Date of Award
6-8-2025
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2025
Department
Electrical and Computer Engineering
First Advisor
Maria Kyrarini
Second Advisor
Fatemeh Davoudi Kakhki
Abstract
For decades, robots have been kept in cages in industry. With the advances of collaborative robots and Artificial Intelligence (AI), there is a shift towards humans and robots working together. In this research, we propose an ergonomically friendly collaborative robotic cell that enables a human and a collaborative robot to work synergistically to assemble a mobile robot. The collaborative robot provides the parts while explaining the process through a computer, and the human co-worker follows the instructions to complete the assembly. The proposed collaborative robotic cell is evaluated in a user study to ensure that the handovers of the parts between the robot and the human happen ergonomically. Muscle activation data is gathered during human-subject experiments using surface electromyography (sEMG) sensors and post-experiment data through a survey. Three machine learning models, including 4-layer neural networks (4-layer NN), convolutional neural networks (CNN), and long short-term memory (LSTM), are developed to classify ergonomic and non-ergonomic status of muscle activation patterns using the Rapid Upper Limb Assessment (RULA) scores. The results show that the NN are capable of distinguishing the ergonomic status of the experiment with 80%, 83%, and 88% for LSTM, 4-layer NN, and CNN, respectively. The post-experiment questionnaire evaluating the human interaction with the collaborative robot showed positive experience, specifically on safety, usability, and trust in the system we developed. Our findings provide insights into the successful implementation and evaluation of human-robot collaboration in industrial assembly tasks. This research contributes to the growing trend of ergonomically centered robot design, focusing on reducing the risk of musculoskeletal disorders (MSDs) among human workers through collaborative robotics. Notably, the F1 scores of our models highlight the reliability of our ergonomic status classification. Overall, this study underscores the potential of cobots in creating safer and more efficient work environments while prioritizing human health and well-being.
Recommended Citation
Murphy, Maya and Mishkanian, Michael, "Ergonomic Human Robot Handovers Using Surface Electromyography (sEMG) Sensors" (2025). Electrical and Computer Engineering Senior Theses. 107.
https://scholarcommons.scu.edu/elec_senior/107
