Team Leader
Muhammad Khan
Texas A&M University
Construction Science
mkhan13@tamu.edu
Project Type
Research
Who Can Join
Graduate Students, Masters Students, Undergraduate Students
Project Description
This project develops an intelligent monitoring system that combines wearable sensors and computer vision to detect exoskeleton misalignment during dynamic construction tasks. By fusing multiple AI models, the system aims to identify poor fit or alignment issues in real-time, ensuring safe and effective exoskeleton use while reducing the risk of injuries and performance degradation.
Team Needs
This project requires team members with a strong background in computer science to support the development and implementation of AI-based models for exoskeleton misalignment detection. Specifically, we are looking for individuals with skills in:
Machine Learning & Deep Learning – building and training AI models for multimodal data fusion (sensor + camera data).
Computer Vision – developing algorithms to process video streams and identify exoskeleton misalignment during dynamic construction tasks.
Data Processing & Integration – handling sensor signals (IMU, pressure, etc.) and synchronizing them with camera input for analysis.
Programming & Frameworks – experience in Python, TensorFlow/PyTorch, and real-time system integration.
A background in computer science, data science, or related fields will be highly valuable. Prior experience with human activity recognition, wearable data, or robotics applications is a plus.
Special Opportunities
Students participating in this project will gain hands-on experience working directly with wearable exoskeletons and advanced sensing technologies. Team members will have the opportunity to “play” with exoskeleton systems during real or simulated construction tasks, giving them practical insights into human–robot interaction and ergonomics.
In addition to technical skill development in AI modeling, sensor fusion, and computer vision, students will also benefit from working in a collaborative research environment. If the project results in significant findings, there will be opportunities for co-authorship on conference papers or journal publications, providing strong academic and professional credentials.