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Lower back pain is one of the most prevalent diseases in Western societies. A large percentage of European and American populations suffer from back pain at some point in their lives. One successful approach to address lower back pain is postural training, which can be supported by wearable devices, providing real-time feedback about the user’s posture. In this work, we analyze the changes in posture induced by postural training. To this end, we compare snapshots before and after training, as measured by the Gokhale SpineTracker™. Considering pairs of before and after snapshots in different positions (standing, sitting, and bending), we introduce a feature space, that allows for unsupervised clustering. We show that resulting clusters represent certain groups of postural changes, which are meaningful to professional posture trainers.
The work at hand outlines a recording setup for capturing hand and finger movements of musicians. The focus is on a series of baseline experiments on the detectability of coloured markers under different lighting conditions. With the goal of capturing and recording hand and finger movements of musicians in mind, requirements for such a system and existing approaches are analysed and compared. The results of the experiments and the analysis of related work show that the envisioned setup is suited for the expected scenario.
We propose a high-performance GPU implementation of Ray Histogram Fusion (RHF), a denoising method for stochastic global illumination rendering. Based on the CPU implementation of the original algorithm, we present a naive GPU implementation and the necessary optimization steps. Eventually, we show that our optimizations increase the performance of RHF by two orders of magnitude when compared to the original CPU implementation and one order of magnitude compared to the naive GPU implementation. We show how the quality for identical rendering times relates to unfiltered path tracing and how much time is needed to achieve identical quality when compared to an unfiltered path traced result. Finally, we summarize our work and describe possible future applications and research based on this.