Research Topic 12: Advanced sensing technologies for preventing construction workers’ work-related musculoskeletal disorders (WMSDs)

Research Description

Construction tasks frequently necessitate prolonged overexertion without sufficient rest, leading to physical fatigue. This fatigue can contribute to work-related musculoskeletal disorders (WMSDs). Construction workers face a significantly higher risk of WMSDs than those in other industries, with approximately 20% affected by these conditions. Therefore, it is essential to monitor and manage construction tasks to prevent WMSDs.

The advent of wearable sensing technologies—including motion sensors (e.g., inertial measurement units) and physiological sensors (e.g., heart-rate sensors, electrodermal activity sensors, skin-temperature sensors, eye trackers, and brainwave monitors)—has created unprecedented opportunities for near real-time collection and analysis of workers’ safety and health data. This project explores methodsdetecting activities that expose workers to the risk of developing WMSDs by assessing hand movements and gait patterns.

REU research plan:

REU students will be involved in identifying construction tasks that expose workers to the risk of developing WMSDs and conducting experiments using inertial measurement units (IMUs). The students will first perform a job hazard analysis and literature review to understand which construction tasks can be monitored and measured using IMUs. The advisor and graduate student mentor will work with the REU students to design experiments and collect data. The students will receive guidance on coding, data preprocessing, anomaly detection, and classification using machine learning techniques. They will also have the opportunity to contribute to a conference or journal paper with the research team members. 

Keywords: Work-related Musculoskeletal Disorders (WMSDs), Wearable Sensing, Construction Safety

Required/Preferred Skills: Basic knowledge of statistics and coding (e.g., MATLAB, Python)

Undergraduate Degrees: Computer Science, Civil Engineering, Building Construction, Construction Science/Management

Faculty Advisor: Dr. Namgyun Kim