Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide; however, due to a lack of public awareness, as well as the expensive testing involved in the identification of PCOS, 70% of cases go undiagnosed. Theref...
Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), ...
The patch clamp technique has become the gold standard for neuron electrophysiology research in brain science. Brain slices have been widely utilized as the targets of the patch clamp technique due to their higher optical transparency compared to a l...
Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focu...
Lameness detection in horses is a critical challenge in equine veterinary practice, particularly when symptoms are mild. This study aimed to develop a predictive system using a support vector machine (SVM) to identify the affected limb in horses trot...
WiFi channel state information (CSI) has emerged as a promising modality for human action recognition due to its non-invasive nature and robustness in diverse environments. However, most existing methods process CSI channels collectively, potentially...
sEMG is a non-invasive biomedical engineering technique that can detect and record electrical signals generated by muscles, reflecting both motor intentions and the degree of muscle contraction. This study aims to classify and recognize nine types of...
Traditional approaches for human monitoring and motion recognition often rely on wearable sensors, which, while effective, are obtrusive and cause significant discomfort to workers. More recent approaches have employed unobtrusive, real-time sensing ...
Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, ...
This project aims to develop an interactive control system for an autonomous service robot using an ROS (robot operating system). The system integrates an intuitive web interface and an interactive chatbot supported by Google Gemini to enhance the co...