Unobtrusive Sensor Systems for Intelligent Healthcare: Monitoring Physiological and Behavioral Data

The expected research contribution of this project would be the development of unobtrusive sensor systems to monitor a wide range of physiological and behavioral data of the human body. By incorporating innovative wearable or non-contact sensing techniques with new sensing materials and embedded systems, the candidate will contribute to the advancement of biomedical data acquisition and processing. \r\n\r\nThis research will also contribute to the field by proposing lightweight artificial intelligence (AI) algorithms and software for embedded sensor systems, enabling the development of intelligent healthcare systems. The introduction of these AI algorithms will enhance the efficiency and accuracy of data analysis, enabling real-time monitoring and providing valuable insights into various health conditions.\r\n\r\nThe novelty of this research lies in the integration of multiple sensors to monitor various physiological and behavioral signals simultaneously. The development of unobtrusive sensor systems that can capture EEG, ECG, EOG, EMG, respiration, SpO2, temperature, activities, etc., will be a significant contribution to the discipline. By utilizing new sensing materials and embedded systems, this research aims to improve the comfort and usability of these sensor systems, making them suitable for long-term monitoring and enabling continuous data acquisition.\r\n\r\nThe potential applications of this research are vast and include areas such as sleep monitoring, neonatal monitoring, and smart rehabilitation. Successful delivery of a fully functioning system will benefit not only individuals but also healthcare professionals and researchers. For instance, sleep monitoring systems can provide valuable insights into sleep patterns and disorders, enabling personalized sleep therapy. Neonatal monitoring systems can aid in the early detection of health complications in infants, ensuring timely intervention. Smart rehabilitation systems can assist in monitoring the progress of patients undergoing rehabilitation, optimizing treatment plans, and facilitating their recovery.\r\n\r\nIn summary, the research proposed in this project has the potential to make a significant impact on the discipline by developing unobtrusive sensor systems and lightweight AI algorithms for intelligent healthcare applications. The successful delivery of a fully functioning system will benefit individuals, healthcare professionals, and researchers alike, revolutionizing the way physiological and behavioral data is monitored and analyzed.