Activity recognition using biomechanical model based pose estimation
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Proceedings of the 2nd Augmented Human International Conference
Detecting leisure activities with dense motif discovery
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Qualitative activity recognition of weight lifting exercises
Proceedings of the 4th Augmented Human International Conference
A survey on smartphone-based systems for opportunistic user context recognition
ACM Computing Surveys (CSUR)
A tutorial on human activity recognition using body-worn inertial sensors
ACM Computing Surveys (CSUR)
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Pattern Recognition Letters
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Model-based activity recognition has been recently proposed as an alternative to signal-oriented recognition. Such model-based approaches seem attractive due to their ability to enable user-independent activity recognition and due to their improved robustness to signal-variation. The first goal of this paper is therefore to systematically analyze the benefit of body-model derived primitives in different sensor settings for multi activity recognition. Furthermore we propose a new body-model based approach using accelerometer sensors only thereby reducing the sensor requirements significantly. Results on a 20 activity dataset indicate that body-model based approaches consistently improve results over signal-oriented approaches.