Modeling and prediction of human behavior
Neural Computation
Imitation in animals and artifacts
Computational Modeling and Analysis of Knowledge Sharing in Collaborative Distance Learning
User Modeling and User-Adapted Interaction
International Journal of Robotics Research
Searching for Complex Human Activities with No Visual Examples
International Journal of Computer Vision
Learning to Recognize Activities from the Wrong View Point
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Journal of Intelligent and Robotic Systems
International Journal of Computer Vision
Zero knowledge hidden Markov model inference
Pattern Recognition Letters
Learning atomic human actions using variable-length Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Incremental Learning and Memory Consolidation of Whole Body Human Motion Primitives
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Intelligent coaching of mobile working machine operators
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Automatic extraction of highlights from a cricket video using MPEG-7 descriptors
COMSNETS'09 Proceedings of the First international conference on COMmunication Systems And NETworks
Mimesis Model from Partial Observations for a Humanoid Robot
International Journal of Robotics Research
Behavior detection using confidence intervals of hidden Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmentation of human body parts using deformable triangulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Hand gesture recognition using gabor and radon transform with invariant moment features
CSECS '10 Proceedings of the 9th WSEAS international conference on Circuits, systems, electronics, control & signal processing
Accelerometry-based classification of human activities using Markov Modeling
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Handshake: Realistic human-robot interaction in haptic enhanced virtual reality
Presence: Teleoperators and Virtual Environments
Pedestrian-movement prediction based on mixed Markov-chain model
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A mixed autoregressive hidden-markov-chain model applied to people's movements
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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To successfully interact with and learn from humans in cooperative modes, robots need a mechanism for recognizing, characterizing, and emulating human skills. In particular, it is our interest to develop the mechanism for recognizing and emulating simple human actions, i.e., a simple activity in a manual operation where no sensory feedback is available. To this end, we have developed a method to model such actions using a hidden Markov model (HMM) representation. We proposed an approach to address two critical problems in action modeling: classifying human action-intent, and learning human skill, for which we elaborated on the method, procedure, and implementation issues in this paper. This work provides a framework for modeling and learning human actions from observations. The approach can be applied to intelligent recognition of manual actions and high-level programming of control input within a supervisory control paradigm, as well as automatic transfer of human skills to robotic systems