Date of Award

8-2016

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Electrical Engineering

Committee Member

Dr. Richard Groff, (Committee Chair), Department of Electrical and Computer Engineering

Committee Member

Dr. Ravikiran Singapogu, Department of Bioengineering

Committee Member

Dr. Ian Walker, Department of Electrical and Computer Engineering

Abstract

Suturing is a common surgical task where surgeons stitch a particular tissue. There is an increasing demand for a tool to objectively quantify and train surgical skills. Suturing is particularly difficult to teach due to various multi-modal aspects involved in the task including applied forces, hand motion and optimal time for suturing. Towards quantifying the task of suturing, a platform is required to capture force, motion and video data while performing surgical suturing. This objective data can potentially be used to evaluate performance of a trainee and provide feedback regarding improving suturing skill. In the previous prototype of the platform, 3 key issues faced were synchronization of the three sensors, inadequate construction of the platform and the lack of a framework for image processing towards real-time assessment of suturing skill. In order to improve the platform, the aforementioned issues have been addressed in specific ways. The data collected in the system is synchronized in real-time along with a video recording for image processing and the noise due to the platform is considerably reduced by making modification to the platform construction. Data was collected on the platform with 15 novice participants. Initial analysis validates the synchronization of the sensor data. In the future, the suture skill of experts and novices will be analyzed using meaningful metrics and machine learning algorithms. This work has the potential of enabling objective and structured training and evaluation for next generation surgeons.

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