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Updated: Sep 28, 2025

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Counseling (ro)bot as a use case for 5G/6G.

Yoshio Taniguchi1, Yukino Ikegami2, Hiroshi Fujikawa3

  • 1Hitachi Industry & Control Solutions Ltd, Tokyo, Japan.

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Summary
This summary is machine-generated.

This study introduces an AI-powered Visual Counseling Agent (VICA) for remote mental healthcare. Leveraging 5G/6G networks and edge computing significantly improves VICA

Keywords:
5G networkArtificial intelligenceHuman–computer interaction (HCI)Natural language processing

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Area of Science:

  • Mental Healthcare Technology
  • Artificial Intelligence in Psychology
  • Telecommunications

Background:

  • Remote mental healthcare delivery faces challenges with current network limitations.
  • Conventional 4G networks hinder the performance of AI-driven counseling agents like VICA, causing issues like word dropping and connection failures.
  • Existing AI counseling systems require enhanced network infrastructure for reliable and high-quality remote support.

Purpose of the Study:

  • To enhance the Visual Counseling Agent (VICA) for improved remote mental healthcare.
  • To mitigate performance issues of VICA on 4G networks by utilizing 5G/6G network slicing and mobile/multiple edge computing (MEC).
  • To improve speech recognition reliability and enable facial expression analysis for superior counseling quality.

Main Methods:

  • Proposed and implemented an enhanced and advanced version of the VICA system.
  • Utilized 5G/6G network slicing inclusive of MEC to process data closer to the user.
  • Developed a multi-level catalog quality assurance mechanism for VICA.
  • Conducted experiments comparing VICA performance on 4G networks versus 5G/6G with MEC.

Main Results:

  • Speech recognition errors were more than twofold higher in Internet Cloud compared to edge computing.
  • The advanced VICA version, with facial expression recognition, significantly enhances counseling quality.
  • 5G/6G network slicing with MEC demonstrated higher efficiency in quality assurance for the VICA counseling (ro)bot.
  • Edge computing reduced speech recognition errors compared to traditional cloud-based processing.

Conclusions:

  • 5G/6G network slicing combined with MEC effectively addresses the limitations of 4G for AI-driven remote mental healthcare.
  • The enhanced VICA system offers a more reliable and higher-quality remote counseling experience.
  • Integrating advanced features like facial expression recognition with robust network infrastructure is crucial for future tele-mental health solutions.