Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining models of human activities from the web
Proceedings of the 13th international conference on World Wide Web
A service-oriented middleware for building context-aware services
Journal of Network and Computer Applications
Hands-On RFID: Wireless Wearables for Detecting Use of Objects
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
ReachMedia: On-the-move interaction with everyday objects
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The design of a portable kit of wireless sensors for naturalistic data collection
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Cross-domain activity recognition
Proceedings of the 11th international conference on Ubiquitous computing
Activity recognition in the home using a hierarchal framework with object usage data
Journal of Ambient Intelligence and Smart Environments
Virtual walls: protecting digital privacy in pervasive environments
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Recognising activities of daily life using hierarchical plans
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
What is happening now? Detection of activities of daily living from simple visual features
Personal and Ubiquitous Computing
On-line ADL Recognition with Prior Knowledge
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Cross-domain activity recognition via transfer learning
Pervasive and Mobile Computing
A top-level ontology for smart environments
Pervasive and Mobile Computing
Time handling for real-time progressive activity recognition
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Applying hierarchical information with learning approach for activity recognition
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
Unsupervised recognition of ADLs
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Activity recognition in the home using a hierarchal framework with object usage data
Journal of Ambient Intelligence and Smart Environments
Hi-index | 0.00 |
Activity inference based on object use has received considerable recent attention. Such inference requires statistical models that map activities to the objects used in performing them. Proposed techniques for constructing these models (hand definition, learning from data, and web extraction) all share the problem of model incompleteness: it is difficult to either manually or automatically identify all the possible objects that may be used to perform an activity, or to accurately calculate the probability with which they will be used. In this paper, we show how to use auxiliary information, called an ontology, about the functional similarities between objects to mitigate the problem of model incompleteness. We show how to extract a large, relevant ontology automatically from WordNet, an online lexical reference system for the English language. We adapt a statistical smoothing technique, called shrinkage, to apply this similarity information to counter the incompleteness of our models. Our results highlight two advantages of performing shrinkage. First, overall activity recognition accuracy improves by 15.11% by including the ontology to re-estimate the parameters of models that are automatically mined from the web. Shrinkage can therefore serve as a technique for making web-mined activity models more attractive. Second, smoothing yields an increased recognition accuracy when objects not present in the incomplete models are used while performing an activity. When we replace 100% of the objects with other objects that are functionally similar, we get an accuracy drop of only 33% when using shrinkage as opposed to 91.66% (equivalent to random guessing) without shrinkage. If training data is available, shrinkage further improves classification accuracy.