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Emotion recognition models for companion robots

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Abstract

There has been a steep increase in the use of machine learning for various healthcare applications like the diagnosis of diseases, drug discovery, medical image analysis, etc. Machine learning solutions are proven to be more efficient and less time-consuming than conventional approaches. In this paper, we leverage the advantages of machine learning models to enable a humanoid robot to assist mental health patients. Facial expression and human voice are some of the most demonstrative ways to analyze human emotions, especially for the mentally challenged. We carry out this assistance by constantly monitoring the patient’s voice (or audio) and facial expressions to predict human emotions. To implement the model of audio monitoring, we train three different machine learning and deep learning models to compare and choose the better model. Similarly, for facial recognition, we train a deep learning model using a specific dataset to predict facial expressions from the video captured in real time. We then integrate the better-performing machine learning models into a web interface for demonstration.

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Authors and Affiliations

  1. International Institute of Information Technology, Bhubaneswar, Odisha, India

    Ritvik Nimmagadda

  2. Dayalbagh Educational Institute, Agra, Uttar Pradesh, India

    Kritika Arora

  3. Ontario Tech University, Oshawa, ON, Canada

    Miguel Vargas Martin

Authors
  1. Ritvik Nimmagadda
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  2. Kritika Arora
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  3. Miguel Vargas Martin
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Correspondence to Ritvik Nimmagadda.

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Ritvik Nimmagadda and Kritika Arora these authors Worked on this project as research interns at the Ontario Tech University, supported by MITACS Globalink Research Internship.

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Nimmagadda, R., Arora, K. & Martin, M.V. Emotion recognition models for companion robots. J Supercomput 78, 13710–13727 (2022). https://doi.org/10.1007/s11227-022-04416-4

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  • DOI: https://doi.org/10.1007/s11227-022-04416-4

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