A User Behavior Perception Model Based on Markov Process
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Activity-aware computing in mobile collaborative working environments
CRIWG'07 Proceedings of the 13th international conference on Groupware: design implementation, and use
Statistic-based context recognition in smart car
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
An ambient intelligent agent model based on behavioural monitoring and cognitive analysis
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Pervasive and Mobile Computing
Adaptive daily rhythm atmospheres for stroke patients: a staff evaluation
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Detection of daily living activities using a two-stage Markov model
Journal of Ambient Intelligence and Smart Environments - Intelligent agents in Ambient Intelligence and smart environments
| Hi-index | 0.00 |
Context-aware computing offers several advantages for human computer interaction by augmenting ambient intelligence environments with computational artifacts that can be responsive to the needs of users. One of the main challenges in context-aware computing is context recognition. While some contextual variables, such as location, can be easily recognized, others, such as activity are more complex to estimate. This paper describes an approach to estimate activities in a working environment. The approach is based on information gathered from a workplace study, in which 196 hours of detailed observation of hospital workers were recorded. This data is used to train a Hidden Markov Model to estimate user activity. The results indicate that the user activity can be correctly estimated 92.6% of the time. We compare our results with the use of neuronal networks and human observers familiar with those work practice. We discuss how these results can be used for context-aware applications.