JACIII Vol.24 No.5 pp. 668-675
doi: 10.20965/jaciii.2020.p0668


Adaptive Personalized Multiple Machine Learning Architecture for Estimating Human Emotional States

Akihiro Matsufuji, Eri Sato-Shimokawara, and Toru Yamaguchi

Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

February 20, 2020
June 29, 2020
September 20, 2020
affective computing, human robot interaction, non-verbal, multi-modal learning, personal modeling

Robots have the potential to facilitate the future education of all generations, particularly children. However, existing robots are limited in their ability to automatically perceive and respond to a human emotional states. We hypothesize that these sophisticated models suffer from individual differences in human personality. Therefore, we proposed a multi-characteristic model architecture that combines personalized machine learning models and utilizes the prediction score of each model. This architecture is formed with reference to an ensemble machine learning architecture. In this study, we presented a method for calculating the weighted average in a multi-characteristic architecture by using the similarities between a new sample and the trained characteristics. We estimated the degree of confidence during a communication as a human internal state. Empirical results demonstrate that using the multi-model training of each person’s information to account for individual differences provides improvements over a traditional machine learning system and insight into dealing with various individual differences.

Adaptation using personalized models

Adaptation using personalized models

Cite this article as:
A. Matsufuji, E. Sato-Shimokawara, and T. Yamaguchi, “Adaptive Personalized Multiple Machine Learning Architecture for Estimating Human Emotional States,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.5, pp. 668-675, 2020.
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