Parkinson’s disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson’s patients to be $21,482, with an additional $29,695 burden to society. Due to the high stakes and rapidly rising Parkinson’s patients’ count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient’s conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson’s over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson’s progression.

中文翻译：

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用于帕金森病患者室内医疗监测的智能物联网框架

帕金森病与高治疗费用有关，主要归因于住院和频繁护理服务的需要。一项研究显示，帕金森病患者每年的人均医疗保健费用为 21,482 美元，对社会造成额外的 29,695 美元负担。由于高风险和快速增长的帕金森病患者数量，引入智能监测和分析系统势在必行。在本文中，提出了一种基于物联网 (IoT) 的框架，以在典型的室内环境中实现对患者状况的远程监控、管理和分析。提议的基础设施提供静态和动态路由，以及延迟分析和优先启用的通信。该计划还引入了机器学习技术，以使用听觉输入检测帕金森病在六个月内的进展。对提议的物联网基础设施和机器学习算法进行了彻底评估，并进行了详细分析。结果表明，所提出的方案提供了有效的通信调度，以低延迟促进了大量用户。所提出的机器学习方案在准确预测帕金森病进展方面也优于最先进的技术。