Although prior studies have shown that individuals with upper limb loss can achieve successful motion classification with sonomyography, it is important to better understand the time-course over which proficiency develops. In this study, we characterized user performance during their initial and subsequent exposures to sonomyography. Method: Ultrasound images corresponding to a series of hand gestures were collected from individuals with transradial limb loss under three scenarios: during their initial exposure to sonomyography (Experiment 1), during a subsequent exposure to sonomyography where they were provided biofeedback as part of a training protocol (Experiment 2), and during testing sessions held on different days (Experiment 3). User performance was characterized by offline classification accuracy, as well as metrics describing the consistency and separability of the sonomyography signal patterns in feature space. Results: Classification accuracy was high during initial exposure to sonomyography (96.2 ± 5.9%) and did not systematically change with the provision of biofeedback or on different days. Despite this stable classification performance, some of the feature space metrics changed. Conclusions: User performance was strong upon their initial exposure to sonomyography and did not improve with subsequent exposure. Clinical Impact: Prosthetists may be able to quickly assess if a patient will be successful with sonomyography without submitting them to an extensive training protocol, leading to earlier socket fabrication and delivery.

scholarly journals Classification Performance and Feature Space Characteristics in Individuals with Upper Limb Loss Using Sonomyography

2022 ◽  
pp. 1-1
Author(s):  
Susannah Engdahl ◽  
Ananya Dhawan ◽  
Ahmed Bashatah ◽  
Guoqing Diao ◽  
Biswarup Mukherjee ◽  
...  
Keyword(s):  
Upper Limb ◽  
Feature Space ◽  
Classification Performance ◽  
Limb Loss

scholarly journals Individuals With Upper Limb Loss Require Minimal Training to Achieve Robust Motion Classification Using Sonomyography

2020 ◽  
Author(s):  
Susannah Engdahl ◽  
Ananya Dhawan ◽  
Ahmed Bashatah ◽  
Guoqing Diao ◽  
Biswarup Mukherjee ◽  
...  
Keyword(s):  
Upper Limb ◽  
Cross Validation ◽  
Positive Association ◽  
Classification Performance ◽  
Limb Loss ◽  
Ultrasound Images ◽  
Motion Classification ◽  
Initial Exposure ◽  
Motion Patterns ◽  
Upper Limb Prostheses

Abstract Background: Although surface electromyography is commonly used as a sensing strategy for upper limb prostheses, it remains difficult to reliably decode the recorded signals for controlling multi-articulated hands. Sonomyography, or ultrasound-based sensing of muscle deformation, overcomes some of these issues and allows individuals with upper limb loss to reliably perform multiple motion patterns. The purposes of this study were to determine 1) the effect of training on classification performance with sonomyographic control and 2) the effect of training on the underlying muscle deformation patterns.Methods: A series of motion pattern datasets were collected from five individuals with transradial limb loss. Each dataset contained five ultrasound images corresponding each of the following five motions: power grasp, wrist pronation, key grasp, tripod, point. Participants initially performed the motions for the datasets without receiving feedback on their performance (baseline phase), then with visual and verbal feedback (feedback phase), and finally again without feedback (retention phase). Cross-validation accuracy and metrics describing the consistency and separability of the muscle deformation patters were computed for each dataset. Changes in classification performance over the course of the study were assessed using linear mixed models. Associations between classification performance and the consistency and separability metrics were evaluated using Pearson correlations.Results: The average cross-validation accuracy for each phase was 92% or greater and there was no significant change in cross-validation accuracy throughout training. Misclassifications of one motion as another did not persist systematically across datasets. Few of the correlations were significant, although many were moderate or greater in strength and showed a positive association between accuracy and improved consistency and separability metrics.Conclusions: Participants were able to achieve high classification rates upon their initial exposure to sonomyography and training did not affect their performance. Thus, motion classification using sonomyography may be highly intuitive and is unlikely to require a structured training protocol to gain proficiency.

scholarly journals Motion prediction using electromyography and sonomyography for an individual with transhumeral limb loss

2020 ◽  
Author(s):  
Susannah Engdahl ◽  
Ananya Dhawan ◽  
György Lévay ◽  
Ahmed Bashatah ◽  
Rahul Kaliki ◽  
...  
Keyword(s):  
Upper Limb ◽  
Cross Validation ◽  
Classification Performance ◽  
Limb Loss ◽  
Single Individual ◽  
Motion Classification ◽  
Motion Patterns ◽  
Prosthetic Hands ◽  
Upper Limb Prosthesis ◽  
Power Grasp

AbstractControlling multi-articulated prosthetic hands with surface electromyography can be challenging for users. Sonomyography, or ultrasound-based sensing of muscle deformation, avoids some of the problems of electromyography and enables classification of multiple motion patterns in individuals with upper limb loss. Because sonomyography has been previously studied only in individuals with transradial limb loss, the purpose of this study was to assess the feasibility of an individual with transhumeral limb loss using this modality for motion classification. A secondary aim was to compare motion classification performance between electromyography and sonomyography. A single individual with transhumeral limb loss created two datasets containing 11 motions each (individual flexion of each finger, thumb abduction, power grasp, key grasp, tripod, point, pinch, wrist pronation). Electromyography or sonomyography signals associated with every motion were acquired and cross-validation accuracy was computed for each dataset. While all motions were usually predicted successfully with both electromyography and sonomyography, the cross-validation accuracies were typically higher for sonomyography. Although this was an exploratory study, the results suggest that controlling an upper limb prosthesis using sonomyography may be feasible for individuals with transhumeral limb loss.

scholarly journals Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Nasir Rashid ◽  
Javaid Iqbal ◽  
Amna Javed ◽  
Mohsin I. Tiwana ◽  
Umar Shahbaz Khan
Keyword(s):  
Logistic Regression ◽  
Embedded System ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Eeg Signals ◽  
Data Set ◽  
Implanted Electrodes ◽  
Movement Data ◽  
Upper Limb Prosthesis ◽  
Limb Prosthesis

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

scholarly journals Laplacian Support Vector Machine for Vibration-Based Robotic Terrain Classification

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 513 ◽  
Author(s):  
Wenlei Shi ◽  
Zerui Li ◽  
Wenjun Lv ◽  
Yuping Wu ◽  
Ji Chang ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Classification Accuracy ◽  
Temporal Correlation ◽  
Feature Space ◽  
Classification Performance ◽  
Support Vector ◽  
Terrain Classification ◽  
Potential Threat ◽  
Temporal Space ◽  
Unlabelled Data

The achievement of robot autonomy has environmental perception as a prerequisite. The hazards rendered from uneven, soft and slippery terrains, which are generally named non-geometric hazards, are another potential threat reducing the traversing efficient, and therefore receiving more and more attention from the robotics community. In the paper, the vibration-based terrain classification (VTC) is investigated by taking a very practical issue, i.e., lack of labels, into consideration. According to the intrinsic temporal correlation existing in the sampled terrain sequence, a modified Laplacian SVM is proposed to utilise the unlabelled data to improve the classification performance. To the best of our knowledge, this is the first paper studying semi-supervised learning problem in robotic terrain classification. The experiment demonstrates that: (1) supervised learning (SVM) achieves a relatively low classification accuracy if given insufficient labels; (2) feature-space homogeneity based semi-supervised learning (traditional Laplacian SVM) cannot improve supervised learning’s accuracy, and even makes it worse; (3) feature- and temporal-space based semi-supervised learning (modified Laplacian SVM), which is proposed in the paper, could increase the classification accuracy very significantly.

scholarly journals Co-Creation Facilitates Translational Research on Upper Limb Prosthetics

Prosthesis ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 110-118
Author(s):  
Hannah Jones ◽  
Sigrid Dupan ◽  
Maxford Coutinho ◽  
Sarah Day ◽  
Deirdre Desmond ◽  
...  
Keyword(s):  
Upper Limb ◽  
Rehabilitation Centre ◽  
Stakeholder Collaboration ◽  
Policy Makers ◽  
Academic Knowledge ◽  
Upper Limb Prosthesis ◽  
The People ◽  
User Data ◽  
Multi Stakeholder ◽  
Upper Limb Prosthetics

People who either use an upper limb prosthesis and/or have used services provided by a prosthetic rehabilitation centre, hereafter called users, are yet to benefit from the fast-paced growth in academic knowledge within the field of upper limb prosthetics. Crucially over the past decade, research has acknowledged the limitations of conducting laboratory-based studies for clinical translation. This has led to an increase, albeit rather small, in trials that gather real-world user data. Multi-stakeholder collaboration is critical within such trials, especially between researchers, users, and clinicians, as well as policy makers, charity representatives, and industry specialists. This paper presents a co-creation model that enables researchers to collaborate with multiple stakeholders, including users, throughout the duration of a study. This approach can lead to a transition in defining the roles of stakeholders, such as users, from participants to co-researchers. This presents a scenario whereby the boundaries between research and participation become blurred and ethical considerations may become complex. However, the time and resources that are required to conduct co-creation within academia can lead to greater impact and benefit the people that the research aims to serve.

scholarly journals Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

2021 ◽  
Vol 13 (4) ◽  
pp. 547
Author(s):  
Wenning Wang ◽  
Xuebin Liu ◽  
Xuanqin Mou
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Classification Problem ◽  
Classification Performance ◽  
Spectral Structure ◽  
Limited Sample ◽  
Sample Classification ◽  
Training Samples ◽  
Traditional Classification ◽  
Hyperspectral Classification

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.

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