IoT-Based Remote Pain Monitoring System: From Device to Cloud Platform

TLDR

Facial expressions are behavioral indicators of pain that can be leveraged to create an automatic pain assessment tool, offering an alternative to self‑report for patients who cannot self‑report, such as ICU patients and minors. This study proposes a scalable IoT system featuring a wearable biosensing facial mask that monitors pain intensity via facial surface electromyogram (sEMG) for real‑time biopotential monitoring and automatic pain assessment. The system comprises a wireless sensor node with up to eight sEMG channels sampled at 1000 Hz, a low‑energy, comfortable design, transmitting data in real time to a cloud platform that bridges the node to a mobile web application for streaming, digital signal processing, interpretation, and visualization.

Abstract

Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the intensive care unit and minors. In this paper, a wearable device with a biosensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things (IoT) system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system acts as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions.