Authors:
Negar Ghourchian
and
Doina Precup
Affiliation:
McGill University, Canada
Keyword(s):
Activity Recognition, Sensor Selection, Active Learning.
Related
Ontology
Subjects/Areas/Topics:
Active Learning
;
Applications
;
Classification
;
Learning of Action Patterns
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
Activity recognition is a key component of human-machine interaction applications. Information obtained from sensors in smart wearable devices is especially valuable, because these devices have become ubiquitous, and they record large amounts of data. Machine learning algorithms can then be used to process this data. However, wearable devices impose restrictions in terms of computation and energy resources, which need to be taken into account by a learning algorithm. We propose to use a real-time learning approach, which interactively determines the most effective set of modalities (or features) for classification, given the task at hand. Our algorithm optimizes sensor selection, in order to consume less power, while still maintaining good accuracy in classifying sequences of activities. Performance on a large, noisy dataset including four different sensing modalities shows that this is a promising approach.