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An Acoustic Sensing Gesture Recognition System Design Based on a Hidden Markov Model.

Bruna Salles Moreira1, Angelo Perkusich1, Saulo O D Luiz1

  • 1Embedded Systems and Pervasive Computing Laboratory, Electrical Engineering Department, Federal University of Campina Grande, Campina Grande, Paraíba 58429-900, Brazil.

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Summary

Hidden Markov Models (HMM) show superior performance in acoustic-based gesture recognition compared to Artificial Neural Networks (ANN). HMM achieved 90% accuracy with faster training for this tactile interaction system.

Keywords:
Hidden Markov modelsacoustic-based inputartificial neural networkgesture recognition

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Area of Science:

  • Human-computer interaction
  • Machine learning
  • Signal processing

Background:

  • Human activities heavily rely on tactile interactions with surrounding objects and surfaces.
  • Recognizing touch-based gestures is crucial for developing interactive surface technologies.
  • Acoustic-based input classification is an active research area due to its low computational cost.

Purpose of the Study:

  • To compare the effectiveness of Artificial Neural Network (ANN) and Hidden Markov Models (HMM) for acoustic-based gesture recognition.
  • To evaluate a low-cost hardware system for passive gesture recognition.
  • To assess the performance of ANN and HMM using a simple alphabet of geometric shapes.

Main Methods:

  • Developed a low-cost hardware system with a microphone, stethoscope, conditioning circuit, and microcontroller.
  • Integrated the hardware with a surface to create a passive gesture recognition input system.
  • Acquired acoustic signals, processed them via a microcontroller, and evaluated ANN and HMM models using MATLAB toolboxes.

Main Results:

  • The Hidden Markov Model (HMM) technique demonstrated robustness in gesture recognition.
  • HMM achieved a 90% success rate in classifying gestures (circle, square, triangle).
  • HMM exhibited a shorter training time compared to the Artificial Neural Network (ANN).

Conclusions:

  • Hidden Markov Models are a robust and efficient technique for acoustic-based gesture recognition.
  • The developed low-cost system is effective for evaluating gesture recognition algorithms.
  • Further research can explore the advantages and limitations of ANN and HMM in more complex gesture recognition tasks.