Paper Info
Title
Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata
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 data sets, 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.
Year
DOI
Venue
2022
10.1186/s40708-022-00162-8
Brain Informatics
Keywords
DocType
Volume
Brain-inspired, Hyperdimensional computing, Emotion recognition, Wearable, Memory optimization, Hardware efficient, Multi-modal sensor fusion
Journal
9
Issue
ISSN
Citations 
1
2198-4018
1
PageRank 
References 
Authors
0.36
10
8
Name
Order
Citations
PageRank
Alisha Menon1112.23
Anirudh Natarajan210.70
Reva Agashe310.36
Daniel Sun410.36
Melvin Aristio510.36
Harrison Liew681.84
Yakun Sophia Shao723514.70
Jan M. Rabaey847961049.96