A context-aware experience sampling tool
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Using the Experience Sampling Method to Evaluate Ubicomp Applications
IEEE Pervasive Computing
Bayesian approach to sensor-based context awareness
Personal and Ubiquitous Computing
Mining models of human activities from the web
Proceedings of the 13th international conference on World Wide Web
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Real world activity recognition with multiple goals
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Location-Based Activity Recognition
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
CIGAR: concurrent and interleaving goal and activity recognition
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Recognizing daily activities with RFID-based sensors
Proceedings of the 11th international conference on Ubiquitous computing
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Abnormal activity recognition based on HDP-HMM models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
An 'object-use fingerprint': the use of electronic sensors for human identification
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Multi levels semantic architecture for multimodal interaction
Applied Intelligence
Building health persona from personal data streams
Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia
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The fundamental problem of the existing Activity Recognition (AR) systems is that these are not general-purpose. An AR system trained in an environment would only be applicable to that environment. Such a system would not be able to recognize the new activities of interest. In this paper we propose a General-Purpose Activity Recognition System (GPARS) using simple and ubiquitous sensors. It would be applicable to almost any environment and would have the ability to handle growing amounts of activities and sensors in a graceful manner (Scalable). Given a set of activities to monitor, object names (with embedded sensors) and their corresponding locations, the GPARS first mines activity knowledge from the web, and then uses them as the basis of AR. The novelty of our system, compared to the existing general-purpose systems, lies in: (1) it uses more robust activity models, (2) it significantly reduces the mining time. We have tested our system with three real world datasets. It is observed that the accuracy of activity recognition using our system is more than 80%. Our proposed mechanism yields significant improvement (more than 30%) in comparison with its counterpart.