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Abstract

Research on human activity recognition has traditionally focused on discriminating between different activities, i.e. to predict \textquoteleft}{\textquoteleft}which{\textquoteright}{\textquoteright} activity was performed at a specific point in time. The quality of executing an activity, the {\textquoteleft}{\textquoteleft}how (well){\textquoteright}{\textquoteright, has only received little attention so far, even though it potentially provides useful information for a large variety of applications, such as sports training. In this work we first define quality of execution and investigate three aspects that pertain to qualitative activity recognition: the problem of specifying correct execution, the automatic and robust detection of execution mistakes, and how to provide feedback on the quality of execution to the user. We illustrate our approach on the example problem of qualitatively assessing and providing feedback on weight lifting exercises. In two user studies we try out a sensor- and a model-based approach to qualitative activity recognition. Our results underline the potential of model-based assessment and the positive impact of real-time user feedback on the quality of execution.