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Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular

Alisha Menon1, Anirudh Natarajan2, Reva Agashe2

  • 1Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA. allymenon@berkeley.edu.

Brain Informatics
|June 27, 2022
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Summary
This summary is machine-generated.

This study introduces a hardware-optimized approach using hyperdimensional computing (HDC) for efficient emotion recognition. The method significantly reduces memory usage and improves accuracy for human-computer interactions.

Keywords:
Brain-inspiredEmotion recognitionHardware efficientHyperdimensional computingMemory optimizationMulti-modal sensor fusionWearable

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Area of Science:

  • Computer Science
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Emotion recognition is crucial for human-computer interaction.
  • High input channel and modality counts create memory challenges.
  • Existing methods are memory-intensive.

Purpose of the Study:

  • To propose a hardware-optimized approach for emotion recognition.
  • To address memory constraints in multi-modal emotion recognition.
  • To enhance the efficiency of brain-inspired hyperdimensional computing (HDC).

Main Methods:

  • Implemented a hardware-optimized hyperdimensional computing (HDC) paradigm.
  • Utilized memory reduction techniques, including combinatorial encoding and cellular automata.
  • Employed early sensor fusion with HDC.

Main Results:

  • Achieved >76% accuracy for valence and >73% for arousal on AMIGOS and DEAP datasets.
  • Reduced vector storage by 98% and vector requests by at least 1/5.
  • Outperformed state-of-the-art methods in multi-modal classification.

Conclusions:

  • Efficient hyperdimensional computing is suitable for low-power, multi-channeled emotion recognition.
  • The proposed methods significantly optimize memory usage.
  • This approach enhances the feasibility of real-time emotion recognition systems.