Towards a general theory of action and time
Artificial Intelligence
Natural language parsing systems
Natural language parsing systems
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
A survey on temporal reasoning in artificial intelligence
AI Communications
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
A mining technique using n-grams and motion transcripts for body sensor network data repository
WH '10 Wireless Health 2010
Behavior-oriented data resource management in medical sensing systems
ACM Transactions on Sensor Networks (TOSN)
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In application domains such as medicine, where a large amount of data is gathered, a medical diagnosis and a better understanding of the underlying generating process is an aim. Recordings of temporal data often afford an interpretation of the underlying pattens. This means that for diagnosis purposes a symbolic, i.e. understandable and interpretable representation of the results for physicians, is needed. This paper proposes the use of definitive-clause grammars for the induction of temporal expressions, thereby providing a more powerful framework than context-free grammars. An implementation in Prolog of these grammars is then straightforward. The main idea lies in introducing several abstraction levels, and in using unsupervised neural networks for the pattern discovery process. The results at each level are then used to induce temporal grammatical rules. The approach uses an adaptation of temporal ontological primitives often used in Al-systems.