Cost-effective long-range secure speech communication system for internet of things-enabled applications

This study focuses on developing an efficient and cost-effective approach for compressing Phonocardiogram (PCG) signals without compromising their quality. The method utilizes two data compression techniques, capturing heart sounds and transforming them into the frequency domain to extract essential features such as frequency, phase, and amplitude peaks. The compressed signals are subsequently reconstructed to faithfully replicate the original heart sounds. The findings contribute to advancements in biomedical signal processing and compression methodologies, with potential applications in telemedicine and remote sustainable healthcare systems. Compressed PCG signals enable real-time remote consultations and continuous cardiac health monitoring, particularly in underserved regions with limited medical resources. This research holds significant potential for improving access to cardiovascular healthcare and promoting overall health and well-being.

Early diagnosis and effective rehabilitation of cerebral palsy (CP) are essential for improving functional outcomes and reducing long-term complications. CP affects the ability to perform daily physical activities and is often associated with secondary health problems such as obesity and chronic pain. Owing to the complexity of the condition, rehabilitation typically requires continuous supervision by clinical specialists, which can be challenging in environments with limited resources. Recent advances in artificial intelligence (AI) have created new opportunities for enhancing CP healthcare. This review summarises the major applications of AI in diagnosis, clinical decision support, motion classification and rehabilitation systems. AI-based diagnostic tools—including medical-image analysis using MRI and CT—support the early detection of neural abnormalities. Motion-classification systems use physical activity data, functional motor scales and gait analysis features to detect deviations and evaluate treatment progress. In rehabilitation, AI is increasingly integrated into robotic systems, virtual reality environments, video game-based training and metaverse-based therapies, enabling adaptive and engaging therapeutic experiences. This study highlights the shift towards AI-enhanced interventions, discusses the importance of human–computer interfaces for improving interaction between patients and rehabilitation systems and outlines current limitations and challenges. Continued research is required to improve data quality, model generalisability and clinical integration of AI technologies in CP care.

There is a growing interest in the field of brain-computer interface (BCI) that uses electroencephalography (EEG) to connect the brain to external devices. BCI There is a growing interest in the field of brain-computer interface (BCI) that uses electroencephalography (EEG) to connect the brain to external devices. BCI

Further evidence suggests that smart appliances with several types of controllability are necessary, such as smart homes. This practical approach will be greatly appreciated by those who cannot access control of devices that require direct human engagement. This study proposes and constructs a smart home setup based on enhanced electroencephalography (EEG), which could potentially assist individuals with or without movement problems to manage gadgets with greater comfort and ease. In this study, motor/imaginary EEG (MI-EEG) datasets were used. This dataset includes 25 subjects who completed motor and imagery tasks. EEG signals were captured using a 64-channel (BCI2000) instrument. The features are extracted from the CNN's convolutional layers, which are then used to train the classifier. Features are extracted from both the test and training datasets, and the labels are flattened to match the expected input format for the classifier. In addition, the proposed Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Linear Discriminant Analysis (LDA) models can remove the excessive noise and overlaps that lead to misclassification and classify the chosen features into separate classes. Based on the number of classes reached by Arduino UNO, the identified classes are tasked with controlling the smart home. The high overall accuracies using SVM, CNN, and LDA are (99%, 96%, 99%), respectively. According to the current research, the suggested approach for classifying MI-EEG is successful. It may be suitable for BCI applications, allowing for controlling a smart home via brain waves. Results from this study support the idea that the proposed EEG-based smart home could be helpful for older people and others with mobility issues in the future.