Recognizing complex activities is a challenging research problem, particularly in the presence of strong variability in the way activities are performed. Food preparation activities are prime examples, involving many different utensils and ingredients as well as high inter-person variability. Recognition models need to adapt to users in order to robustly account for differences between them. This paper presents three methods for user-adaptation: combining classifiers that were trained separately on generic and user-specific data, jointly training a single support vector machine from generic and user-specific data, and a weighted K-nearest-neighbor formulation with different probability mass assigned to generic and user-specific samples. The methods are evaluated on video and accelerometer data of people preparing mixed salads. A combination of generic and user-specific models considerably increased activity recognition accuracy and was shown to be particularly promising when data from only a limited number of training subjects was available.
|Title of host publication||CEA 2013 - Proceedings of the 5th International Workshop on Multimedia for Cooking and Eating Activities|
|Number of pages||6|
|Publication status||Published - 1 Jan 2013|
Multi-Modal Recognition of Manipulation Activities through Visual Accelerometer Tracking, Relational Histograms, and User-AdaptationAuthor: Stein, S., 2014
Supervisor: McKenna, S. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy
Stein, S., & McKenna, S. J. (2013). User-adaptive models for recognizing food preparation activities. In CEA 2013 - Proceedings of the 5th International Workshop on Multimedia for Cooking and Eating Activities (pp. 39-44) https://doi.org/10.1145/2506023.2506031