HDI methods are still of limited semantic abstraction power. Using selected state-of-the-art examples Machine learning Pattern Recognition Sensor Data Interpretation The rapid development in the area of sensor technology has been responsible for a number of societal phenomena like UGC (User Generated Content) or QS (Quantified Self). Machine learning algorithms benefit a lot from the availability of such huge volumes of digital data. For example VSA and HDI. especially in the context of healthcare systems in industrialised countries. The goal of this book is to present selected algorithms for Visual Scene Analysis (VSA new technical solutions for challenges caused by the demographic change (ageing society) can be proposed in this way processing UGC) as well as for Human Data Interpretation (HDI this book shows the maturity of approaches towards closing the semantic gap in both areas using data produced within the QS movement) and to expose a joint methodological basis between these two scientific directions. While VSA approaches have reached impressive robustness towards human-like interpretation of visual sensor data