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Human Activity Classification Using Multilayer Perceptron.

Ojan Majidzadeh Gorjani¹, Radek Byrtus¹, Jakub Dohnal¹

¹Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 700 30 Ostrava, Czech Republic.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

Smart homes can now monitor resident well-being using advanced data analysis. This study uses a neural network to accurately recognize human activities from wearable sensors, enhancing smart home capabilities beyond basic monitoring.

Keywords:

artificial neural network (ANN)human activity recognition intelligent buildings (IB)smart home (SH)

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Area of Science:

Human-Computer Interaction
Artificial Intelligence
Wearable Technology

Background:

Smart homes are increasingly prevalent, offering convenience and automation.
Integrating smart home data with advanced processing can yield insights into resident well-being.
Current smart home analytics often focus on occupancy and fall detection.

Purpose of the Study:

To advance smart home data analysis beyond traditional monitoring.
To develop a system for recognizing multiple human activities within a smart home environment.
To leverage wearable sensor data for enhanced well-being insights.

Main Methods:

Utilized a multilayer perceptron neural network for human activity recognition.
Employed data from wrist- and ankle-worn sensors.
Implemented rigorous cross-validation and scoring evaluation methods.

Main Results:

Achieved very high recognition accuracy for multiple human activities.
Cross-validation demonstrated accuracy levels exceeding 98% across all models.
Scoring evaluations showed only a minor average accuracy reduction of 10%.

Conclusions:

The developed neural network models are highly effective for human activity recognition in smart homes.
This approach significantly enhances the potential for monitoring resident well-being through smart home technology.
The findings support the expansion of smart home data analysis for comprehensive inhabitant monitoring.