Research Topic 11: Fatigue Monitoring of Construction Workers Using Advanced Machine Learning

Research Description

Fatigue is a significant issue in the construction industry, leading to reduced productivity, poorer work quality, and an increased risk of accidents. Traditional methods for assessing fatigue, such as surveys and questionnaires, are inconvenient and unsuitable for real-time monitoring on active construction sites. This research project proposes a smart fatigue monitoring system that integrates hardware (Arduino with heart rate, Bluetooth, and vibration sensors) and software (a mobile application) to classify fatigue levels in real-time. The project involves constructing a compact circuit with heart rate, Bluetooth, and vibration sensors. Machine learning models will be trained using previously collected heart rate data (objective) and fatigue ratings (subjective) to classify fatigue levels accurately. These models will be deployed in a mobile application connected to the developed hardware, enabling the app to receive live heart rate data. When the system detects that a worker is fatigued, it will trigger a vibration alert via the hardware to notify the worker, promoting timely interventions. This integrated approach offers a practical and efficient solution for real-time fatigue monitoring, providing a responsive tool to support safety and productivity in construction environments.

REU Research Plan:

REU students will be at the forefront of this innovative project. The research will begin with a thorough literature review on existing fatigue detection methods and their effectiveness in the construction industry. Students will then collaborate with experts in machine learning and sensing technology to design a prototype. They will participate in both the hardware and software development phases to bring the system to completion, followed by experiments to evaluate its effectiveness. The prototype will undergo multiple testing phases, with refinements made based on feedback and results. By the end of the research period, students will have the opportunity to present their findings at conferences and contribute to journal publications.

Keywords: Fatigue prediction, Machine Learning, Sensor Technology, Wearable Sensors, Construction Safety.

Required Skills: Understanding of sensor technologies, Mobile Development, Circuit Development 

Undergraduate Degrees: Computer Science, Electrical Engineering, Computer Engineering

Faculty Advisors: Dr. Chukwuma Nnaji, Dr. Ashrant Aryal