@inproceedings{2b44044643b24d32865f4cd6b5b6a3bc,
title = "Exploring Edge Machine Learning-based Stress Prediction using Wearable Devices",
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.",
keywords = "Classification Models, Edge Learning, Machine Learning, Neural Networks, Stress Prediction, Wearable Devices",
author = "Sim, {Sang Hun} and Tara Paranjpe and Nicole Roberts and Ming Zhao",
note = "Funding Information: We thank the anonymous reviewers for their feedback. This research is sponsored by the U.S. National Science Foundation awards OAC-2126291 and CNS-1955593, with support from Arizona State University{\textquoteright}s Institute for Social Sciences Research and School of Social and Behavioral Sciences. We also thank the Phoenix Regional Police Academy for their support and engagement. Publisher Copyright: {\textcopyright} 2022 IEEE.; 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 ; Conference date: 12-12-2022 Through 14-12-2022",
year = "2022",
doi = "10.1109/ICMLA55696.2022.00203",
language = "English (US)",
series = "Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1266--1273",
editor = "Wani, {M. Arif} and Mehmed Kantardzic and Vasile Palade and Daniel Neagu and Longzhi Yang and Kit-Yan Chan",
booktitle = "Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022",
}