{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:15:56Z","timestamp":1790111756501,"version":"4.0.1"},"reference-count":35,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T00:00:00Z","timestamp":1588636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 (\u226415 \u201cSomewhat hard to hard\u201d on Borg\u2019s 6\u201320 scale vs. RPE     &gt; 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.<\/jats:p>","DOI":"10.3390\/s20092637","type":"journal-article","created":{"date-parts":[[2020,5,7]],"date-time":"2020-05-07T03:10:38Z","timestamp":1588821038000},"page":"2637","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study"],"prefix":"10.3390","volume":"20","author":[{"given":"Padraig","family":"Davidson","sequence":"first","affiliation":[{"name":"Data Science, Institute for Computer Sciences, University of W\u00fcrzburg, 97074 W\u00fcrzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0277-9858","authenticated-orcid":false,"given":"Peter","family":"D\u00fcking","sequence":"additional","affiliation":[{"name":"Integrative and Experimental Exercise Science &amp; Training, Institute for Sport Sciences, University of W\u00fcrzburg, 97082 W\u00fcrzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5365-4847","authenticated-orcid":false,"given":"Christoph","family":"Zinner","sequence":"additional","affiliation":[{"name":"Department of Sport, University of Applied Sciences for Police and Administration of Hesse, 65199 Wiesbaden, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Billy","family":"Sperlich","sequence":"additional","affiliation":[{"name":"Integrative and Experimental Exercise Science &amp; Training, Institute for Sport Sciences, University of W\u00fcrzburg, 97082 W\u00fcrzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Hotho","sequence":"additional","affiliation":[{"name":"Data Science, Institute for Computer Sciences, University of W\u00fcrzburg, 97074 W\u00fcrzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"D\u00fcking, P., Achtzehn, S., Holmberg, H.C., and Sperlich, B. (2018). Integrated Framework of Load Monitoring by a Combination of Smartphone Applications, Wearables and Point-of-Care Testing Provides Feedback that Allows Individual Responsive Adjustments to Activities of Daily Living. Sensors, 18.","DOI":"10.3390\/s18051632"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s40279-014-0253-z","article-title":"Monitoring Training Load to Understand Fatigue in Athletes","volume":"44","author":"Halson","year":"2014","journal-title":"Sport. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1136\/bjsports-2015-094758","article-title":"Monitoring the athlete training response: Subjective self-reported measures trump commonly used objective measures: A systematic review","volume":"50","author":"Saw","year":"2016","journal-title":"Br. J. Sport. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1249\/01.MSS.0000128199.23901.2F","article-title":"Use of RPE-Based Training Load in Soccer","volume":"36","author":"Impellizzeri","year":"2004","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_5","unstructured":"Borg, G. (1998). Borg\u2019s Perceived Exertion and Pain Scales, Human Kinetics."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1123\/ijspp.7.2.175","article-title":"Use of ratings of perceived exertion in sports","volume":"7","author":"Eston","year":"2012","journal-title":"Int. J. Sport. Physiol. Perform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s00421-012-2421-x","article-title":"Associations between Borg\u2019s rating of perceived exertion and physiological measures of exercise intensity","volume":"113","author":"Scherr","year":"2013","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_8","first-page":"295","article-title":"The training intensity distribution among well-trained and elite endurance athletes","volume":"6","author":"Sperlich","year":"2015","journal-title":"Front. Physiol."},{"key":"ref_9","first-page":"33","article-title":"Polarized training has greater impact on key endurance variables than threshold, high intensity, or high volume training","volume":"5","author":"Sperlich","year":"2014","journal-title":"Front. Physiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1249\/MSS.0000000000001704","article-title":"Wrist-worn Accelerometry for Runners","volume":"50","author":"Stiles","year":"2018","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1186\/s40798-018-0172-x","article-title":"The Current State of Subjective Training Load Monitoring\u2014A Practical Perspective and Call to Action","volume":"4","author":"Coyne","year":"2018","journal-title":"Sport. Med. Open"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"778","DOI":"10.3389\/fphys.2018.00778","article-title":"Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise\u2014Basics and Options for Wearable Devices","volume":"9","author":"Ludwig","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_13","unstructured":"Sch\u00e4fer, P., and H\u00f6gqvist, M. (2012, January 27\u201330). SFA: A Symbolic Fourier Approximation and Index for Similarity Search in High Dimensional Datasets. Proceedings of the 15th International Conference on Extending Database Technology, Berlin, Germany."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer, P., and Leser, U. (2017). Multivariate Time Series Classification with WEASEL+MUSE. arXiv.","DOI":"10.1145\/3132847.3132980"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017, January 14\u201319). Time series classification from scratch with deep neural networks: A strong baseline. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_18","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_19","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust. Speech, Signal Process."},{"key":"ref_21","unstructured":"Cuturi, M. (July, January 28). Fast global alignment kernels. Proceedings of the 28th International Conference on Machine Learning (ICML-11), Bellevue, WA, USA."},{"key":"ref_22","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_24","first-page":"100","article-title":"Introduction to information retrieval","volume":"16","author":"Manning","year":"2010","journal-title":"Nat. Lang. Eng."},{"key":"ref_25","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity analysis of k-fold cross validation in prediction error estimation","volume":"32","author":"Rodriguez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","unstructured":"Chollet, F. (2018, April 29). Keras. Available online: https:\/\/keras.io."},{"key":"ref_28","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Eskofier, B., Hoenig, F., and Kuehner, P. (2008, January 8\u201311). Classification of perceived running fatigue in digital sports. Proceedings of the International Conference on Pattern Recognition ICPR, Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761204"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Eskofier, B., Kugler, P., Melzer, D., and Kuehner, P. (2012, January 9\u201312). Embedded classification of the perceived fatigue state of runners: Towards a body sensor network for assessing the fatigue state during running. Proceedings of the 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks (BSN), London, UK.","DOI":"10.1109\/BSN.2012.4"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"179","DOI":"10.2174\/1875399X01710010179","article-title":"Carisma 1.0: Cardiac risk self-monitoring assessment","volume":"10","author":"Agostinelli","year":"2017","journal-title":"Open Sport. Sci. J."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Buckley, C., O\u2019Reilly, M., Whelan, D., Farrell, A.V., Clark, L., Longo, V., Gilchrist, M., and Caulfield, B. (2017, January 9\u201312). Binary classification of running fatigue using a single inertial measurement unit. Proceedings of the 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Eindhoven, The Netherlands.","DOI":"10.1109\/BSN.2017.7936040"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1007\/s10439-013-0917-0","article-title":"Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors","volume":"42","author":"Zhang","year":"2014","journal-title":"Ann. Biomed. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2637\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:25:22Z","timestamp":1760361922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/9\/2637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,5]]},"references-count":35,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20092637"],"URL":"https:\/\/doi.org\/10.3390\/s20092637","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,5]]}}}