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 ◽  
...  

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.

Author(s):  
SIDHARTH PANCHOLI ◽  
AMIT M. JOSHI

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.


2004 ◽  
Vol 34 (8) ◽  
pp. 1561-1569 ◽  
Author(s):  
GÜNTHER KNOBLICH ◽  
FRANK STOTTMEISTER ◽  
TILO KIRCHER

Background. The present study investigated whether a failure of self-monitoring contributes to core syndromes of schizophrenia.Method. Three groups of patients with a DSM-IV diagnosis of schizophrenia (n=27), with either prominent paranoid hallucinatory or disorganization syndrome, or without these symptoms, and a matched healthy control group (n=23) drew circles on a writing pad connected to a PC monitor. Subjects were instructed to continuously monitor the relationship between their hand movements and their visual consequences. They were asked to detect gain changes in the mapping. Self-monitoring ability and the ability to automatically correct movements were assessed.Results. Patients with either paranoid-hallucinatory syndrome or formal thought disorder were selectively impaired in their ability to detect a mismatch between a self-generated movement and its consequences, but not impaired in their ability to automatically compensate for the gain change.Conclusions. These results support the claim that a failure of self-monitoring may underlie the core symptoms of schizophrenia.


Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rabeb Faleh ◽  
Sami Gomri ◽  
Khalifa Aguir ◽  
Abdennaceur Kachouri

Purpose The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors. Design/methodology/approach To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array. Findings The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF. Originality/value Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.


Author(s):  
M. Chandraleka ◽  
D. Anitha

In mobile, many applications provide services to the users based on the photos provided by the user.Certain applications, client users take a photo of a certain spot and send it to a server, the server identifies the spot with an image recognizer and returns its related information to the users.It can cause a privacy issue because image recognition results are sometimes privacy sensitive.To overcome the problems of existing approaches, proposed an Encryption-Free framework for Privacy preserving Image Recognition, called Enfpire.InEnfPire, the server cannot identify the client users current location, its candidates can only be presented. In proposed thefeature extraction with CNN algorithm help to collect the unique and accurate features from input image and also used Duplicate Detection process to detect images with same features present within same index.In proposed approach user transform the extracted image feature x into y on the user server and sends it to the public server.With the transformation , the effectiveness of the original feature x is degraded so that the public server cannot uniquely recognize the spot-ID of user from y.It only retrives the relevant spot ID’s.The unique spot ID will identify and information regarding the spot and relevant images will be given to the user.


Author(s):  
Fadhlan Hafizhelmi Kamaru Zaman

Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed Convolutional Neural Network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-of-the-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively.


2013 ◽  
Vol 475-476 ◽  
pp. 374-378
Author(s):  
Xue Ming Zhai ◽  
Dong Ya Zhang ◽  
Yu Jia Zhai ◽  
Ruo Chen Li ◽  
De Wen Wang

Image feature extraction and classification is increasingly important in all sectors of the images system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates, so can reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of images, and the result shows that using this algorithm to extract invariant moments and classifying can achieve better classification accuracy.


2019 ◽  
Vol 92 (1101) ◽  
pp. 20181020 ◽  
Author(s):  
Daniel Fakhry-Darian ◽  
Neva Hiten Patel ◽  
Sairah Khan ◽  
Tara Barwick ◽  
William Svensson ◽  
...  

Objectives:This study investigates the usefulness of quantitative SUVR thresholds on sub types of typical (type A) and atypical (non-type A) positive (Aβ+) and negative (Aβ-)18F-florbetapir scans and aims to optimise the thresholds.Methods:Clinical18F-florbetapir scans (n = 100) were categorised by sub type and visual reads were performed independently by three trained readers. Inter-reader agreement and reader-to-reference agreement were measured. Optimal SUVR thresholds were derived by ROC analysis and were compared with thresholds derived from a healthy control group and values from published literature.Results:Sub type division of18F-florbetapir PET scans improves accuracy and agreement of visual reads for type A: accuracy 90%, 96% and 70% and agreement κ > 0.7, κ ≥ 0.85 and −0.1 < κ < 0.9 for all data, type A and non-type A respectively. Sub type division also improves quantitative classification accuracy of type A: optimum mcSUVR thresholds were found to be 1.32, 1.18 and 1.48 with accuracy 86%, 92% and 76% for all data, type A and non-type A respectively.Conclusions:Aβ+/Aβ- mcSUVR threshold of 1.18 is suitable for classification of type A studies (sensitivity = 97%, specificity = 88%). Region-wise SUVR thresholds may improve classification accuracy in non-type A studies. Amyloid PET scans should be divided by sub type before quantification.Advances in knowledge:We have derived and validated mcSUVR thresholds for Aβ+/Aβ-18F-florbetapir studies. This work demonstrates that division into sub types improves reader accuracy and agreement and quantification accuracy in scans with typical presentation and highlights the atypical presentations not suited to global SUVR quantification.


Sensors ◽  
2011 ◽  
Vol 11 (2) ◽  
pp. 1721-1743 ◽  
Author(s):  
Birsel Ayrulu-Erdem ◽  
Billur Barshan

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction.


Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3303
Author(s):  
Farah Masood ◽  
Hussein A. Abdullah ◽  
Nitin Seth ◽  
Heather Simmons ◽  
Kevin Brunner ◽  
...  

This study aims to characterize traumatic spinal cord injury (TSCI) neurophysiologically using an intramuscular fine-wire electromyography (EMG) electrode pair. EMG data were collected from an agonist-antagonist pair of tail muscles of Macaca fasicularis, pre- and post-lesion, and for a treatment and control group. The EMG signals were decomposed into multi-resolution subsets using wavelet transforms (WT), then the relative power (RP) was calculated for each individual reconstructed EMG sub-band. Linear mixed models were developed to test three hypotheses: (i) asymmetrical volitional activity of left and right side tail muscles (ii) the effect of the experimental TSCI on the frequency content of the EMG signal, (iii) and the effect of an experimental treatment. The results from the electrode pair data suggested that there is asymmetry in the EMG response of the left and right side muscles (p-value < 0.001). This is consistent with the construct of limb dominance. The results also suggest that the lesion resulted in clear changes in the EMG frequency distribution in the post-lesion period with a significant increment in the low-frequency sub-bands (D4, D6, and A6) of the left and right side, also a significant reduction in the high-frequency sub-bands (D1 and D2) of the right side (p-value < 0.001). The preliminary results suggest that using the RP of the EMG data, the fine-wire intramuscular EMG electrode pair are a suitable method of monitoring and measuring treatment effects of experimental treatments for spinal cord injury (SCI).


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