Internet of Things (IoT) and Artificial Intelligence (AI) play a vital role in the upcoming years to improve the assistance systems. The IoT devices utilize several sensor devices that able to collect a large volume of data in different domains which is processed by AI techniques to make the decision about the assistance problems. Among several applications, in this work, IoT with AI is used to examine the healthcare sectors to improve patient assistance and patient care in the future direction. Traditional health care assistance system fails to predict the exact patient health information and needs which reduces the accuracy of patient assistance process. For these issues, an IoT sensor with AI is used to predict the exact patient details such as fitness tracker, medical reports, health activity, body mass, temperature, and other health care information which helps to choose the right assistance process. Healthcare mobile application is used to achieve this goal and collect the patient’s information. This information is shared in the cloud environment, which is accessed and processed by applying the optimized machine learning techniques. The gathered patient details are processed according to the iterative golden section optimized deep belief neural network (IGDBN). The introduced network examines the patient’s details from the previous health information which helps to predict the exact patient health condition in the future direction. The efficiency of IoT sensor with an AI-based health assistance prediction process is developed using MATLAB tool. Excellence is determined in terms of precision (99.87), loss error (0.045), simple matching coefficient (99.71%), Matthews correlation coefficient (99.10%) and accuracy (99.86%).

物联网（IoT）和人工智能（AI）在未来几年中将在改善辅助系统方面发挥至关重要的作用。物联网设备利用多个传感器设备，这些传感器设备能够收集不同域中的大量数据，这些数据由AI技术处理以做出有关辅助问题的决策。在这项工作中，在多个应用程序中，带有AI的IoT用于检查医疗保健领域，以改善未来的患者援助和患者护理。传统的医疗辅助系统无法准确预测患者的健康信息和需求，从而降低了患者辅助过程的准确性。对于这些问题，使用带AI的IoT传感器来预测患者的确切信息，例如健身追踪器，医疗报告，健康活动，体重，温度，以及其他有助于选择正确协助程序的医疗保健信息。医疗保健移动应用程序用于实现此目标并收集患者的信息。此信息在云环境中共享，可通过应用优化的机器学习技术来访问和处理该信息。根据迭代的黄金分割优化的深度信念神经网络（IGDBN）处理收集的患者详细信息。引入的网络从先前的健康信息中检查患者的详细信息，这有助于预测未来方向的确切患者健康状况。使用MATLAB工具开发了具有基于AI的健康辅助预测过程的IoT传感器，以提高效率。优异性取决于精度（99.87），损耗误差（0.045），简单匹配系数（99.71％），