Activity Recognition using Hierarchical Hidden Markov Models on Streaming Sensor Data
This addresses the problem of real-time activity detection in smart homes, though it appears incremental as it builds on existing methods with modest gains.
The paper tackles online activity recognition from streaming sensor data using hierarchical hidden Markov models, achieving accuracy improvements of up to 4% on real-world smart home datasets, with results reaching 59% and 64.6%.
Activity recognition from sensor data deals with various challenges, such as overlapping activities, activity labeling, and activity detection. Although each challenge in the field of recognition has great importance, the most important one refers to online activity recognition. The present study tries to use online hierarchical hidden Markov model to detect an activity on the stream of sensor data which can predict the activity in the environment with any sensor event. The activity recognition samples were labeled by the statistical features such as the duration of activity. The results of our proposed method test on two different datasets of smart homes in the real world showed that one dataset has improved 4% and reached (59%) while the results reached 64.6% for the other data by using the best methods.