Date of Award
12-2016
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Electrical Engineering
Committee Member
Dr. Richard Groff (Committee Chair), Department of Electricaland Computer Engineering
Committee Member
Dr. Joseph Ravikiran Singapogu, Institute of Bio-Engineering
Committee Member
Dr. Ian D. Walker, Department of Electrical and Computer Engineering
Abstract
Growth in surgical technology has given rise to numerous innovative methods to perform surgery and every surgery involves certain skills that have different learning curve. Due to significant growth in medical problems that involves surgery, there is a greater demand for good surgeons. Therefore there is a need to train aspiring surgeons into becoming an expert. Surgical training has undergone a drastic change in recent years with the advent of simulation based training, which allows novice surgeons to train and acquire essential surgical skills before performing an actual surgery. With the goal of objectively evaluating the level of expertise of surgeons, a training device has been designed to practice suturing skill. Suturing is surgical procedure where an incision or wound is stitched together. Performing this task efficiently requires a degree of skill and measuring that is the objective of this project. The suture training device is integrated with sensors to capture hand motion, applied force and video data to obtain parameters for skill assessment. This work has focused on using computer vision algorithms to extract vital information about the movement of the needle and the thread inside a tissue like membrane during sutures. Critical information such as the location and time of needle entry and exit, stitch length, and the needle movement underneath the tissue are some essential parameters that have been measured and recorded for future analysis and classification of surgeons based on their level of expertise.
Recommended Citation
Jagannathan, Anand, "Suture Training Device with Computer Vision Based Information Acquisition" (2016). All Theses. 2582.
https://tigerprints.clemson.edu/all_theses/2582