| Type | Semester project |
| Split | 50% theory, 30% implementation, 20% experimentation |
| Knowledge | Programming skills: Basic Python. Common python ml libraries and data analysis is a plus |
| Subjects | Signal Pre- and Post-Processing, Data Analysis |
| Supervision | Soheil Gholami |
A recent trend exists in surgical training to objectively assess the trainees’ skills and performance instead of using traditional methods involving subjectivity and bias problems. This quantitative, objective assessment is essential to provide practical feedback and estimate surgeons’ proficiency. This project aims to systematically analyze the arms motions recorded from a microsurgical anastomosis task. The student investigates the state-of-the-art techniques in the context of surgery, to differentiate the inherent categories amongst the obtained data.
Approach
- Exploring the common features used with EMG signals, arm/hand motions.
- Exploring the common classification techniques used with signal features.
- Comparative study between these approaches and finding the most suited method to be used within the surgical context.
Expectation
- The student knows Python and learns (or knowing in advance is a plus) its common data analysis and machine learning libraries.
- The student will learn about different classification techniques [e.g., Support Vector Machine (SVM)].
- The student will learn about statistical analysis