An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Learning large scale common sense models of everyday life
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
What is happening now? Detection of activities of daily living from simple visual features
Personal and Ubiquitous Computing
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Challenges of human behavior understanding
HBU'10 Proceedings of the First international conference on Human behavior understanding
A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living
Expert Systems with Applications: An International Journal
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Activity recognition can be seen as a local task aimed at identifying an on-going activity performed at a certain time, or a global one identifying time segments in which a certain activity is being performed. We combine these tasks by a hierarchical approach which locally predicts on-going activities by a Support Vector Machine and globally refines them by a Conditional Random Field focused on time segments involving related activities. By varying temporal scales in order to account for widely different activity durations, we achieve substantial improvements in on-going activity recognition on a realistic dataset from the PlaceLab sensing environment. When focusing on periods within which related activities are known to be performed, the refinement stage manages to exploit these relationships in order to correct inaccurate local predictions.