@inproceedings{50b2752184ea479897bb98c9583a04ba,
title = "Smart Wearable Analytics for Cycling: AI-Based Physical Exertion Prediction",
abstract = "We evaluated deep learning approaches for classification and regression prediction, focusing on an LSTM with Multi-Head Attention model. Data from 27 healthy participants performing cycling exercises were segmented into eight two-minute intervals. Heart rate, oxygen saturation, pedal speed (RPM), and HRV features (extracted from ECG in both frequency and time domains) served as predictive inputs. Rating of Perceived Exertion (RPE) was collected every minute and used as the predictive response, categorized into high and low exertion for classification. Physiological features and RPM from each segment were used to predict the next two-minute RPE. Feature selection via Minimum Redundancy Maximum Relevance (MRMR) and Univariate Feature Ranking (UFR) identified key predictors. The LSTM with Multi-Head Attention model achieved an MSE of 1.4 and R2 of 0.54 for regression and 82.9\% accuracy with an F1 score of 86.3\% for classification, demonstrating its effectiveness in exertion prediction.",
keywords = "Heart rate variability (HRV), deep learning, physical exertion pre-diction, wearable sensors",
author = "Aref Smiley and Joseph Finkelstein",
note = "Publisher Copyright: {\textcopyright} 2025 The Authors.; 23rd Annual International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2025 ; Conference date: 04-07-2025 Through 06-07-2025",
year = "2025",
month = jun,
day = "26",
doi = "10.3233/SHTI250714",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "256--260",
editor = "John Mantas and Arie Hasman and Parisis Gallos and Emmanouil Zoulias and Konstantinos Karitis",
booktitle = "Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics",
address = "Netherlands",
}