Affiliations: | STEM Research Leadership |
Project Leader: | Muhammad Khan
mkhan13@tamu.edu Construction Science |
Faculty Mentor: | Chukwuma (Chuma) Nnaji, Ph.D. |
Meeting Times:
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TBA |
Team Size:
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8
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Open Spots: | 0 |
Special Opportunities:
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Students participating in this project will have several unique opportunities to enhance their academic and professional development:
Exploration of Cutting-Edge Research: Gain hands-on experience with advanced sensor technology and data analysis methods, offering deep insights into the growing field of safety engineering and wearable technology. Skill Development: Learn valuable skills in data collection, analysis, and interpretation using real-world sensor and camera data, which are applicable across multiple disciplines such as biomechanics, computer vision, and machine learning. Co-Authorship: Contribute to research papers and potentially earn co-authorship on publications, adding a significant credential to your academic resume. Full Membership in Research Group: Become a valued member of an interdisciplinary research group, working closely with graduate students and faculty, and playing a critical role in advancing research that could make workplaces safer across various industries. |
Team Needs:
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Have fundamental knowledge of python to perform data cleaning and sub tasks. |
Description:
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Falls are a leading cause of injury, especially in environments where physical work is demanding, such as in construction or manufacturing. This project aims to improve safety by predicting falls before they happen. We will use advanced insole sensors that can measure pressure and movement in real-time, combined with camera data, to analyze how people walk and move. Our goal is to understand what factors lead to falls, particularly when workers are wearing exoskeletons—devices designed to reduce strain during heavy lifting and other tasks.
The data collected will help us develop a system that can alert workers to potential risks, both when they are using an exoskeleton and when they are not. By integrating sensor and camera data, we hope to create more accurate predictions, ultimately reducing the number of fall-related accidents. This project offers a unique opportunity for students to get hands-on experience with cutting-edge technology and contribute to a safer working environment for many industries. |