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

TL;DR

This paper introduces a hardware-efficient hyperdimensional computing approach for emotion recognition that significantly reduces memory usage and improves accuracy across multi-modal datasets.

Contribution

It presents a novel combinatorial encoding method and cellular automaton integration to optimize hyperdimensional computing for emotion recognition.

Findings

Achieved >76% accuracy for valence and >73% for arousal on AMIGOS and DEAP datasets.

Reduced vector storage by 98% and vector request frequency by at least 80%.

Outperformed most existing methods in multi-modal emotion recognition accuracy.

Abstract

In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human-computer interactions, however the large number of input channels (>200) and modalities (>3) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of >76% for valence and >73% for arousal on the multi-modal AMIGOS and DEAP datasets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks.