Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices

Sang Hun Sim, Tara Paranjpe, Nicole Roberts, Ming Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Stress is a central factor in our daily lives, impacting performance, decisions, well-being, and our interactions with others. With the development of IoT technology, smart wearable devices can handle diverse operations, including networking and recording biometric signals. The enhanced data processing capability of wearables has also allowed for increased stress awareness among users. Edge computing on such devices enables real-time feedback which can provide an opportunity to prevent severe consequences that might result if stress is left unaddressed. Edge computing can also strengthen privacy by implementing stress prediction on local devices without transferring personal information to the public cloud.This paper presents a framework for real-time stress prediction, specifically for police training cadets, using wearable devices and machine learning with support from cloud computing. We developed an application for Fitbit and the user's accompanying smartphone to collect heart rate fluctuations and corresponding stress levels entered by users and a cloud backend for storing data and training models. Real-world data for this study was collected from police cadets during a police academy training program. Machine learning classifiers for stress prediction were built using this data through classic machine learning models and neural networks. To analyze efficiency across different environments, the models were optimized using model compression and other relevant techniques and tested on cloud and edge environments. Evaluation using real data and real devices showed that the highest accuracy came from XGBoost and Tensorflow neural network models, and on-edge stress prediction models produced lower latency results than in-cloud prediction.

Original languageEnglish (US)
Title of host publicationProceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022
EditorsM. Arif Wani, Mehmed Kantardzic, Vasile Palade, Daniel Neagu, Longzhi Yang, Kit-Yan Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1266-1273
Number of pages8
ISBN (Electronic)9781665462839
DOIs
StatePublished - 2022
Event21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 - Nassau, Bahamas
Duration: Dec 12 2022Dec 14 2022

Publication series

NameProceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022

Conference

Conference21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022
Country/TerritoryBahamas
CityNassau
Period12/12/2212/14/22

Keywords

  • Classification Models
  • Edge Learning
  • Machine Learning
  • Neural Networks
  • Stress Prediction
  • Wearable Devices

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence
  • Hardware and Architecture

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