INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

2021 ◽  
pp. 2150043
Author(s):  
SIDHARTH PANCHOLI ◽  
AMIT M. JOSHI
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Time Derivative ◽  
Machine Learning Algorithms ◽  
Prosthetic Control ◽  
Myoelectric Prosthesis ◽  
Feature Extraction Method ◽  
Similar Work ◽  
Emg Signal

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

scholarly journals The Current Status of EMG Signals on Kinematics Needed for Precise Online Myoelectric Control and the Development Direction

2021 ◽  
Vol 271 ◽  
pp. 01029
Author(s):  
Tong Kang
Keyword(s):  
Pattern Recognition ◽  
Degrees Of Freedom ◽  
Quantum Control ◽  
Quantum Information Processing ◽  
Current Status ◽  
Prosthetic Control ◽  
Pattern Recognition Problem ◽  
Emg Signal ◽  
Traditional Pattern ◽  
Electric Signals

Widely accepted idea is the prosthetic control problem could be regarded as the pattern recognition problem. The prosthetic control means there are several differences such as distinguishable electric signals between different activation of muscle. However, this conventional method could not provide proper control of the artificial limbs. Kinematics behavior is continuous and needs the coordination of multiple physiological degrees of freedom (DOF) among various joints. Currently, a huge challenge is achieving precise, coherent and elegant coordination protheses which needs many DOFs to rehabilitation of patients with limb deficiency. This article analyzed the principles of control of bionic limbs from the aspect of EMG and traditional pattern recognition. According to the research results, the following conclusions can be given. Since the quantum amplitudes are complex numbers generally, different parameter should be included and analyzed together during the quantum information processing. Besides, the quantum control scheme could be combined with the classic one. What is more, other sensor modes should be applied for robust control instead of the EMG signal only.

scholarly journals Adaptive Windowing Framework for Surface Electromyogram-Based Pattern Recognition System for Transradial Amputees

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2402 ◽  
Author(s):  
Ali Al-Timemy ◽  
Guido Bugmann ◽  
Javier Escudero
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Time Domain ◽  
Window Size ◽  
Error Rates ◽  
Classification Error ◽  
Linear Discriminant ◽  
Emg Signal ◽  
Upper Limb Prosthesis ◽  
Upper Limb Prostheses

Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signal increases. We demonstrate our framework utilizing EMG datasets collected from nine transradial amputees who performed nine movement classes with Time Domain Power Spectral Descriptors (TD-PSD), Wavelet and Time Domain (TD) feature extraction (FE) methods and a Linear Discriminant Analysis (LDA) classifier. Nonetheless, the concept can be applied to other types of features and classifiers. In addition, the proposed framework is validated with different movement and EMG channel combinations. The results indicate that the proposed framework works well with different FE methods and movement/channel combinations with classification error rates of approximately 13% with TD-PSD FE. Thus, we expect our proposed framework to be a straightforward, yet important, step towards the improvement of the control methods for upper-limb prostheses.

scholarly journals Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4596 ◽  
Author(s):  
Nawadita Parajuli ◽  
Neethu Sreenivasan ◽  
Paolo Bifulco ◽  
Mario Cesarelli ◽  
Sergio Savino ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Upper Limb ◽  
Real World ◽  
Healthcare Sector ◽  
Limb Amputation ◽  
Future Research ◽  
Myoelectric Prosthesis ◽  
Upper Limb Prosthesis ◽  
Maximum Utility

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

scholarly journals Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 680
Author(s):  
Sehyeon Kim ◽  
Dae Youp Shin ◽  
Taekyung Kim ◽  
Sangsook Lee ◽  
Jung Keun Hyun ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Input Image ◽  
Multimodal Fusion ◽  
Image Feature ◽  
Hand Movements ◽  
Electrode Placement ◽  
Control Group ◽  
Motion Classification ◽  
Feature Extraction Method ◽  
Emg Signal

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group.

Bispectrum-based sEMG multi-domain joint feature extraction for upper limb motion classification

2015 ◽  
Vol 230 (2) ◽  
pp. 248-258 ◽  
Author(s):  
Yanzhao Chen ◽  
Yiqi Zhou ◽  
Xiangli Cheng ◽  
Xiaohua Fan ◽  
Yuwei Zhang
Keyword(s):  
Feature Extraction ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Medical Engineering ◽  
Svm Classifier ◽  
Semg Signal ◽  
Feature Extraction Method ◽  
Non Gaussian ◽  
Feature Dimension ◽  
Data Acquisition Program

sEMG based motion pattern recognition is the focus in the rehabilitation medical engineering area. In order to get more information to characterize the sEMG signal of different upper limb motions, the signal data acquisition program is designed and the non-Gaussian characteristic of the sEMG signal is analyzed, the result shows that the sEMG signal collected is non-Gaussian signal. Bispectrum as a third-order statistics contains non-Gaussian information and the integral of bispectrum slice is extracted as feature. After that, the PCA method is adopted to reduce the bispectrum feature dimension. Then, the integral of bispectrum slice after PCA and the integral value of sEMG are combined as a multi-domain joint feature called BisIE. Finally, the experiment is executed to validate the effectiveness of the feature extraction method proposed by the SVM classifier compared with power spectrum-based multi-domain joint feature MMIE. The average classification accuracy of BisIE is about 97% and that of MMIE is about 93%. Besides, for the same subject, the classification accuracy of BisIE is higher than that of MMIE. The result shows that the proposed feature BisIE is effective in promoting sEMG-based upper limb motion recognition accuracy.

Real-time classification of shoulder girdle motions for multifunctional prosthetic hand control: A preliminary study

2019 ◽  
Vol 42 (9) ◽  
pp. 508-515
Author(s):  
Ghaith Kadhim Sharba ◽  
Mousa Kadhim Wali ◽  
Ali Hussein AI-Timemy
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Shoulder Girdle ◽  
Recognition System ◽  
Pattern Recognition System ◽  
High Level ◽  
Real Time Classification

In every country in the world, there are a number of amputees who have been exposed to some accidents that led to the loss of their upper limbs. The aim of this study is to suggest a system for real-time classification of five classes of shoulder girdle motions for high-level upper limb amputees using a pattern recognition system. In the suggested system, the wavelet transform was utilized for feature extraction, and the extreme learning machine was used as a classifier. The system was tested on four intact-limbed subjects and one amputee, with eight channels involving five electromyography channels and three-axis accelerometer sensor. The study shows that the suggested pattern recognition system has the ability to classify the shoulder girdle motions for high-level upper limb motions with 88.4% average classification accuracy for four intact-limbed subjects and 92.8% classification accuracy for one amputee by combining electromyography and accelerometer channels. The outcomes of this study may suggest that the proposed pattern recognition system can help to provide control signals to drive a prosthetic arm for high-level upper limb amputees.

Differences in muscular activation and fatigue for intermittent and constant load

2005 ◽  
Vol 5 (1) ◽  
pp. 43-56
Author(s):  
Danuta Roman-Liu ◽  
Krzysztof Kȩdzior
Keyword(s):  
External Force ◽  
Upper Limb ◽  
Muscle Activation ◽  
Regression Line ◽  
Constant Load ◽  
Median Frequency ◽  
Experimental Conditions ◽  
Emg Signal ◽  
Limb Posture ◽  
Muscular Activation

The aim of this study was to compare the influence of constant or intermittent load on muscle activation and fatigue. The analysis and assessment of muscular activation and fatigue was based on surface EMG measurements from eight muscles (seven muscles of the right upper limb and trapezius muscle). Two EMG signal parameters were analyzed for each of the experimental conditions distinguished by the value of the external force and the character of the load – constant or intermittent. The amplitude related to its maximum (AMP) and the slope of the regression line between time and median frequency (SMF) were the EMG parameters that were analyzed. The results showed that constant load caused higher muscular fatigue than intermittent load despite the lower value of the external force and lower muscle activation. Results suggest that additional external force might influence muscle activation and fatigue more than upper limb posture. The results of the study support the thesis that all biomechanical factors which influence upper limb load and fatigue (upper limb posture, external force and time sequences) should be considered when work stands and work processes are designed. They also indicate that constant load should be especially avoided.

scholarly journals A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chinmay P. Swami ◽  
Nicholas Lenhard ◽  
Jiyeon Kang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Upper Limb ◽  
Daily Living ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Random Forest Regression ◽  
Prosthetic Devices ◽  
Upper Limb Function ◽  
The Neural Network

AbstractProsthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.

scholarly journals Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 642
Author(s):  
Yi-Da Wu ◽  
Ruey-Kai Sheu ◽  
Chih-Wei Chung ◽  
Yen-Ching Wu ◽  
Chiao-Chi Ou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Excellent Agreement ◽  
Antinuclear Antibody ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Observer Agreement ◽  
Supervised Machine Learning ◽  
International Consensus ◽  
Testing Data

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

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