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Related Experiment Video

Updated: Oct 30, 2025

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Emotion Recognition on Edge Devices: Training and Deployment.

Vlad Pandelea1, Edoardo Ragusa2, Tommaso Apicella2

  • 1School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

Large transformer models combined with linear classifiers offer efficient emotion recognition on edge devices. This approach achieves competitive performance with real-time inference and fast training, overcoming computational cost challenges.

Keywords:
deep learningembedded systemsemotion recognition

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computer Vision

Background:

  • Large transformer models excel in natural language processing (NLP) tasks like emotion recognition.
  • Deploying these computationally intensive models on resource-constrained edge devices presents a significant challenge.

Purpose of the Study:

  • To investigate efficient deployment of large transformer models for emotion recognition on edge devices.
  • To achieve competitive performance with real-time inference and fast training.

Main Methods:

  • Utilizing large transformers as feature extractors combined with hardware-friendly linear classifiers.
  • Analyzing batch and Online Sequential Learning methods.
  • Applying dimensionality reduction and pre-training techniques.

Main Results:

  • The combined approach achieves competitive performance for emotion recognition.
  • Real-time inference and fast training are enabled on edge devices.
  • Dimensionality reduction and pre-training further improve latency and performance.

Conclusions:

  • Large transformers and linear classifiers offer an effective solution for efficient emotion recognition on edge devices.
  • The proposed system demonstrates practical implementation on edge accelerators and smartphones.
  • This method balances high performance with the computational constraints of edge computing.