3D human pose estimation is a widely researched computer vision task that could be applied in scenarios such as virtual reality and human-robot interaction. With the lack of depth information, 3D estimation from monocular images is an inherently ambiguous problem. On top of that, unrealistic human poses have been overlooked in the majority of papers since joint detection is the only focus. Our work consists of two parts, an end-to-end 2D-3D lifting pipeline and a novel kinematic human model integrated approach. We start with Pose Estimation using TRansformer (PETR), an approach that does not require temporal information and has the attention mechanism to model the inter-joint relationship from RGB images. In the approach with human model, we emphasize pose similarity rather than focusing on joint detection. We propose a new metric, called Mean Per Bone Vector Error (MPBVE), that evaluates poses regardless of a human body’s gender, weight, or age. We introduce Pose Estimation on Bone Rotation using Transformer (PEBRT), a novel approach that regresses rotation matrices for 16 human bones, assuming labeled 2D poses as input. Our human model encapsulates joint angle and bone length constraints. Existing methods treat these constraints as an additional loss term, which does not guarantee realistic final out- puts. Our method does not require temporal information or receptive fields to generate kinematically realistic human poses. We demonstrate that PEBRT is capable of delivering comparable results on Human3.6M to existing methods.

Tooth removal is one of the most performed surgical procedures worldwide. Despite the high amount of tooth removal procedures carried out each year, scientific understanding of these procedures is not present. Knowledge of force and torque behaviour is limited and knowledge about movements has never been subject to scientific research before. This study is an initial attempt to describe the factors that influence tooth removal in terms of forces, torques and movements. In-vitro measurements were performed that resulted in a dataset containing force, torque and movement time series of 181 human demonstrations of tooth extractions. This report showed how feature engineering and classification modelling were employed to find tooth removal explaining parameters in the dataset. The feature engineering process led to numerical features describing the force and movement (rotation) time series. The rotation features were found to be most descriptive in describing differences in tooth removal procedures. This introduced five distinct rotation strategies that grouped the human demonstrations based on similarity of extraction strategy. These groups have been used as classification labels in the supervised learning process. A Naive Bayes algorithm and a Logistic Regression algorithm were implemented as prediction models. These models showed that while the rotation features contributed the most to the prediction performance, there was need for additional force features to reach maximum prediction performance. The results showed how feature engineering and classification modelling are the first steps in understanding the procedure of tooth removal.