A Ahila, Dahan Fadl, Alroobaea Roobaea, Alghamdi Wael Y, Hajjej Fahima, Raahemifar Kaamran
Indian Institute of Technology, Madras, Chennai, India.
Department of Management Information Systems, College of Business Administration-Hawat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Front Physiol. 2023 Jan 30;14:1125952. doi: 10.3389/fphys.2023.1125952. eCollection 2023.
Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Wearable sensor devices and the Internet of Medical Things (IoMT) have changed the health service, resulting in faster monitoring, prediction, diagnosis, and treatment. Nevertheless, there have been difficulties that need to be resolved by the use of AI methods. The primary goal of this study is to introduce an AI-powered, IoMT telemedicine infrastructure for E-healthcare. In this paper, initially the data collection from the patient body is made using the sensed devices and the information are transmitted through the gateway/Wi-Fi and is stored in IoMT cloud repository. The stored information is then acquired, preprocessed to refine the collected data. The features from preprocessed data are extracted by means of high dimensional Linear Discriminant analysis (LDA) and the best optimal features are selected using reconfigured multi-objective cuckoo search algorithm (CSA). The prediction of abnormal/normal data is made by using Hybrid ResNet 18 and GoogleNet classifier (HRGC). The decision is then made whether to send alert to hospitals/healthcare personnel or not. If the expected results are satisfactory, the participant information is saved in the internet for later use. At last, the performance analysis is carried so as to validate the efficiency of proposed mechanism.
一般来说，云计算与无线传感器网络集成以实现监测系统，并提高服务质量。使用生物传感器对感知到的患者数据进行监测，而不考虑患者数据类型，这最大限度地减少了医院和医生的工作量。可穿戴传感器设备和医疗物联网（IoMT）改变了医疗服务，实现了更快的监测、预测、诊断和治疗。然而，使用人工智能方法仍存在一些需要解决的困难。本研究的主要目标是为电子医疗保健引入一种由人工智能驱动的IoMT远程医疗基础设施。在本文中，首先使用感知设备从患者身体收集数据，信息通过网关/无线网络传输并存储在IoMT云存储库中。然后获取存储的信息，进行预处理以细化收集到的数据。通过高维线性判别分析（LDA）从预处理数据中提取特征，并使用重新配置的多目标布谷鸟搜索算法（CSA）选择最佳最优特征。使用混合残差网络18和谷歌网络分类器（HRGC）对异常/正常数据进行预测。然后决定是否向医院/医护人员发送警报。如果预期结果令人满意，则将参与者信息保存在互联网上以供后续使用。最后，进行性能分析以验证所提出机制的效率。
