Explaining rifle shooting factors through multi-sensor body tracking : Using transformers and attention to mine actionable patterns from skeleton graphs

Sammanfattning: There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but most of them have focused on single aspects of postural control. This thesis has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. We performed a data collection with 13 human participants who carried out live rifle shooting scenarios while being recorded with multiple biometric sensors, including several body trackers. An experiment was conducted to identify what aspects of rifle shooting could be predicted and explained using these data. We employed a pre-processing pipeline to produce a novel skeleton sequence representation, and used it to train a transformer model. The predictions from this model could be explained on a per sample basis using the attention mechanism, and visualised in an interactive format for humans to interpret. It was possible to separate the different phases of a shooting scenario from body posture with a high classification accuracy (81%). However, no correlation could be shown between shooting performance and body posture from our data. Future work could focus on novel feature engineering, and on examining alternative machine learning approaches. The dataset and pre-processing pipeline, as well as the techniques for generating explainable predictions presented in this thesis has laid the groundwork for future research in the sports shooting domain.