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Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study

Please always quote using this URN: urn:nbn:de:bvb:20-opus-205686
  • The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms.The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.show moreshow less

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Metadaten
Author: Padraig Davidson, Peter DükingORCiD, Christoph ZinnerORCiD, Billy Sperlich, Andreas Hotho
URN:urn:nbn:de:bvb:20-opus-205686
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Informatik
Fakultät für Humanwissenschaften (Philos., Psycho., Erziehungs- u. Gesell.-Wissensch.) / Institut für Sportwissenschaft
Language:English
Parent Title (English):Sensors
ISSN:1424-8220
Year of Completion:2020
Volume:20
Issue:9
Article Number:2637
Source:Sensors 2020, 20(9), 2637; https://doi.org/10.3390/s20092637
DOI:https://doi.org/10.3390/s20092637
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
7 Künste und Unterhaltung / 79 Sport, Spiele, Unterhaltung / 796 Sportarten, Sportspiele
Tag:artificial intelligence; endurance; exercise intensity; precision training; prediction; wearable
Release Date:2021/03/03
Date of first Publication:2020/05/05
Open-Access-Publikationsfonds / Förderzeitraum 2020
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International