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Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns
Itsaso Rodríguez-Moreno1, José María Martínez-Otzeta1, Izaro Goienetxea1
1Department of Computer Science and Artificial Intelligence, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia-San Sebastián, Spain.
Sensors (Basel, Switzerland)
|April 30, 2020
Summary
This study introduces a novel action recognition method for robots using Common Spatial Patterns (CSP) on skeleton data. The CSP-based approach achieves comparable or superior results to Long Short-Term Memory networks, enhancing robot social interaction.
Area of Science:
- Robotics
- Computer Vision
- Signal Processing
Background:
- Action recognition is crucial for robot social interaction.
- Existing methods often require complex models.
Purpose of the Study:
- To develop a simpler yet effective action recognition method for humanoid robots.
- To enable more natural human-robot social interaction through enhanced perception.
Main Methods:
- Skeleton data sequences from robot-captured videos were treated as multidimensional signals.
- Common Spatial Patterns (CSP) algorithm was applied for signal filtering.
- Extracted features were used with a Linear Discriminant Analysis (LDA) classifier.
Main Results:
- The CSP-based method demonstrated comparable or superior performance to Long Short-Term Memory (LSTM) networks.
- The proposed method proved to be computationally simpler than LSTM networks.
- A new database of 46 individuals performing six actions was created for validation.
Conclusions:
- CSP offers a viable and efficient alternative for action recognition in robotics.
- This method can facilitate more natural social interactions for humanoid robots.
- The CSP-LDA approach provides a robust solution for video-based activity recognition.
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