Regulating learning module for patient monitoring interactive event detecting robots

• Published in: Expert systems with applications Vol. 260; p. 125383
• Main Authors: Wu, Fan, Wu, Lin, Liu, Songming, Abbas, Ghulam, Othmen, Salwa, Wang, Jingming
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2025
• Subjects: Neural Network; Patient Monitoring; Robot; Robot Interaction; Sigmoid Function; Sigmoid Function; Neural Network; Robot Interaction; Robot; Patient Monitoring
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125383

Process automation robots in smart healthcare services are reliable in providing interactive patient services. The patient requirements are identified and approached through automated learning and responses. A Regulating Learning Interaction Module (RLIM) using Sigmoid Optimized Neural Networks is designed for robots in such sensitive environments. This learning module trains the robots for various services/ user interactions over patient demands. In particular, the patient interacting features on monitoring and responses/ event triggering are induced through the learning. The miscommunicated interactions are streamlined using sigmoid optimization from the conditional neural layers for assessment. This assessment delivers the possible choices to improve event detection/ response delivery. The neural network regulates the training patterns from the user inputs, histories, and patient diagnosis data for which the events are detected. This event relates the patient with the diagnosis, assistance, and monitoring for better assistance. The performance of this proposed module is validated using interaction detection, response precision, event generation rate, and miscommunication.