Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Enabling nutrition-aware cooking in a smart kitchen
CHI '07 Extended Abstracts on Human Factors in Computing Systems
Ambient kitchen: designing situated services using a high fidelity prototyping environment
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
uWave: Accelerometer-based personalized gesture recognition and its applications
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Sensor-Based Human Activity Recognition in a Multi-user Scenario
AmI '09 Proceedings of the European Conference on Ambient Intelligence
The diet-aware dining table: observing dietary behaviors over a tabletop surface
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Distributed event processing for activity recognition
Proceedings of the 5th ACM international conference on Distributed event-based system
On the practicality of motion based keystroke inference attack
TRUST'12 Proceedings of the 5th international conference on Trust and Trustworthy Computing
The french kitchen: task-based learning in an instrumented kitchen
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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We present a dynamic time warping based activity recognition system for the analysis of low-level food preparation activities. Accelerometers embedded into kitchen utensils provide continuous sensor data streams while people are using them for cooking. The recognition framework analyzes frames of contiguous sensor readings in real-time with low latency. It thereby adapts to the idiosyncrasies of utensil use by automatically maintaining a template database. We demonstrate the effectiveness of the classification approach by a number of real-world practical experiments on a publically available dataset. The adaptive system shows superior performance compared to a static recognizer. Furthermore, we demonstrate the generalization capabilities of the system by gradually reducing the amount of training samples. The system achieves excellent classification results even if only a small number of training samples is available, which is especially relevant for real-world scenarios.