A Study of Personal Recognition Method Based on EMG Signal.

Artificial intelligence (AI) has shown potential in analyzing electromyography (EMG) signals, but clinical applicability remains limited by studies based on small, curated datasets, and variable accuracy. This study evaluated AI performance in classifying needle electromyography (EMG) signals as muscle activity versus background/noise/artifact, and then in distinguishing three clinical categories: amyotrophic lateral sclerosis (ALS), myopathy, and non-disease controls. Data from the Cleveland Clinic Foundation EMG Database (CCFDB), a large clinically acquired EMG dataset was utilized for this study. A two-step classification approach was used: a convolutional neural network (CNN) to separate muscle activity from background/noise/artifact, followed by a random forest algorithm and CNNs for clinical category classification. Feature extraction techniques included Short-Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), and Wavelet Packet Decomposition (WPD). EMG data from 608 participants (266 ALS, 89 myopathy, 253 non-disease controls), totaling 11,456 muscle recordings, and 15,613 segments of muscle activity were included. The muscle activity detection model achieved 85.4% accuracy. For clinical category classification, CWT with a two-layer CNN performed best on the CCFDB (62% accuracy). Deeper CNN architectures did not consistently improve performance. On the publicly available curated EMGlab dataset, the best accuracy using the same AI models was higher (91%). AI can assist in EMG analysis, but the performance gap between curated and clinically-acquired datasets underscores the need for robust models capable of handling signal variability and complexity in authentic clinical contexts. Future efforts should focus on clinically-oriented AI development to improve translational applicability.

Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with respect to current clinical approaches based on direct control. However, the choice of features for classification is challenging and impacts long-term performance. Here, we propose the use of EMG raw signals as direct inputs to deep networks with intrinsic feature extraction capabilities recorded over multiple days. Seven able-bodied subjects performed six active motions (plus rest), and EMG signals were recorded for 15 consecutive days with two sessions per day using the MYO armband (MYB, a wearable EMG sensor). The classification was performed by a convolutional neural network (CNN) with raw bipolar EMG samples as the inputs, and the performance was compared with linear discriminant analysis (LDA) and stacked sparse autoencoders with features (SSAE-f) and raw samples (SSAE-r) as inputs. CNN outperformed (lower classification error) both LDA and SSAE-r in the within-session, between sessions on same day, between the pair of days, and leave-out one-day evaluation (p < 0.001) analyses. However, no significant difference was found between CNN and SSAE-f. These results demonstrated that CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features. This data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities alongside normal signals. This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on diverse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting CVD. The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analysis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection.

A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes

Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in supramaximal constant load dynamic exercise (110% VO(2peak)). The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in supramaximal constant load dynamic exercise (P>0.05). However, the results of the variance was significantly lower for at least one of the muscles studied in CWT compared to STFT (P < 0.05) indicating more variability in the EMG signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.

As Computer Science has grown into an encompassed field in various scientific areas, the need for developing a computer aided and artificially intelligent device has become more important especially in the medical field. Artificial Intelligence (AI) plays a vital role not only in accelerating and optimizing common tasks but also in performing tasks that humans are incapable of. This paper presents a Myo Armband Sign-Language Translator (MAST), which is a novel algorithm to translate a hand's gestures into medical sign language using a Myo armband sensor which collects muscles' electromyography signals and then to classify them using an enhanced version of a dynamic random forest. Our experimental results indicate that a systematic fine tuning of MAST parameters leads to an accuracy improvement of 13% over the state-of-the-art scheme such as SCIKIT's random forest. Other comparison results show an improvement of over 20% compared to a popular classification scheme such as Support Vector Machines (SVM) and a deep learning technique such as Convolutional Neural Network (CNN).

Studying muscle fatigue plays an important role in preventing the risks associated with musculoskeletal disorders. The effect of elbow-joint angle on time-frequency parameters during a repetitive motion provides valuable information in finding the most accurate position of the angle causing muscle fatigue. Therefore, the purpose of this study is to analyze the effect of muscle fatigue on the spectral and time-frequency domain parameters derived from electromyography (EMG) signals using the Continuous Wavelet Transform (CWT). Four male participants were recruited to perform a repetitive motion (flexion and extension movements) from a non-fatigue to fatigue condition. EMG signals were recorded from the biceps muscle. The recorded EMG signals were then analyzed offline using the complex Morlet wavelet. The time-frequency domain data were analyzed using the time-averaged wavelet spectrum (TAWS) and the Scale-Average Wavelet Power (SAWP) parameters. The spectral domain data were analyzed using the Instantaneous Mean Frequency (IMNF) and the Instantaneous Mean Power Spectrum (IMNP) parameters. The index of muscle fatigue was observed by calculating the increase of the IMNP and the decrease of the IMNF parameters. After performing a repetitive motion from non-fatigue to fatigue condition, the average of the IMNF value decreased by 15.69% and the average of the IMNP values increased by 84%, respectively. This study suggests that the reliable frequency band to detect muscle fatigue is 31.10-36.19Hz with linear regression parameters of 0.979mV^2Hz^(-1) and 0.0095mV^2Hz^(-1) for R^2 and slope, respectively.

Electromyography (EMG) signals hold critical significance in biomedical research, capturing muscle electrical activity during rest and contraction in the upper limb. Their versatility in applications, particularly in human-assisting robotic tools, drives ongoing exploration and research. This paper presents an original study focusing on leveraging machine learning techniques to classify EMG datasets and efficiently control a robotic arm based on predicted gestures. Data acquisition involves strategically placing an EMG muscle sensor on the forearm to ensure precise measurement of signals associated with hand gestures and movements. Diverse classifiers including random forest, support vector machine (SVM), K-nearest neighbors (KNN), Gaussian naïve Bayes, gated recurrent unit (GRU), long short-term memory (LSTM), artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), and vision transformer (ViT) are employed. Performance results are meticulously analyzed and presented in tabular format, showcasing the ViT classifier as the most successful, achieving an impressive 97.7% accuracy in robotic arm control. Notably, ANN, RNN, and CNN also exhibit high accuracy exceeding 90%. Furthermore, this work is comprehensively compared with existing literature, laying the groundwork for future advancements in human-robot interaction and cutting-edge assistive technologies that markedly enhance the quality of life for individuals with motor impairments or disabilities. The findings carry significant implications for designing and implementing intuitive, responsive robotic systems based on EMG signals.

Deep vein thrombosis (DVT) can lead to life-threatening disorders; however, it can only be recognized after its symptom appear. This study proposed a novel method that can detect the early stage of DVT using electromyography (EMG) signals with vibration stimuli using the convolutional neural networks (CNN) algorithm. The feasibility of the method was tested with eight legs before and after the surgical induction of DVT at nine-time points. Furthermore, perfusion pressure (PP), intracompartmental pressure (IP), and shear elastic modulus (SEM) of the tibialis anterior were also collected. In the proposed method, principal component analysis (PCA) and CNN were used to analyze the EMG data and classify it before and after the DVT stages. The cross-validation was performed in two strategies. One is for each leg and the other is the leave-one-leg-out (LOLO), test without any predicted information, for considering the practical diagnostic tool. The results showed that PCA-CNN can classify before and after DVT stages with an average accuracy of 100% (each leg) and 68.4±20.5% (LOLO). Moreover, all-time points (before induction of DVT and eight-time points after DVT) were classified with an average accuracy of 72.0±11.9% which is substantially higher accuracy than the chance levels (11% for 9-class classification). Based on the experimental results in the pig model, the proposed CNN-based method can classify the before- and after-DVT stages with high accuracy. The experimental results can provide a basis for further developing an early diagnostic tool for DVT using only EMG signals with vibration stimuli.

The subject of the study is the methods of controlling a robotic manipulator based on surface electromyography (EMG) signals using individualized normalization and classification of gestures by a convolutional neural network (CNN). The aim of the work is to create an effective control system that combines pre-normalization of EMG signals and deep learning to increase the accuracy of gesture recognition and stability in real time. For this purpose, the following were used: signal collection using the Myo Armband, pre-processing (min-max and zero-mean), signal conversion into images through a sliding window, CNN training (ResNet, Adam optimization) and comparison with SVM and Random Forest models. The results obtained showed a classification accuracy of 97.27% in the test environment and 91.71% in real time. The CNN model outperformed traditional methods by 18–19%, and zero-mean increased the accuracy by 2.34% compared to min-max normalization. The system remained stable even with variations in the bracelet position due to individual normalization. Conclusions: the proposed system demonstrates high accuracy, reliability and adaptability in real time. The scientific novelty lies in the combination of individualized normalization of EMG signals with ResNet, which provides stability and exceeds the accuracy of traditional algorithms. In the future, it is planned to expand the set of gestures, study more complex conditions and optimize neural networks for embedded systems.

Deep learning assisted continuous wavelet transform-based spectrogram for the detection of chlorophyll content in potato leaves

Frequency domain analyses in electromyographic (EMG) signals are frequently applied to assess muscle fatigue and similar variables. Moreover, Fourier-based approaches are typically used for investigating these procedures. Nonetheless, Fourier analysis assumes the signal as stationary which is unlikely during dynamic contractions. As an alternative method, wavelet-based treatments do not assume this pattern and may be considered as more appropriate for joint time-frequency domain analysis. Based on the previous statements, the purpose of the present study was to compare the application of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) to assess muscle fatigue in dynamic exercise of a 1-km of cycling (time-trial condition). The results of this study indicated that CWT and STFT analyses have provided similar fatigue estimates (slope) (p> 0.05). However, CWT application represents lesser dispersion (pp> 0.05) according to different methods, it is important to note that these responses seem to show greater values for CWT compared to STFT for 2 superficial muscles. Thereby, we are capable of considering CWT as a reliable and useful method to take into consideration when non-stationary or oscillating exercise models are evaluated.

Over the past several years, electromyography (EMG) signals have been used as a natural interface to interact with computers and machines. Recently, deep learning algorithms such as Convolutional Neural Networks (CNNs) have gained interest for decoding the hand movement intention from EMG signals. However, deep networks require a large dataset to train appropriately. Creating such a database for a single subject could be very time-consuming. In this study, we addressed this issue from two perspectives: (i) we proposed a subject-transfer framework to use the knowledge learned from other subjects to compensate for a target subject's limited data; (ii) we proposed a task-transfer framework in which the knowledge learned from a set of basic hand movements is used to classify more complex movements, which include a combination of mentioned basic movements. We introduced two CNN-based architectures for hand movement intention detection and a subject-transfer learning approach. Classifiers are tested on the Nearlab dataset, a sEMG hand/wrist movement dataset including 8 movements and 11 subjects, along with their combination, and on open-source hand sEMG dataset "NinaPro DataBase 2 (DB2)." For the Nearlab database, the subject-transfer learning approach improved the average classification accuracy of the proposed deep classifier from 92.60 to 93.30% when classifier was utilizing 10 other subjects' data via our proposed framework. For Ninapro DB2 exercise B (17 hand movement classes), this improvement was from 81.43 to 82.87%. Moreover, three stages of analysis in task-transfer approach proved that it is possible to classify combination hand movements using the knowledge learned from a set of basic hand movements with zero, few samples and few seconds of data from the target movement classes. First stage takes advantage of shared muscle synergies to classify combined movements, while second and third stages take advantage of novel algorithms using few-shot learning and fine-tuning to use samples from target domain to further train the classifier trained on the source database. The use of information learned from basic hand movements improved classification accuracy of combined hand movements by 10%.

This paper investigates methods for estimating interaction forces during the dynamic process of upper limb elevation. By collecting data from two modalities- electromyographic (EMG) signals of the unilateral forearm and joint angles-along with synchronized interaction force data, a deep learning methodology merging convolutional neural network and long short-term memory network (CNN-LSTM) is adopted to generate a predictive model for characterizing dynamic interactive force, ultimately achieving the task of dynamic force estimation. The comparative analysis of estimation performance using two types of data, EMG signals and EMG-inertial measurement unit (IMU) signals, along with the performance comparison between the CNN-LSTM model and support vector regression (SVR) model for the dynamic force estimation task, demonstrates the advantages of multimodal data and the CNN-LSTM model in facilitating the estimation of dynamic interaction forces in the upper limb.