Integrating Wearable Sensors and Machine Learning for the Detection of Critical Events in Industry Workers

Abstract

The event where an industry worker experiences some sort of critical health problems on site, due to factors not strictly related to the job, poses a serious concern and is an issue of research. These events can be mitigated almost entirely if the workers' health is being monitored in real time by an occupational physician along with an artificial intelligence system that can foresee a health incident and act fast and efficiently. For this reason, we developed a framework of devices, systems, and algorithms which help the industry workers along with the industries to monitor such events and, if possible, minimize them. The aforementioned framework performs seamlessly and autonomously and creates a system where the health of the industry workers is being monitored in real time. In the proposed solution, the worker would wear a wrist sensor in the form of a smartwatch as well as a blood pressure device on the ear. These sensors can communicate directly with a cloud storage system to store sensor data, and then real-time data analysis can be performed. Subsequently, all results can be displayed in an interface operated by an occupational physician, and in case of a health issue event, the doctor and the worker will be notified.

Keywords: Artificial intelligence; Real-time health data monitoring; e-Health.