Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Active learning for Hidden Markov Models: objective functions and algorithms
ICML '05 Proceedings of the 22nd international conference on Machine learning
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones
Proceedings of the 5th international conference on Mobile systems, applications and services
Activity Recognition Based on Semi-supervised Learning
RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Experience sampling for building predictive user models: a comparative study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
On using existing time-use study data for ubiquitous computing applications
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Using a complex multi-modal on-body sensor system for activity spotting
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Using rhythm awareness in long-term activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Exploring semi-supervised and active learning for activity recognition
ISWC '08 Proceedings of the 2008 12th 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
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Common sense based joint training of human activity recognizers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distributed vision-based accident management for assisted living
ICOST'07 Proceedings of the 5th international conference on Smart homes and health telematics
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Toward scalable activity recognition for sensor networks
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
A comparison of HMMs and dynamic bayesian networks for recognizing office activities
UM'05 Proceedings of the 10th international conference on User Modeling
MIForests: multiple-instance learning with randomized trees
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
HMM based semi-supervised learning for activity recognition
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Pervasive and Mobile Computing
Proceedings of the 2nd Conference on Wireless Health
Ambient Assisted Living system for in-home monitoring of healthy independent elders
Expert Systems with Applications: An International Journal
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Activity recognition has attracted increasing attention in recent years due to its potential to enable a number of compelling context-aware applications. As most approaches rely on supervised learning methods, obtaining substantial amounts of labeled data is often an important bottle-neck for these approaches. In this paper, we present and explore a novel method for activity recognition from sparsely labeled data. The method is based on multi-instance learning allowing to significantly reduce the required level of supervision. In particular we propose several novel extensions of multi-instance learning to support different annotation strategies. The validity of the approach is demonstrated on two public datasets for three different labeling scenarios.