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This study presents an inclusive, voice-controlled smart wheelchair with improved speech recognition and navigation for enhanced autonomy. The system ensures safe and reliable mobility for individuals with motor impairments in dynamic environments.

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Area of Science:

  • Robotics
  • Assistive Technology
  • Human-Computer Interaction

Background:

  • Existing smart wheelchairs lack robust speech recognition and safety features for dynamic environments.
  • Severe motor impairments limit personal autonomy, highlighting the need for advanced assistive mobility solutions.

Purpose of the Study:

  • To develop and validate an integrated, voice-controlled smart wheelchair system.
  • To enhance autonomy for individuals with severe motor impairments through improved assistive mobility.
  • To address limitations in speech robustness, environmental handling, and safety mechanisms in current systems.

Main Methods:

  • Developed an inclusive speech-recognition module using a fine-tuned deep learning model trained on diverse speech data.
  • Integrated 2D LiDAR-based SLAM (GMapping), AMCL localization, and a dual-level voice-command interface within a real-time coordination layer.
  • Implemented a safety module with adaptive speed modulation and calibrated emergency-stop thresholds for dynamic environments.

Main Results:

  • Achieved a 6.7% Word Error Rate in realistic noise conditions with the inclusive speech module.
  • Demonstrated a mean localization error below 10 cm and a 94% goal-completion rate in extensive real-world tests.
  • Reported an end-to-end voice-to-motion latency of 0.8 seconds for seamless control.

Conclusions:

  • The presented smart wheelchair offers a low-cost, experimentally validated platform for enhanced assistive mobility.
  • The system provides inclusive voice interaction, robust navigation, and safety-aware behavior for everyday indoor use.
  • This integrated framework offers a deployable and reproducible solution, advancing beyond component-level studies.