The goal of this study is to develop a classification model that can accurately and efficiently label human kinematics data. Kinematics data provides information about the movement of individuals by placing sensors on the human body and tracking their velocity, acceleration and position in three dimensions. These data points are available in C3D format that contains numerical data transformed from 3D data captured from the sensors. The data points can be used to analyse movements of injured patients or patients with physical disorders. To get an accurate view of the movements, the datasets generated by the sensors need to be properly labelled. Due to inconsistencies in the data capture process, there are instances where the markers have missing data or missing labels. The missing labels are a hindrance in motion analysis as it introduces noise and produces incomplete data points of sensor’s positioning in 3 dimensional spaces. Labelling the data manually introduces substantial effort in the analysis process. In this paper, we will describe approaches to pre-process the kinematics data from its raw format and label the data points with missing markers using classification models.
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