Machine Learning-Based Gesture Recognition Glove: Design and Implementation.

In the evolving field of human-computer interaction (HCI), gesture recognition has emerged as a critical focus, with smart gloves equipped with sensors playing one of the most important roles. Despite the significance of dynamic gesture recognition, most research on data gloves has concentrated on static gestures, with only a small percentage addressing dynamic gestures or both. This study explores the development of a low-cost smart glove prototype designed to capture and classify dynamic hand gestures for game control and presents a prototype of data gloves equipped with five flex sensors, five force sensors, and one inertial measurement unit (IMU) sensor. To classify dynamic gestures, we developed a neural network-based classifier, utilizing a convolutional neural network (CNN) with three two-dimensional convolutional layers and rectified linear unit (ReLU) activation where its accuracy was 90%. The developed glove effectively captures dynamic gestures for game control, achieving high classification accuracy, precision, and recall, as evidenced by the confusion matrix and training metrics. Despite limitations in the number of gestures and participants, the solution offers a cost-effective and accurate approach to gesture recognition, with potential applications in VR/AR environments.

Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language.

Gesture recognition has become a significant part of human-machine interaction, particularly when verbal interaction is not feasible. The rapid development of biomedical sensing and machine learning algorithms, including electromyography (EMG) and convolutional neural networks (CNNs), has enabled the interpretation of sign languages, including the Polish Sign Language, based on EMG signals. The objective was to classify the game control gestures and Polish Sign Language gestures recorded specifically for this study using two different data acquisition systems: BIOPAC MP36 and MyoWare 2.0. We compared the classification performance of various machine learning algorithms, with a particular emphasis on CNNs on the dataset of EMG signals representing 24 gestures, recorded using both types of EMG sensors. The results (98.324% versus ≤7.8571% and 95.5307% versus ≤10.2697% of accuracy for CNNs and other classifiers in data recorded with BIOPAC MP36 and MyoWare, respectively) indicate that CNNs demonstrate superior accuracy. These results suggest the feasibility of using lower-cost sensors for effective gesture classification and the viability of integrating affordable EMG-based technologies into broader gesture recognition frameworks, providing a cost-effective solution for real-world applications. The dataset created during the study offers a basis for future studies on EMG-based recognition of Polish Sign Language.

Synthesis, Structural Studies and Biological Evaluation of Connections of Thiosemicarbazide, 1,2,4-Triazole and 1,3,4-Thiadiazole with Palmitic Acid.

Thirty new derivatives of palmitic acid were efficiently synthesized. All obtained compounds can be divided into three groups of derivatives: Thiosemicarbazides (compounds 1-10), 1,2,4-triazoles (compounds 1a-10a) and 1,3,4-thiadiazoles (compounds 1b-10b) moieties. ¹H-NMR, 13C-NMR and MS methods were used to confirm the structure of derivatives. All obtained compounds were tested in vitro against a number of microorganisms, including Gram-positive cocci, Gram-negative rods and Candida albicans. Compounds 4, 5, 6, 8 showed significant inhibition against C. albicans. The range of MIC values was 50-1.56 µg/mL. The halogen atom, especially at the 3rd position of the phenyl group was significantly important for antifungal activity. The biological activity against Candida albicans and selected molecular descriptors were used as a basis for QSAR models, that have been determined by means of multiple linear regression. The models have been validated by means of the Leave-One-Out Cross Validation. The obtained QSAR models were characterized by high determination coefficients and good prediction power.

Statistical Analysis of the Impact of Molecular Descriptors on Cytotoxicity of Thiourea Derivatives Incorporating 2-Aminothiazole Scaffold.

Chemical reactivity descriptors and lipophilicyty (log P) were evaluated via semi-empirical method for the quantum calculation of molecular electronic structure (PM3) in order to clarify the structure-cytotoxic activity relationships of disubstutited thioureas. Analysed compounds were obtained by the linkage of 2-aminothiazole ring, thiourea and substituted phenyl ring. The detailed examination was carried out to establish correlation between descriptors and cytotoxic activity against the MT-4 cells for 11 compounds. For the most active compounds (6 compounds) cytotoxic activity against three cancer cell lines (CCRF-CEM, WIL-2NS, CCRF-SB) and normal human cell (HaCaT) was determined. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) reduction and lactate dehydrogenase (LDH) release were assessed. Regression analysis revealed that electrophilicity index and chemical potential significantly contributed to expain the thioureas cytotoxic potential.

Antimicrobial and anti-biofilm activity of thiourea derivatives incorporating a 2-aminothiazole scaffold.

A series of new thiourea derivatives of 1,3-thiazole have been synthesized. All obtained compounds were tested in vitro against a number of microorganisms, including Gram-positive cocci, Gram-negative rods and Candida albicans. Compounds were also tested for their in vitro tuberculostatic activity against the Mycobacterium tuberculosis H37Rv strain, as well as two 'wild' strains isolated from tuberculosis patients. Compounds 3 and 9 showed significant inhibition against Gram-positive cocci (standard strains and hospital strain). The range of MIC values is 2-32 µg/mL. Products 3 and 9 effectively inhibited the biofilm formation of both methicillin-resistant and standard strains of S. epidermidis. The halogen atom, especially at the 3rd position of the phenyl group, is significantly important for this antimicrobial activity. Moreover, all obtained compounds resulted in cytotoxicity and antiviral activity on a large set of DNA and RNA viruses, including Human Immunodeficiency Virus type 1 (HIV-1) and other several important human pathogens. Compound 4 showed activity against HIV-1 and Coxsackievirus type B5. Seven compounds resulted in cytotoxicity against MT-4 cells (CC50<10 µM).