Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring

TLDR

Telehealth and remote patient monitoring have become essential during the pandemic, enabling low‑cost, high‑quality care through easy access to patient data, though current machine‑learning models only detect cough and healthy breathing. The study proposes an AIoT‑based Intelligent Remote Patient Activity Tracking System that monitors patient activities and associated vitals using attached sensors. The system comprises an IoT‑enabled health monitoring device that employs machine‑learning models to classify activities and measure vitals such as temperature, heart rate, and breathing patterns, complemented by a web application that aggregates the uploaded data.

Abstract

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.