Manipal University Jaipur, Jaipur, India.
School of Computer Science, Dr. Vishwanath Karad MIT World peace University, S. No.124, Paud Road, Kothrud, Pune 411038, Maharashtra, India.
J Healthc Eng. 2022 Mar 1;2022:8732213. doi: 10.1155/2022/8732213. eCollection 2022.
Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient's activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient's breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient's respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.
远程医疗和远程患者监测（RPM）自大流行开始以来一直是受到广泛关注并得到应用的重要组成部分。远程医疗和 RPM 可方便地访问患者数据，并有助于以低成本为患者提供高质量的护理。本文提出了一种智能远程患者活动跟踪系统，该系统可以根据附加的传感器在活动期间监测患者的活动和生命体征。使用机器学习模型设计了一种物联网（IoT）启用的健康监测设备，以跟踪患者的活动，例如跑步、睡眠、行走和锻炼，这些活动期间的生命体征，例如体温和心率，以及患者在这些活动期间的呼吸模式。使用机器学习模型来识别患者的不同活动，并分析患者在各种活动中的呼吸健康状况。目前，机器学习模型仅用于检测咳嗽和健康呼吸。还设计了一个 Web 应用程序来跟踪所提出设备上传的数据。
