A Multimodal AI-Enabled Human-Robot Collaboration Framework for Intelligent Patient Monitoring, Assistance, and Decision Support

Abstract

Robotics is one of the human alternative execution systems. It covers almost all the application based fields due to its versatility. The major attraction of doing such research is due to its multidisciplinary nature and involvement of diverse technologies. It is a reprogrammable and multifunctional device which follows the concepts of science, engineering and technology. In this paper a complete study has been done about robotics, design and work execution. The cause of doing so is to make use of robots for the purpose of patient monitoring, assistant as well as decision support system. There are different open source tools as well as virtual labs have been used for the purpose of design, simulation and execution of diverse robots. The major studies have been done about the design and configurations for healthcare based applications. Actually, to create a complete picture of the patient's condition, the framework combines physiological sensors, wearable technology, cameras, microphones, environmental sensors, and patient interaction data. Heterogeneous data is preprocessed, aligned, and fused using multimodal AI algorithms for anomaly detection and patient-state estimation. A decision-support module next evaluates the identified conditions to produce risk assessments and suggestions for medical practitioners. The robot may carry out suitable assistance duties while crucial decisions are still overseen by healthcare professionals thanks to a human-in-the-loop human–robot collaboration system. Explainable AI, safety monitoring, feedback, and ongoing learning are all included in the suggested structure. Accuracy, precision, recall, F1-score, response time, false-alarm rate, task success rate, and human-robot interaction metrics can all be used to assess performance. The framework seeks to offer a patient-centered, scalable method for intelligent. For intelligent healthcare environments, the framework seeks to offer a scalable and patient-centered method.