A concept for emotion recognition systems for children with profound intellectual and multiple disabilities based on artificial intelligence using physiological and motion signals.

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. First, the heartbeat interval (R–R interval, RRI) of a child with PIMD was measured, an...

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Publicado en:Disability & Rehabilitation: Assistive Technology Vol. 19; no. 4; pp. 1319 - 1327
Autores principales: Tanabe, Hiroki, Shiraishi, Toshihiko, Sato, Haruhiko, Nihei, Misato, Inoue, Takenobu, Kuwabara, Chika
Formato: pictorial research tables/charts Journal Article
Publicado: Taylor & Francis Ltd May2024
Acceso en línea:Ver este registro en EBSCOhost
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      dt: May2024
      vid: 19
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/17483107.2023.2170478
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        atl: A concept for emotion recognition systems for children with profound intellectual and multiple disabilities based on artificial intelligence using physiological and motion signals.
      aug:
        au:
          Tanabe, Hiroki
          Shiraishi, Toshihiko
          Sato, Haruhiko
          Nihei, Misato
          Inoue, Takenobu
          Kuwabara, Chika
        affil: Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Japan
      sug:
        subj:
          Child Behavior Evaluation
          Children with Disabilities Psychosocial Factors
          Emotions Evaluation
          Intellectual Disability Rehabilitation
          Motion Analysis Systems
          Artificial Intelligence
          Human
          Heart Rate
          Male
          Descriptive Statistics
          Adult
          Middle Age
          Funding Source
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Male
      ab: 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. 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. 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. The results indicate that the proposed concept for emotion recognition systems is useful for communicating with children with PIMD. A new concept based on artificial intelligence for emotion recognition systems for children with profound intellectual and multiple disabilities (PIMD) using physiological and motion signals is proposed. An emotion recognition system based on the proposed concept developed using a random forest classifier taking as inputs the heartbeat interval, eye gaze, and other data acquired using low physical burden sensors were tested in terms of the emotion recognition rate. The recognition rate of "negative" and "not negative" of the developed system (i.e., 70.4% ± 6.1%) is higher than that of an unfamiliar person (i.e., 48.5% ± 5.0%). The proposed concept for emotion recognition systems may be useful for communicating with children with PIMD.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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