Vision, brain, and cooperative computation: an overview
Vision, brain, and cooperative computation
Automated derivation of behavior vocabularies for autonomous humanoid motion
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A dynamic operating system for sensor nodes
Proceedings of the 3rd international conference on Mobile systems, applications, and services
A sensory grammar for inferring behaviors in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Understanding visuo-motor primitives for motion synthesis and analysis: Research Articles
Computer Animation and Virtual Worlds - CASA 2006
Neural Circuits Involved in the Recognition of Actions Performed by Nonconspecifics: An fMRI Study
Journal of Cognitive Neuroscience
Detecting Patterns for Assisted Living Using Sensor Networks: A Case Study
SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Advances in Ambient Intelligence: Volume 164 Frontiers in Artificial Intelligence and Applications
Advances in Ambient Intelligence: Volume 164 Frontiers in Artificial Intelligence and Applications
Identifying hierarchical structure in sequences: a linear-time algorithm
Journal of Artificial Intelligence Research
Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
Journal of Artificial Intelligence Research
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semiotic schemas: A framework for grounding language in action and perception
Artificial Intelligence - Special volume on connecting language to the world
Decomposition of human motion into dynamics-based primitives with application to drawing tasks
Automatica (Journal of IFAC)
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One of the major goals of Ambient Intelligence and Smart Environments is to interpret human activity sensed by a variety of sensors. In order to develop useful technologies and a subsequent industry around smart environments, we need to proceed in a principled manner. This paper suggests that human activity can be expressed in a language. This is a special language with its own phonemes, its own morphemes (words) and its own syntax and it can be learned using machine learning techniques applied to gargantuan amounts of data collected by sensor networks. Developing such languages will create bridges between Ambient Intelligence and other disciplines. It will also provide a hierarchical structure that can lead to a successful industry.