High performance of real-time hand movement classifier based on surface EMG sensor with intelligent system

The study of muscle signal classification has been widely explored for the control of myoelectric prostheses. Traditional approaches rely on manually designed features extracted from time- or frequency-domain representations, which may limit the generalization and adaptability of EMG-based systems. In this work, an autoencoder-based framework is proposed for automatic feature extraction, enabling the learning of compact latent representations directly from raw EMG signals and reducing dependence on handcrafted features. A custom instrumentation system with three surface EMG sensors was developed and placed on selected forearm muscles to acquire signals associated with five hand movements from 20 healthy participants aged 18 to 40 years. The signals were segmented into 200 ms windows with 75% overlap. The proposed method employs a recurrent autoencoder with a symmetric encoder–decoder architecture, trained independently for each sensor to achieve accurate signal reconstruction, with a minimum reconstruction loss of 3.3×10−4V2. The encoder’s latent representations were then used to train a dense neural network for gesture classification. An overall efficiency of 93.84% was achieved, demonstrating that the proposed reconstruction-based approach provides high classification performance and represents a promising solution for future EMG-based assistive and control applications.

Persian sign language recognition using IMU and surface EMG sensors

Objective: The aim of the current study was to investigate the suprahyoid muscle activation during isometric chin-tuck exercises in different body positions. Methods: Thirty-two healthy volunteers were enrolled. Suprahyoid muscle activation was recorded with surface EMG sensors placed under the chin. Isometric Shaker Exercise (SE) and isometric chin-tuck exercises were performed in five different body positions for 3 repetitions. Maximum voluntary isometric contraction (MVC) of the suprahyoid muscles was recorded. The normalization procedure was applied by proportioning the maximum suprahyoid muscle activation recorded during each exercise to the MVC, and recorded as a percentage (MVC%). Results: Significantly higher suprahyoid muscle activation was detected during isometric SE compared to isometric chin-tuck exercises in supine and prone press up on elbows positions (p< .008). Suprahyoid muscle activation was found to be similar during isometric SE and isometric chin-tuck exercises in sitting, quadruped and prone positions (p> .008). Conclusions: In conclusion, similar suprahyoid muscle activation was obtained during isometric chin-tuck exercises in sitting, quadruped and prone positions as during isometric SE. Thus, isometric chin-tuck exercises in these positions could be used as an alternative option to increase suprahyoid muscle activation with the possible potential for less fatigue and better patient compliance during dysphagia management.

The neuromotor effects of transverse friction massage

Swallowing disorders are highly interrelated with increased morbidity and mortality rates; therefore, early detection is crucial. Most screening tools rely on subjective observation, thus making objective assessment tools more vital. Timed Water Swallowing Test (TWST) is a screening tool used in the field providing quantitative data. This study aimed to investigate the swallowing parameters in a wide age range by using TWST and to expand the already existing normative data pool accordingly. It is also aimed to examine the reliability of the TWST and assess its validity in stroke survivors. This study had a cross-sectional design. TWST carried out simultaneously along with surface EMG and laryngeal sensor on 196 healthy subjects aged 10 to 80 for normative data. Also, TWST carried out 30 patients having a history of recent stroke. Test-retest and inter-rater scoring analysis were used for reliability purposes, while Gugging Swallowing Screen (GUSS) test was used for validity purposes. Additionally, the correlations between the participants' TWST scores and GUSS scores were examined using the Spearman correlation coefficient. The normative TWST data of healthy participants are tabulated and presented and their average swallowing capacity was found 13.73ml/s. Furthermore, the mean swallowing capacity of stroke survivors was found 4.61ml/s. As a result of validity analyses, a statistically strong and significant relationship was found between GUSS and TWST parameters (r = 0.775, p < 0.001). Intraclass correlation coefficient (ICC) and correlation values were found between moderate to good agreement between test-retest measurement (ICC = 0.563 to 0.891, p < 0.05). Also, the agreement between the raters was found to be significant (ICC = 0.949 to 0.995, p < 0.05). TWST is a valid and reliable screening tool to evaluate dysphagia on given population. Although the test's performance on healthy individuals is adequate, more research is still needed to confirm that it can be used as a screening tool for stroke.

Conventional EMG sensor designs, typically fabricated using Ag/AgCl materials, often suffer from drawbacks, such as discomfort, limited reusability, and skin irritation. In contrast, textile-based electrodes are viable alternatives, offering enhanced comfort and durability. This paper presents an experimental evaluation and performance analysis of an 8-channels nickel fabric sEMG sensor with three different dimensions. This study aims to assess how different materials besides conventional silver electrodes, such as nickel fabric, and variations in the sensor’s dimensions affect the EMG signal quality. Nickel was chosen because of its flexibility, durability, and superior conductivity. Sensors of varying dimensions (1 cm × 1 cm, 2 cm × 1 cm, and 2 cm × 2 cm) were fabricated and tested on twenty-five participants. The nickel fabric sEMG sensor was used to measure the muscle activity of an arm while performing certain movements. The sensors’ signal amplitude and RMS values, compared to conventional Ag/AgCl electrodes, were the primary metrics used to evaluate their performance. The results demonstrated that the sensor dimensions affected the quality of the recorded EMG signals. All sensor dimensions showed very little variation compared to conventional electrodes, with dimensions of 1 cm × 1 cm was closest to the Ag/AgCl electrode readings with average of 15% difference. Statistical analysis showed 1 cm × 2 cm sensors consistently gives higher peak-to-peak and root mean square values compared to conventional wet electrodes.

Human-machine interfaces (HMIs) are essential tools facilitating effective interactions between users and intelligent systems. However, current wearable HMIs, particularly smart gloves, often face challenges in precisely recognizing subtle finger movements and delivering real-time tactile feedback, limiting their operational effectiveness and user immersion. To overcome these challenges, we propose an intelligent glove system integrating highly stretchable liquid metal-based strain sensors and programmable electromagnetic feedback actuators to realize closed-loop immersive human-machine interaction. The strain sensors achieve superior stretchability (over 200%), linear response, minimal hysteresis, and robust durability, effectively capturing complex hand movements. Gesture recognition, performed by a convolutional neural network, achieves an accuracy of 98% across common gestures, demonstrating exceptional precision and robustness. Furthermore, the glove integrates soft programmable electromagnetic actuators that generate an output force greater than 0.25 N, providing adjustable and precise tactile feedback via pulse-width modulation. This allows users to intuitively perceive tactile sensations in human-machine interaction scenarios, including robotic manipulation and motion guidance tasks. Comprehensive experiments confirm the intelligent glove system’s superior real-time responsiveness and adaptability in dynamic environments. This advanced closed-loop HMI glove significantly enhances operator immersion, operational accuracy, and interaction realism, demonstrating strong potential in robotics, virtual reality, and remote teleoperation applications.

Support vector machines (SVM) have been successfully applied in numerous areas of pattern recognitions, and have demonstrated excellent performance. However, traditional SVM does not make efficient use of both labeled training data and unlabeled testing data. Moreover, one usually encounters high dimensional and nonlinear distributed data in classification problems, especially in financial credit rating assessments. They generally degrade the performance of a classifier due to the curse of dimensionality. This study addresses these problems by proposing a novel intelligent system which integrates a kernel locality preserving projection (KLPP) with a data-dependent manifold-regularized SVM. KLPP is employed to gain a perfect approximation of data manifold and simultaneously preserve local within-class geometric structures according to prior class-label information. Empirical results indicate that, compared with other dimensionality reduction methods and conventional classifiers, the hybrid classifier performs best.

Most of the existing trackers have high performance and good effect, but it is difficult to have fast speed and can not meet the real-time requirements. Therefore, the development of target tracking system with high accuracy and good robustness has ...

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

Depression is an illness that involves the body, mood, and thoughts, and it adversely affects human life. Depression not only lowers the happiness index of individuals but also reduces mindfulness. The increase in the prevalence of clinical depression has been linked to a range of serious outcomes, particularly to an increase in the number of suicide attempts and deaths; making it a public health concern. This underlines the need of an intelligent depression detection system which is able to automatically classify the individual as healthy or depressed. Selection of effective biomarkers plays a vital role in the design of an intelligent depression detection system. For our work, we have used acoustic features extracted from the spontaneous speech samples of the volunteers. By experimenting and evaluating classification results for the dataset of 54 depressed and 75 healthy individuals using different speech features, we found that speech features can be used as a reliable biomarker for depression detection. Speech features like MFCC, pitch, jitter, shimmer and energy have performed better in classifying an individual as a depressed or a healthy one. In the study, the performance of different classifiers like Random Forest, Support Vector Machine (SVM), Gaussian Mixture Model (GMM) and Naive Bayes has been investigated. Among these, hybrid classifier using GMM and SVM has given the best overall classification result.

Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images

The ability to continuously make inferences about another person's latent states from their behavior is integral to how people behave in social situations, yet is lacking from most artificial intelligence (AI) systems. The present study tests the capacity of cognitive models to assess latent motives in real time by evaluating different deep neural networks trained to infer a human player's intent during a continuous control task. These networks were trained by (a) directly using observable information or (b) selecting important features by estimating the parameters of a generative model of movement behavior inspired by approach-avoidance theory. Comparisons of classifier accuracy suggest that latent model parameters predict a participant's intent at a level exceeding human performance. Furthermore, classifier performance was best when model-based inferences were combined with summary statistics about behavior, yielding faster and more stable network training compared to networks that had no manual feature extraction. Equipping AI with cognitive models is a promising avenue for developing explainable, accurate, and trustworthy systems.

Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction

Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset