Ontology-based sensor fusion for activity recognition

Context-aware activity recognition systems are dealing with heterogeneous sensors and these sensors are providing data at different sampling rate and output forms. Wearable sensors such as accelerometers and gyroscopes provide fast and real-time raw data which has to be interpreted before being useful to the application. Whereas ambient sensors such as temperature, humidity or object-interaction […]

Adaptive Sliding Window for Physical Activity Recognition

A sliding window with a fixed size is not an effective approach for activity recognition system. Misclassifications could still happen especially for transitional activities. This is due to the fact that the length of transitional activity signals varies depending on the time to complete the activity [1], [2]. To overcome the problem, the window size […]

Enhancing ontological reasoning with uncertainty handling for activity recognition

Handling uncertainty is a challenge in activity recognition. Uncertainty can be due to sensor errors (e.g. run out of batteries, imprecise outputs, missing activations etc.) and communication failures. and variability in human activities. These issues may significantly influence the accuracy of activity recognition. Data-driven approaches use machine learning techniques such as Decision Tree, naïve Bayes […]