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Transhumeral movement classification via Electromyographic (EMG) signals is a critical step forward in the quest to develop advanced prosthetic limbs that closely mimic natural arm movements, thereby significantly enhancing the quality of life for amputees. This paper introduces a novel framework that utilizes Deformable Convolutional Neural Networks (DCNN) in conjunction with a magnitude-based Short-Time Fourier Transform (STFT) approach. This method uses three EMG sensors to precisely examine muscle movements in the medial deltoid, biceps, and triceps. It is a notable improvement compared to conventional methods. DCNN offer an unusual ability to handle intricate data patterns, such as shifts, scales, and distortions. This sets them apart from other Convolutional Neural Networks (CNN), particularly in tasks that demand precise feature extraction. In order to enhance the functionality and user experience of prosthetics, we conduct an experimental study involving 20 healthy participants. The study focuses on six fundamental arm movements: flexion, extension, abduction, adduction, pronation, and supination. The outcomes are encouraging, as the DCNN-based classifier had a remarkable accuracy of $82.03\pm 0.44$ % and F1-score $81.82\pm 0.46$ %, overcoming the standard CNN benchmark of $79.47\pm 1.49$ % and F1-score $79.27\pm 1.49$ %. The results emphasize the considerable potential of DCNN in advancing prosthetic limb technology, leading to the development of more intuitive and efficient prosthetic solutions.

Abstract Shouted and normal speech classification plays an important role in many speech-related applications. The existing works are often based on magnitude-based features and ignore phase-based features, which are directly related to magnitude information. In this paper, the importance of phase-based features is explored for the detection of shouted speech. The novel contributions of this work are as follows. (1) Three phase-based features, namely, relative phase (RP), linear prediction analysis estimated speech-based RP (LPAES-RP) and linear prediction residual-based RP (LPR-RP) features, are explored for shouted and normal speech classification. (2) We propose a new RP feature, called the glottal source-based RP (GRP) feature. The main idea of the proposed GRP feature is to exploit the difference between RP and LPAES-RP features to detect shouted speech. (3) A score combination of phase- and magnitude-based features is also employed to further improve the classification performance. The proposed feature and combination are evaluated using the shouted normal electroglottograph speech (SNE-Speech) corpus. The experimental findings show that the RP, LPAES-RP, and LPR-RP features provide promising results for the detection of shouted speech. We also find that the proposed GRP feature can provide better results than those of the standard mel-frequency cepstral coefficient (MFCC) feature. Moreover, compared to using individual features, the score combination of the MFCC and RP/LPAES-RP/LPR-RP/GRP features yields an improved detection performance. Performance analysis under noisy environments shows that the score combination of the MFCC and the RP/LPAES-RP/LPR-RP features gives more robust classification. These outcomes show the importance of RP features in distinguishing shouted speech from normal speech.

Recent studies have shown the potential of the Data-Efficient Image Transformer (DeiT)-based transfer learning method in speech/image recognition and classification utilizing models pre-trained on image datasets. However, the use of DeiT models, especially those pre-trained on image datasets, has not yet been explored for Valvular Heart Disease (VHD) detection. This paper proposes a transfer learning methodology using the DeiT model pre-trained on image datasets for VHD classification. Additionally, we introduce a hybrid Convolution-DeiT (Conv-DeiT) architecture to further improve classification performance. The Conv-DeiT framework integrates a convolutional block with a Squeeze-and-Excitation (SE) attention mechanism to enhance the channel and spatial information within the input features before processing by the DeiT model. The proposed models were assessed using the Heart Sound Murmur (HSM) database, accessible on GitHub. Experimental results show that the DeiT-based transfer learning approach achieved an overall accuracy of 97.44%. Moreover, our Conv-DeiT method outperformed the DeiT-based transfer learning with an impressive overall accuracy of 99.44%. This study indicates the effectiveness of transfer learning using DeiT models pre-trained on image datasets for heart sound classification. Specifically, our hybrid Conv-DeiT method, which combines the convolutional block and the SE-attention mechanism, demonstrates significant advantages in this context.

Research focuses on the efficacy of Multi-Task Autoencoder (MTAE) models in signal classification due to their ability to handle many tasks while improving feature extraction. However, researchers have not thoroughly investigated the study of lung sounds (LSs) for pulmonary disease detection. This paper introduces a new framework that utilizes an MTAE model to detect lung diseases based on LS signals. The model integrates an autoencoder and a supervised classifier, simultaneously optimizing both classification accuracy and signal reconstruction. Furthermore, we propose a hybrid approach that combines an MTAE and a Support Vector Machine (MTAE-SVM) to enhance performance. We evaluated our model using LS signals from a publicly available database from King Abdullah University Hospital. The model attained an accuracy of 89.47% for four classes (normal, pneumonia, asthma, and chronic obstructive pulmonary disease) and 90.22% for three classes (normal, pneumonia, and asthma cases). Using the MTAE-SVM, the accuracy was further improved to 91.49% for four classes and 93.08% for three classes, respectively. The results indicate that the MTAE and MTAE-SVM have a considerable potential for detecting pulmonary diseases from lung sound signals. This could aid in the creation of more user-friendly and effective diagnostic tools.

Recent studies have reported the success of multi-scale convolution neural network (MSConvNet) model for many classification applications due to its powerful ability of exploring multi-scale convolution block to extract multi-scale representations to make a detection. However, a new design based on MSConvNet for pathological voice detection has not been explored. In this paper, we propose RS-MSConvNet, a novel end-to-end MSConvNet model using raw speech for pathological voice detection. The main contribution of the proposed RS-MSConvNet method is to exploit the multi-scale convolution block, followed by spatial-temporal feature block, and fully connected layer as classification. In addition, to further improve accuracy performance, we propose a novel hybrid detection model by integrating the feature extraction ability of the RS-MSConvNet model and the classifier of support vector machine (SVM) method, called RS-MSConvNet-SVM model. The effectiveness of our proposed models is investigated using the TORGO database. The experimental results reveal that the RS-MSConvNet model outperforms other baseline methods in the speaker-independent task. Moreover and as compared to the RS-MSConvNet-SVM model, a further improved accuracy is obtained using the RS-MSConvNet-SVM model. These outcomes exhibit that our proposed models are useful for pathological voice detection.

# Purpose A high level of stress among medical students is perceived as stress caused by strenuous medical programs and medical school is an extremely stressful environment, to begin with. For this reason, identifying stressors facing medical students is expected to enhance medical school lecturers' understanding, leading to a provision of assistance for adequate supervision. The purposes of this study were to investigate stress levels in daily life and the electroencephalogram (EEG) characteristics during daily life and pre-examination period of 2^nd^-year medical students at Suranaree University of Technology (SUT), Thailand, and to compare the EEG characteristics between these two periods. # Methods Medical Student Stressor Questionnaire (MSSQ) was used as a research instrument to collect data from sixty medical students. After that, EEG was administered in two periods for these studies (in daily life (baseline) and pre-examination 1 week). Paired t-test was used for analyzing the difference in the EEG characteristics in the 2 periods. # Results The results indicated that the stress levels among medical students were mild (3.33%), moderate (53.33%), and high (43.33%). Academic Related Stressor (ARS) was found to be the main cause of stress among the subjects. All had a beta wave in 2 periods. # Conclusion In conclusion, stress among medical students can alter brain function as measured by EEG. The findings could assist medical schools in better understanding medical students' stress levels and planning how to teach in order to improve student achievement.

Those with disabilities who have lost their legs must use a prosthesis to walk. However, traditional prostheses have the disadvantage of being unable to move and support the human gait because there are no mechanisms or algorithms to control them. This makes it difficult for the wearer to walk. To overcome this problem, we developed an insole device with a wearable sensor for real-time gait phase detection based on the kNN (k-nearest neighbor) algorithm for prosthetic control. The kNN algorithm is used with the raw data obtained from the pressure sensors in the insole to predict seven walking phases, i.e., stand, heel strike, foot flat, midstance, heel off, toe-off, and swing. As a result, the predictive decision in each gait cycle to control the ankle movement of the transtibial prosthesis improves with each walk. The results in this study can provide 81.43% accuracy for gait phase detection, and can control the transtibial prosthetic effectively at the maximum walking speed of 6 km/h. Moreover, this insole device is small, lightweight and unaffected by the physical factors of the wearer.

Recent studies have reported that the performance of Automatic Speech Recognition (ASR) technologies designed for normal speech notably deteriorates when it is evaluated by whispered speech. Therefore, the detection of whispered speech is useful in order to attenuate the mismatch between training and testing situations. This paper proposes two new Glottal Flow (GF)-based features, namely, GF-based Mel-Frequency Cepstral Coefficient (GF-MFCC) as a magnitude-based feature and GF-based relative phase (GF-RP) as a phase-based feature for whispered speech detection. The main contribution of the proposed features is to extract magnitude and phase information obtained by the GF signal. In the GF-MFCC, Mel-frequency cepstral coefficient (MFCC) feature extraction is modified using the estimated GF signal derived from the iterative adaptive inverse filtering as the input to replace the raw speech signal. In a similar way, the GF-RP feature is the modification of the relative phase (RP) feature extraction by using the GF signal instead of the raw speech signal. The whispered speech production provides lower amplitude from the glottal source than normal speech production, thus, the whispered speech via Discrete Fourier Transformation (DFT) provides the lower magnitude and phase information, which make it different from a normal speech. Therefore, it is hypothesized that two types of our proposed features are useful for whispered speech detection. In addition, using the individual GF-MFCC/GF-RP feature, the feature-level and score-level combination are also proposed to further improve the detection performance. The performance of the proposed features and combinations in this study is investigated using the CHAIN corpus. The proposed GF-MFCC outperforms MFCC, while GF-RP has a higher performance than the RP. Further improved results are obtained via the feature-level combination of MFCC and GF-MFCC (MFCC&GF-MFCC)/RP and GF-RP(RP&GF-RP) compared with using either one alone. In addition, the combined score of MFCC&GF-MFCC and RP&GF-RP gives the best frame-level accuracy of 95.01% and the utterance-level accuracy of 100%.

A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with theGaussianMixtureModel (GMM) andKernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification, in terms of character applications, because of the wavelet-like decomposition without any down sampling process. Secondly, by getting motivated with shallow learning classifiers, we can provide promising performance for the classification of binary classes, GMM and KELM, which are applied for the learning of features along with the brainwave classification. Lastly, we propose a new method by combining GMMand KELM to fuse the merits of different classifiers. Moreover, the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement. The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform (DWT) feature. Moreover, we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40% and 80.10%, respectively, which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%. Furthermore, we obtained the improved performance by combining GMM and KELM, i.e., average accuracy of 80.60%. These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwave classification in terms of the Character-Writing application, which do not require any limb movement and stimulus. [ABSTRACT FROM AUTHOR]

The patients with brain diseases (e.g., Stroke and Amyotrophic Lateral Sclerosis (ALS)) are often affected by the injury of motor cortex, which causes a muscular weakness. For this reason, they require rehabilitation with continuous physiotherapy as these diseases can be eased within the initial stages of the symptoms. So far, the popular control system for robot-assisted rehabilitation devices is only of two types which consist of passive and active devices. However, if there is a control system that can directly detect the motor functions, it will induce neuroplasticity to facilitate early motor recovery. In this paper, the control system, which is a motor recovery system with the intent of rehabilitation, focuses on the hand organs and utilizes a brain-computer interface (BCI) technology. The final results depict that the brainwave detection for controlling pneumatic glove in real-time has an accuracy up to 82%. Moreover, the motor recovery system enables the feasibility of brainwave classification from the motor cortex with Artificial Neural Networks (ANN). The overall model performance reveals an accuracy up to 96.56% with sensitivity of 94.22% and specificity of 98.8%. Therefore, the proposed system increases the efficiency of the traditional device control system and tends to provide a better rehabilitation than the traditional physiotherapy alone. [ABSTRACT FROM AUTHOR]