Cyber-physical platform, enabled by artificial intelligence, for Workers' health monitoring via wireless body area networks

• Grant number: 25/12296-9
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: September 01, 2025
• End date: August 31, 2029
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Herman Augusto Lepikson
• Grantee: Luis Henrique da Hora Nascimento
• Host Institution: Escola Politécnica. Universidade Federal da Bahia (UFBA). Salvador , SP, Brazil
• Company: Centro Universitário SENAI CIMATEC. Faculdade de Tecnologia
• Associated research grant: 20/09770-7 - Center of Excellence in Applied Research in Artificial Intelligence for Industry, AP.PCPE

Abstract

Ensuring workers' safety is important not only to prevent accidents but also to ensure productivity. Consequently, monitoring their health conditions is essential to enable actions that promote safety in the workplace, reduce the risk of occupational accidents, and maintain the expected level of productivity.The use of Wireless Body Area Networks (WBAN) can make it possible to monitor workers' health without interfering with their daily activities, regardless of the presence of pre-existing medical conditions. These networks are composed of small electronic devices that can be worn or attached to the body, enabling wireless communication and making it possible to monitor the worker's physical condition as well as environmental conditions in the workplace. Examples include body temperature, blood pressure, heart rate, posture, atmospheric pressure, ambient temperature, among others. The collected data can allow the assessment of hazardous or unhealthy conditions in the workplace, even in the absence of network connectivity. In such cases, the data can be stored locally and synchronized as soon as possible.Despite their potential benefits, the use of WBANs still presents challenges, as these devices must operate under several limitations without compromising functionality, reliability, or stability. Batteries must offer high durability and security; heat and electromagnetic radiation emissions must be minimal; and neither the devices nor the batteries may cause burns, cellular damage, or toxicity. Furthermore, the signals must not interfere with critical systems.All elements must operate in a coordinated manner, ensuring fault tolerance, reliability, stability, and security, requiring a complete ecosystem of hardware and software for signal acquisition, processing, and analysis. Despite this complexity, the collected data must be presented in a user-friendly way to users-whether the monitored workers themselves or the healthcare professionals assisting them.In this context, the present work proposes a hardware and software platform based on Wireless Body Area Networks, integrated with a distributed middleware and enabled by Artificial Intelligence (AI), aimed at monitoring workers' health conditions.This platform will be responsible for properly receiving and processing the signals, enabling the use of various approaches to support the diagnosis of those health conditions. To this end, WBANs must provide the necessary infrastructure for data collection without interfering with the worker's tasks; the middleware must ensure proper integration, enabling data distribution to the various involved agents, as well as data analysis through intelligent and customizable algorithms that can be dynamically attached to the platform.The connection with the physical world-established through Wireless Body Area Networks-and the integration with a distributed monitoring platform and intelligent algorithms, processed in information systems deployed in data centers, possibly in the cloud, allow us to characterize the proposed platform as cyber-physical. (AU)