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Cloud-Based Personalized sEMG Classification Using Lightweight CNNs for Long-Term Haptic Communication in Deaf-Blind

Kaavya Tatavarty1, Maxwell Johnson1, Boris Rubinsky1

  • 1Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA.

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|November 27, 2025
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Summary
This summary is machine-generated.

This study introduces an AI-powered haptic communication system for individuals with Usher syndrome, offering adaptable, non-visual interaction. The novel wearable sleeve technology enhances communication and independence for the deaf-blind community.

Keywords:
Usher syndromearm sleevecommunicationsurface electromyography

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Area of Science:

  • Assistive Technology
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Deaf-blindness, especially Usher syndrome, creates significant communication and independence barriers.
  • Current tactile communication methods often require close proximity and hand-to-hand contact, limiting usability.
  • Progressive vision and hearing loss in Usher syndrome necessitate adaptable communication solutions.

Purpose of the Study:

  • To develop and validate a novel, cloud-based, AI-assisted gesture recognition and haptic communication system.
  • To create a wearable haptic interface that moves tactile interaction from hands to an arm sleeve, preserving dexterity.
  • To design a system capable of longitudinal adaptation to users' changing physiological conditions.

Main Methods:

  • Utilized surface electromyography (sEMG) to capture muscle activations for gesture recognition.
  • Employed personalized, lightweight convolutional neural networks (CNNs) for real-time gesture classification on a central server.
  • Developed a wearable haptic interface mounted on an arm sleeve for bidirectional tactile communication.

Main Results:

  • Personalized CNN models demonstrated superior accuracy, adaptability, and usability compared to cross-user models.
  • The system successfully adapted to users' evolving conditions, including progressive sensory loss.
  • Real-time testing with seven participants validated the system's effectiveness and potential for long-term use.

Conclusions:

  • The developed platform offers a scalable and longitudinally adaptable solution for non-visual communication.
  • This AI-assisted haptic system significantly advances assistive technology for the deaf-blind community.
  • The relocation of tactile input/output to an arm sleeve preserves manual dexterity and enables continuous interaction.