A concept for emotion recognition systems for children with profound intellectual and multiple disabilities based on
Hiroki Tanabe1, Toshihiko Shiraishi1, Haruhiko Sato2
1Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Japan.
Insights
This study introduces an artificial intelligence (AI) system for recognizing emotions in children with profound intellectual and multiple disabilities (PIMD) using physiological signals. The AI system achieved a higher accuracy than human observers, aiding communication with these children.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence
- Developmental Psychology
Background:
- Effective communication is crucial for individuals with profound intellectual and multiple disabilities (PIMD).
- Current methods for assessing emotional states in children with PIMD are limited.
- Technological advancements offer new avenues for understanding non-verbal communication.
Purpose of the Study:
- To propose and validate a concept for an artificial intelligence (AI)-based emotion recognition system for children with PIMD.
- To develop an AI system utilizing physiological and motion signals for emotion detection.
- To enhance communication and support for children with PIMD.
Main Methods:
- Collected heartbeat interval (R-R interval, RRI) data and tested its correlation with emotional states.
- Developed an AI emotion recognition system using a random forest classifier.
- Integrated physiological (RRI) and motion (eye gaze) data from low-burden sensors.
Main Results:
- A significant correlation was observed between RRI and emotional states.
- The developed AI system achieved a 70.4% ± 6.1% accuracy in distinguishing between "negative" and "not negative" emotions.
- This accuracy surpassed the 48.5% ± 5.0% accuracy of a control group (unfamiliar person).
Conclusions:
- The proposed AI-based emotion recognition system concept is effective for children with PIMD.
- The system offers a valuable tool for improving communication with children with PIMD.
- Further development can enhance the system's capabilities and applications.
Purpose:
This study proposes a concept for emotion recognition systems for children with profound intellectual and multiple disabilities (PIMD) based on artificial intelligence (AI) using physiological and motion signals.
Methods:
First, the heartbeat interval (R-R interval, RRI) of a child with PIMD was measured, and the correlation between the RRI and emotion was briefly tested in a preliminary experiment. Then, a concept based on AI for emotion recognition systems for children with PIMD was created using physiological and motion signals, and an emotion recognition system based on the proposed concept was developed using a random forest classifier taking as inputs the RRI, eye gaze, and other data acquired using low physical burden sensors. Subsequently, the developed emotion recognition system was evaluated, validating the proposed concept. Finally, we proposed a validated concept for emotion recognition systems.
Results:
A correlation was found between the RRI and emotion. The emotion recognition system was created based on the proposed concept and tested. According to the results, the recognition rate of "negative" and "not negative" of 70.4% ± 6.1% (Mean ± S.D.) of the developed emotion recognition system was higher than 48.5% ± 5.0% of an unfamiliar person used as a control.
Conclusion:
The results indicate that the proposed concept for emotion recognition systems is useful for communicating with children with PIMD.
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