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Activity Recognition on Streaming Sensor Data.

Narayanan C Krishnan¹, Diane J Cook¹

¹School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752, USA.

Pervasive and Mobile Computing

|April 15, 2014

Summary

This summary is machine-generated.

This study introduces a real-time activity recognition method using sliding windows and sensor data. Combining mutual information weighting and past context significantly improves streaming activity recognition performance.

Keywords:

activity recognition mutual information online real-time streaming

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Area of Science:

Pervasive computing
Human-computer interaction
Machine learning for activity recognition

Background:

Real-world applications require real-time human activity information.
Pervasive computing offers non-intrusive sensors for activity data.
Current activity recognition methods often rely on pre-segmented sensor event sequences.

Purpose of the Study:

To propose and evaluate a sliding window-based approach for online activity recognition.
To enhance activity recognition by incorporating time decay and mutual information for sensor event weighting.
To improve recognition accuracy by including contextual information from previous activities and windows.

Main Methods:

Implemented a sliding window approach for continuous, real-time activity recognition.
Utilized time decay and mutual information to weight sensor events within windows.
Incorporated contextual features, including previous activity and window data, into the recognition model.

Main Results:

The proposed sliding window approach enables online activity recognition as new sensor events occur.
Mutual information-based weighting and the inclusion of past contextual information significantly boosted performance.
Experiments on real-world smart home datasets validated the effectiveness of the combined techniques.

Conclusions:

The developed sliding window method effectively performs streaming activity recognition.
Mutual information weighting and contextual features are crucial for enhancing the accuracy of real-time activity recognition.
This approach advances the capabilities of pervasive computing in understanding human activities dynamically.