Qualitative activity recognition of weight lifting exercises

Authors:
Eduardo Velloso;Andreas Bulling;Hans Gellersen;Wallace Ugulino;Hugo Fuks
Affiliations:
Lancaster University, Lancaster, UK;Max Planck Institute for Informatics, Saarbrücken, Germany;Lancaster University, Lancaster, UK;Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil;Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
Venue:
Proceedings of the 4th Augmented Human International Conference
Year:
2013

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Abstract

Research on activity recognition has traditionally focused on discriminating between different activities, i.e. to predict which activity was performed at a specific point in time. The quality of executing an activity, the how (well), has only received little attention so far, even though it potentially provides useful information for a large variety of applications. In this work we define quality of execution and investigate three aspects that pertain to qualitative activity recognition: specifying correct execution, detecting execution mistakes, providing feedback on the 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.