Improving the myoelectric motion classification performance by feature filtering strategy

2017 ◽  
Author(s):  
Xiangxin Li ◽  
Yue Zheng ◽  
Zeyang Xia ◽  
Guanglin Li ◽  
Peng Fang
Keyword(s):  
Classification Performance ◽  
Motion Classification ◽  
Feature Filtering

scholarly journals Linear Discriminant Analysis-Based Motion Classification Using Distributed Micro-Doppler Radars with Limited Backhaul

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2924
Author(s):  
Yonggi Hong ◽  
Yunji Yang ◽  
Jaehyun Park
Keyword(s):  
Discriminant Analysis ◽  
Data Fusion ◽  
Linear Discriminant Analysis ◽  
Classification Performance ◽  
Fusion Center ◽  
Simulation Data ◽  
Motion Classification ◽  
Linear Discriminant ◽  
Aspect Angle ◽  
Synthetic Simulation

In this paper, we propose a cooperative linear discriminant analysis (LDA)-based motion classification algorithm for distributed micro-Doppler (MD) radars which are connected to a data fusion center through the limited backhaul. Due to the limited backhaul, each radar cannot report the high-dimensional data of a multi-aspect angle MD signature to the fusion center. Instead, at each radar, the dimensionality of the MD signature is reduced by using the LDA algorithm and the dimensionally-reduced MD signature can be collected at the data fusion center. To further reduce the burden of backhaul, we also propose the softmax processing method in which the distances of the sensed MD signatures from the centers of clusters for all motion candidates are computed at each radar. The output of the softmax process at each radar is quantized through the pyramid vector quantization with a finite number of bits and is reported to the data fusion center. To improve the classification performance at the fusion center, the channel resources of the backhaul are adaptively allocated based on the classification separability at each radar. The proposed classification performance was assessed with synthetic simulation data as well as experimental data measured through the USRP-based MD radar.

scholarly journals Predicting survival in advanced cancers by applying machine learning to clinical narratives.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 32-32
Author(s):  
Frank Po-Yen Lin ◽  
Michael B. Jameson ◽  
Richard J. Epstein
Keyword(s):  
Machine Learning ◽  
Eastern Cooperative Oncology Group ◽  
Feature Learning ◽  
Stage Iv ◽  
Classification Performance ◽  
Survival Prediction ◽  
Feature Filtering ◽  
Text Features ◽  
Task Feature ◽  
Patient Prognosis

32 Background: We previously showed that a text mining approach can identify clinical prognostic factors from electronic medical records (EMR) in patients with advanced cancers (1). Here we further examine whether clinical narratives can be exploited to build prognostic tools by applying a machine-learning (ML) approach. Methods: A retrospective study of all patients with stage IV tumors was conducted at a single tertiary cancer centre. The text corpus was formed by extracting narratives from initial consultation letters authored by oncologists, and a feature learning pipeline (2) was then used to extract text features correlating to survival. Five classes of ML algorithms was then applied for survival prediction. Classification performance was assessed by stratified cross-validation and compared with Eastern Cooperative Oncology Group (ECOG) performance scores. Results: EMR were available for analysis in 4791 of 7043 patients from 2001-2017, and in 2211 of these cases ECOG performance scores were available. By applying ML on features extracted from EMR text, survival of patients at 2, 6, 12, 26, 52, and 80 weeks was predicted, with areas under the receiver operating characteristic (ROC) curve of 0.82, 0.80, 0.77, 0.72, 0.72, and 0.76 respectively. ML outperformed ECOG score in predicting patient prognosis between 12-16 weeks ( p < 0.05) and after 52 weeks ( p < 0.05), and was non-inferior at all other time points. Random forest was the best algorithm for the prognostic classification task. Feature filtering threshold was important to classification accuracy ( p < 0.001). Conclusions: In patients with advanced cancers, ML analysis of clinical narratives can be used to automate prognostication with greater accuracy than is currently obtainable from ECOG status. References: 1. Int Med J 2018; 48: S5: 8, 2. Sci Rep 2017; 7: 6918.

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.

The Sound of Enemies and Friends in the Neighborhood

2011 ◽  
Vol 58 (6) ◽  
pp. 454-463 ◽  
Author(s):  
Diane Pecher ◽  
Inge Boot ◽  
Saskia van Dantzig ◽  
Carol J. Madden ◽  
David E. Huber ◽  
...  
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Neighborhood Effects ◽  
Classification Performance ◽  
Visual Word ◽  
Orthographic Neighborhood ◽  
Semantic Classification ◽  
Semantic Class ◽  
Orthographic Neighbors ◽  
Target Words

Previous studies (e.g., Pecher, Zeelenberg, & Wagenmakers, 2005) found that semantic classification performance is better for target words with orthographic neighbors that are mostly from the same semantic class (e.g., living) compared to target words with orthographic neighbors that are mostly from the opposite semantic class (e.g., nonliving). In the present study we investigated the contribution of phonology to orthographic neighborhood effects by comparing effects of phonologically congruent orthographic neighbors (book-hook) to phonologically incongruent orthographic neighbors (sand-wand). The prior presentation of a semantically congruent word produced larger effects on subsequent animacy decisions when the previously presented word was a phonologically congruent neighbor than when it was a phonologically incongruent neighbor. In a second experiment, performance differences between target words with versus without semantically congruent orthographic neighbors were larger if the orthographic neighbors were also phonologically congruent. These results support models of visual word recognition that assume an important role for phonology in cascaded access to meaning.

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

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