An adaptive rule-based approach to classifying activities of daily living

The need for a human activity recognition system arises when designing a "health smart home" which monitors its occupants to assess their health status. In this work, a rule-based system was constructed to classify the common activities of daily living based on a hierarchical approach, using location measurements from a commercial ultrasonic sensor system. Adaptive rule application was achieved by applying contextual information from adjacent time steps using a finite-state machine. Some common static and dynamic activities of daily living were chosen as the targets for classification. The system was shown to provide comparable performance with results which have been reported for more complex alternative systems. Results reported showed a minimum classification accuracies of 87.7% for the walking activity. The deployed adaptive rule-based system provides a robust and computationally inexpensive solution for common in-situ human activity recognition purposes.