Skill Assessment Using Kinematic Signatures: Geomagic Touch Haptic Device

The aim of this paper is to develop a practical skill assessment for some designed experimental tasks, retrieved from Minimally Invasive Surgery. The skill evaluation is very important in surgery training, especially in MIS. Most of the previous studies for skill assessment methods are limited in the Hidden Markov Model and some frequency transforms, such as Discrete Fourier transform, Discrete Cosine Transform and etc. In this paper, some features have been extracted from time-frequency analysis with the Discrete Wavelet Transform and temporal signal analysis of some kinematic metrics which were computed from Geomagic Touch kinematic data. In addition, the k-nearest neighbors classifier are employed to detect skill level based on extracted features. Through cross-validation results, it is demonstrated that the proposed methodology has annrouriate accuracy in skill level detection.