Activity Recognition using Hierarchical Hidden Markov Models on Streaming Sensor Data

TL;DR

This paper proposes an online hierarchical hidden Markov model for real-time activity recognition from streaming sensor data, addressing challenges like overlapping activities and activity detection in smart home environments.

Contribution

It introduces a novel online hierarchical hidden Markov model approach for activity recognition using streaming sensor data, improving detection accuracy in real-world smart home datasets.

Findings

Achieved 59% accuracy on one dataset with a 4% improvement.

Reached 64.6% accuracy on another dataset.

Demonstrated effectiveness of the method in real-world smart home scenarios.

Abstract

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.