Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
GI '05 Proceedings of Graphics Interface 2005
Activity-based serendipitous recommendations with the Magitti mobile leisure guide
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sensor-based understanding of daily life via large-scale use of common sense
AAAI'06 Proceedings of the 21st 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
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
A Semantically-Based Task Model and Selection Mechanism in Ubiquitous Computing Environments
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Activity recognition using temporal evidence theory
Journal of Ambient Intelligence and Smart Environments
On using temporal features to create more accurate human-activity classifiers
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
OntoIAS: An ontology-supported information agent shell for ubiquitous services
Expert Systems with Applications: An International Journal
An ontology-supported ubiquitous interface agent for cloud computing: example on Zigbee technique
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
Getting closer: an empirical investigation of the proximity of user to their smart phones
Proceedings of the 13th international conference on Ubiquitous computing
Journal of Systems and Software
Engineering Applications of Artificial Intelligence
Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Habits make smartphone use more pervasive
Personal and Ubiquitous Computing
Passive detection of situations from ambient FM-radio signals
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Improving activity recognition without sensor data: a comparison study of time use surveys
Proceedings of the 4th Augmented Human International Conference
Placer: semantic place labels from diary data
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Understanding customer malling behavior in an urban shopping mall using smartphones
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
A tutorial on human activity recognition using body-worn inertial sensors
ACM Computing Surveys (CSUR)
Using time use with mobile sensor data: a road to practical mobile activity recognition?
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
Human activity recognition using social media data
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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Governments and commercial institutions have conducted detailed time-use studies for several decades. In these studies, participants give a detailed record of their activities, locations, and other data over a day, week, or longer period. These studies are particularly valuable for the ubicomp community because of the large number of participants (often the tens of thousands), and because of their public availability. In this paper, we show how to use the data from these studies to provide validated and cheap (although noisy) classifiers, baseline metrics, and other benefits for activity inference applications.