Machine learning enabled robot-assisted virtual health monitoring system design and development

The robot-based approach aims to address the challenge of providing timely medical assistance to individuals who cannot commit to extended hospitalization through a robot-based approach. The primary objective is to develop cost-effective, low-power integrated circuits with intelligent systems utilizing sensors, wireless communication technologies, and machine learning techniques for comprehensive health monitoring. The research leverages advancements in medical technology to meticulously detect, measure, and analyze essential health parameters, including blood oxygen saturation level (SpO(2)), heart rate, electrocardiogram (ECG), and body temperature. Real-time data processing and communication capabilities via Wi-Fi facilitate seamless data transfer. Integration with robotic platforms enhances capabilities in food and medication delivery, ultraviolet (UV) sanitization, and haptic video communication. Machine learning algorithms such as support vector machines (SVM), min-max optimization, and random forest trees are implemented to achieve an impressive optimization accuracy of approximately 96.2%. In addition, a fuzzy logic model is constructed based on experimental outcomes, resulting in an optimized accuracy rate of 95%. The developed system demonstrates significant advancements in the efficiency and effectiveness of robot-based virtual health monitoring. The integration of machine learning algorithms yields a high optimization accuracy, enhancing the reliability of health parameter measurements and data analysis. This research signifies a substantial stride in the field of robot-based virtual health monitoring systems, offering promising applications in the broader healthcare ecosystem. The study demonstrates the feasibility of providing timely medical assistance outside traditional hospital settings through the integration of intelligent systems with robotic platforms. The high optimization accuracy achieved by machine learning algorithms further underscores the potential of this approach in enhancing information exchange and maintaining accuracy in health monitoring, benefiting both patients and healthcare providers.