Therapies customization by emotion recognition in biosignals
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1315Keywords:
Emotion-Focused Therapy, Music Therapy, Artificial IntelligenceAbstract
Goal: This study aimed to develop a hybrid artificial neural network architecture to recognize mood states in biosignals from elderly individuals, including those with mild to moderate dementia, to support the personalization of therapies. Method: The study employed Wavelet Transform to convert signals into images, used as input for a hybrid architecture comprising a pre-trained convolutional neural network of LeNet type for feature extraction, and a Random Forest algorithm with 450 trees for classification. The performance of the proposed algorithm was evaluated on publicly available databases of electroencephalography and voice signals, and subsequently validated on an in-house database of elderly individuals with and without dementia. Results: The achieved accuracy ranged from 71% to 73%. Conclusion: This technology can be integrated into human-machine interfaces to personalize various therapies, such as music therapy.
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