Internet of Things (IoT) concepts constitute a predominant area of research in e-healthcare applications, owing to the plethora of opportunities in medical diagnosis. In this work, a ubiquitous computing and communication architecture is proposed through the amalgamation of Internet of Healthcare and Internet of Drone things by leveraging a 5G/6G communication framework. Gait information is aggregated via a smart shoe and the processing is carried out on a set of edge-enabled Unmanned Aerial Vehicles (UAVs). To transfer the data within the edge and cloud layers, a Software Defined Network (SDN) is modeled. Further, a classifier is designed to analyze the records and make predictions on possible neurological disorders at the edge level. Experimental results suggest a 98% classification accuracy for abnormal gait diagnosis at 20% CPU utilization. The findings further reveal a latency of 335 ms. at QoS 2, and 50 msg/s bandwidth utilization with a Connected Client Ratio and SDN Coverage Ratio of 0.99 and 0.95, respectively.

由于医疗诊断中的大量机会，物联网 (IoT) 概念构成了电子医疗保健应用的主要研究领域。在这项工作中，通过利用 5G/6G 通信框架，通过医疗互联网和无人机物联网的融合，提出了一种泛在计算和通信架构。步态信息通过智能鞋进行汇总，并在一组支持边缘的无人机 (UAV) 上进行处理。为了在边缘层和云层内传输数据，对软件定义网络 (SDN) 进行了建模。此外，分类器旨在分析记录并在边缘级别对可能的神经系统疾病进行预测。实验结果表明，在 20% 的 CPU 利用率下，异常步态诊断的分类准确率为 98%。研究结果进一步揭示了 335 毫秒的延迟。在 QoS 2 和 50 msg/s 带宽利用率下，连接客户端比率和 SDN 覆盖率分别为 0.99 和 0.95。