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Adaptive Feeding Robot With Multisensor Feedback and Predictive Control Using Autoregressive Integrated Moving

Shabnam Sadeghi-Esfahlani1, Vahaj Mohaghegh1, Alireza Sanaei1

  • 1Faculty of Science & Engineering, Anglia Ruskin University, Bishop Hall Lane, Chelmsford, CM1 1SQ, United Kingdom, 44 07944281517.

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Summary

This adaptive feeding robot uses advanced algorithms like ARIMA and FFNN to improve eating precision and efficiency for individuals with motor impairments, significantly enhancing their independence and quality of life.

Keywords:
ARIMAFFNNassistive technologyautoregressive integrated moving averagefeed-forward neural networkfeeding roboticsforecastingmotor impairmentpersonalized assistancetime series analysis

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Eating is vital for independence, but neuromuscular impairments limit device capabilities.
  • Current assistive feeding devices are passive and lack adaptive features.

Purpose of the Study:

  • Introduce an adaptive feeding robot integrating time series decomposition, autoregressive integrated moving average (ARIMA), and feed-forward neural networks (FFNN).
  • Enhance feeding precision, efficiency, and personalization for individuals with motor impairments to promote autonomy.

Main Methods:

  • The robot integrates sensors (strain gauge, ultrasonic) and actuators with real-time data (facial landmarks, mouth status, distances, force, angle).
  • ARIMA and FFNN algorithms predict user behavior and dynamically adjust feeding actions.
  • Facial recognition ensures safety by monitoring mouth conditions and plate contents.

Main Results:

  • The combined ARIMA+FFNN model achieved high accuracy (MSE=0.008, R2=94%), outperforming standalone models.
  • Feeding success rate improved to 90% over 150 iterations, with a 28% reduction in response time.
  • Object detection accuracy was high (face detection precision=97%, recall=96%), and force application was precise.

Conclusions:

  • The adaptive feeding robot demonstrates significant improvements in precision, responsiveness, and personalization.
  • This technology has the potential to revolutionize assistive devices for individuals with motor impairments.
  • The robot enhances independence by providing safe and personalized feeding assistance.