Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Qualitative activity recognition of weight lifting exercises
AU - Velloso, Eduardo
AU - Bulling, Andreas
AU - Gellersen, Hans
AU - Ugulino, Wallace
AU - Fuks, Hugo
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - qualitative activity recognition
KW - real-time user feedback
KW - weight lifting
U2 - 10.1145/2459236.2459256
DO - 10.1145/2459236.2459256
M3 - Conference contribution/Paper
SN - 978-1-4503-1904-1
SP - 116
EP - 123
BT - Proceedings of the 4th Augmented Human International Conference
PB - ACM
CY - New York, NY, USA
T2 - 4th Augmented Human International Conference
Y2 - 7 March 2013 through 8 March 2013
ER -