skip to main content
10.1145/3219819.3219864acmotherconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion

Published: 19 July 2018 Publication History

Abstract

Running is extremely popular and around 10.6 million people run regularly in the United States alone. Unfortunately, estimates indicated that between 29% to 79% of runners sustain an overuse injury every year. One contributing factor to such injuries is excessive fatigue, which can result in alterations in how someone runs that increase the risk for an overuse injury. Thus being able to detect during a running session when excessive fatigue sets in, and hence when these alterations are prone to arise, could be of great practical importance. In this paper, we explore whether we can use machine learning to predict the rating of perceived exertion (RPE), a validated subjective measure of fatigue, from inertial sensor data of individuals running outdoors. We describe how both the subjective target label and the realistic outdoor running environment introduce several interesting data science challenges. We collected a longitudinal dataset of runners, and demonstrate that machine learning can be used to learn accurate models for predicting RPE.

Supplementary Material

suppl.mov (a0729p.mp4)
Supplemental video

References

[1]
M. T. Ballas, J. Tytko, and D. Cookson. 1997. Common overuse running injuries: diagnosis and management. American family physician Vol. 55, 7 (1997), 2473--2484.
[2]
G. Borg. 1998. Borg's perceived exertion and pain scales. Human Kinetics. 1--97 pages.
[3]
G. A. Borg. 1982. Psychophysical bases of perceived exertion. Med Sci Sports Exerc, Vol. 14, 5 (1982), 377--381.
[4]
L. J. Boyd, K. Ball, and R. J. Aughey. 2011. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. International Journal of Sports Physiology and Performance, Vol. 6, 3 (2011), 311--321.
[5]
R. M. Bryce and K. B. Sprague. 2012. Revisiting detrended fluctuation analysis. Scientific reports Vol. 2 (2012), 315.
[6]
H. Crewe, R. Tucker, and T. D. Noakes. 2008. The rate of increase in rating of perceived exertion predicts the duration of exercise to fatigue at a fixed power output in different environmental conditions. European Journal of Applied Physiology Vol. 103, 5 (2008), 569--577.
[7]
J. Dong. 2016. The role of heart rate variability in sports physiology. Experimental and Therapeutic Medicine Vol. 11, 5 (2016), 1531--1536.
[8]
J. H. Friedman. 2002. Stochastic gradient boosting. Computational Statistics &Data Analysis Vol. 38, 4 (2002), 367--378.
[9]
D. Gafurov, K. Helkala, and T. Søndrol. 2006. Biometric Gait Authentication Using Accelerometer Sensor. JCP, Vol. 1, 7 (2006), 51--59.
[10]
T. K. Ho. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, 8 (1998), 832--844.
[11]
S. L. Hooper and L. T. Mackinnon. 1995. Monitoring Overtraining in Athletes Recommendations. Sports Medicine, Vol. 20, 5 (1995), 321--322.
[12]
A. Hreljac, R. N. Marshall, and P. A. Hume. 2000. Evaluation of lower extremity overuse injury potential in runners. Medicine &Science in Sports &Exercise Vol. 32, 9 (2000), 1635--1641.
[13]
A. Jaspers, T. Op De Beéck, M. S. Brink, W. G. P. Frencken, F. Staes, J. J. Davis, and W. F. Helsen. 2017. Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning? International journal of sports physiology and performance (2017), 1--18.
[14]
K. Jordan, J. H. Challis, and K. M. Newell. 2007. Speed influences on the scaling behavior of gait cycle fluctuations during treadmill running. Human Movement Science Vol. 26, 1 (2007), 87--102.
[15]
Y. Kobayashi, T. Takeuchi, T. Hosoi, H. Yoshizaki, and J. A. Loeppky. 2005. Effect of a marathon run on serum lipoproteins, creatine kinase, and lactate dehydrogenase in recreational runners. Research Quarterly for Exercise and Sport Vol. 76, 4 (2005), 450--455.
[16]
Y. Koren. 2010. Collaborative filtering with temporal dynamics. Commun. ACM Vol. 53, 4 (2010), 89--97.
[17]
J. R. Kwapisz, G. M. Weiss, and S. A. Moore. 2011. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter Vol. 12, 2 (2011), 74--82.
[18]
M. L. Bertelsen, A. Hulme, J. Petersen, R. Korsgaard Brund, H. Sørensen, C. F. Finch, E. T. Parner, and R. O. Nielsen. 2017. A framework for the etiology of running-related injuries. Scandinavian Journal of Medicine &Science in Sports (2017), 1170--1180.
[19]
S. Madgwick. 2010. An efficient orientation filter for inertial and inertial/magnetic sensor arrays. Report x-io and University of Bristol (UK) Vol. 25 (2010), 1--32.
[20]
S. P. Messier, C. Legault, C. R. Schoenlank, J. J. Newman, D. F. Martin, and P. Devita. 2008. Risk factors and mechanisms of knee injury in runners. Medicine &Science in Sports &Exercise Vol. 40, 11 (2008), 1873--1879.
[21]
R. Moe-Nilssen and J. L. Helbostad. 2004. Estimation of gait cycle characteristics by trunk accelerometry. Journal of Biomechanics Vol. 37, 1 (2004), 121--126.
[22]
J. B. Morin, P. Samozino, and G. Y. Millet. 2011. Changes in running kinematics, kinetics, and spring-mass behavior over a 24-h run. Medicine and Science in Sports and Exercise, Vol. 43, 5 (may. 2011), 829--36. http://www.ncbi.nlm.nih.gov/ /20962690
[23]
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research Vol. 12 (2011), 2825--2830.
[24]
J. S. Richman and J. R. Moorman. 2000. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, Vol. 278, 6 (2000), 2039--2049.
[25]
K. H. Schütte, E. A. Maas, V. Exadaktylos, D. Berckmans, R. E. Venter, and B. Vanwanseele. 2015. Wireless tri-axial trunk accelerometry detects deviations in dynamic center of mass motion due to running-induced fatigue. PloS One, Vol. 10, 10 (2015), e0141957.
[26]
K. H. Schütte, S. Seerden, R. Venter, and B. Vanwanseele. 2016. Fatigue-related asymmetry and instability during a 3200-m time-trial performance in healthy runners. In ISBS-Conference Proceedings Archive, Vol. 34. 933--936.
[27]
A. Soriano-Maldonado, L. Romero, P. Femia, C. Roero, J. R. Ruiz, and A. Gutierrez. 2014. A learning protocol improves the validity of the Borg 6-20 RPE scale during indoor cycling. International Journal of Sports Medicine Vol. 35, 05 (2014), 379--384.
[28]
N. M. Stoudemire, L. Wideman, K. A. Pass, C. L. Mcginnes, G. A. Gaesser, and A. Weltman. 1996. The validity of regulating blood lactate concentration during running by ratings of perceived exertion. Medicine and Science in Sports and Exercise, Vol. 28, 4 (1996), 490--495.
[29]
J. E. Taunton, M. B. Ryan, D. B. Clement, D. C. McKenzie, D. R. Lloyd-Smith, and B. D. Zumbo. 2002. A retrospective case-control analysis of 2002 running injuries. British journal of sports medicine Vol. 36, 2 (2002), 95--101.
[30]
Y. Tochigi, N. A. Segal, T. Vaseenon, and T. D. Brown. 2012. Entropy analysis of tri-axial leg acceleration signal waveforms for measurement of decrease of physiological variability in human gait. Journal of Orthopaedic Research Vol. 30, 6 (2012), 897--904.
[31]
B. R. N. van Gent, D. D. Siem, M. van Middelkoop, T. A. G. van Os, Sita S. M. A. Bierma-Zeinstra, and B. B. W. Koes. 2007. Incidence and determinants of lower extremity running injuries in long distance runners: a systematic review. British journal of sports medicine (2007), 469--480.
[32]
C. Wetherell. 1986. The Log Percent (L%): An Absolute Measure of Relative Change. Historical Methods: A Journal of Quantitative and Interdisciplinary History, Vol. 19, 1 (1986), 25--26.
[33]
T. J. Williams, G. S. Krahenbuhl, and D. W. Morgan. 1991. Mood state and running economy in moderately trained male runners. Medicine &Science in Sports &Exercise, Vol. 23, 6 (1991), 727--31.

Cited By

View all
  • (2024)Predicting vertical ground reaction force characteristics during running with machine learningFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2024.144003312Online publication date: 8-Oct-2024
  • (2024)Internet of things and ensemble learning-based mental and physical fatigue monitoring for smart construction sitesJournal of Big Data10.1186/s40537-024-00978-711:1Online publication date: 16-Aug-2024
  • (2024)Real-Time Injury Risk Assessment in Athletes Based on Relative Joint Angles2024 International Conference on Emerging Smart Computing and Informatics (ESCI)10.1109/ESCI59607.2024.10497417(1-6)Online publication date: 5-Mar-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
July 2018
2925 pages
ISBN:9781450355520
DOI:10.1145/3219819
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. machine learning
  2. sensor fusion
  3. sports analytics

Qualifiers

  • Research-article

Funding Sources

Conference

KDD '18
Sponsor:

Acceptance Rates

KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)136
  • Downloads (Last 6 weeks)17
Reflects downloads up to 03 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Predicting vertical ground reaction force characteristics during running with machine learningFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2024.144003312Online publication date: 8-Oct-2024
  • (2024)Internet of things and ensemble learning-based mental and physical fatigue monitoring for smart construction sitesJournal of Big Data10.1186/s40537-024-00978-711:1Online publication date: 16-Aug-2024
  • (2024)Real-Time Injury Risk Assessment in Athletes Based on Relative Joint Angles2024 International Conference on Emerging Smart Computing and Informatics (ESCI)10.1109/ESCI59607.2024.10497417(1-6)Online publication date: 5-Mar-2024
  • (2024)A computer vision approach to continuously monitor fatigue during resistance trainingBiomedical Signal Processing and Control10.1016/j.bspc.2023.10570189(105701)Online publication date: Mar-2024
  • (2024)Methodology and evaluation in sports analytics: challenges, approaches, and lessons learnedMachine Learning10.1007/s10994-024-06585-0Online publication date: 17-Jul-2024
  • (2023)Small Data, Big Challenges: Pitfalls and Strategies for Machine Learning in Fatigue DetectionProceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3594806.3594825(364-373)Online publication date: 5-Jul-2023
  • (2023)Perspective on “in the wild” movement analysis using machine learningHuman Movement Science10.1016/j.humov.2022.10304287(103042)Online publication date: Feb-2023
  • (2023)Prediction of instantaneous perceived effort during outdoor running using accelerometry and machine learningEuropean Journal of Applied Physiology10.1007/s00421-023-05322-0Online publication date: 29-Sep-2023
  • (2023)A Knee Injury Prevention System by Continuous Knee Angle Recognition Using Stretch SensorsAdvances in Mobile Computing and Multimedia Intelligence10.1007/978-3-031-48348-6_8(93-103)Online publication date: 22-Nov-2023
  • (2023)DataDebugging: Enhancing Trust in Soccer Action-Value Models by Contextualization13th World Congress of Performance Analysis of Sport and 13th International Symposium on Computer Science in Sport10.1007/978-3-031-31772-9_40(193-196)Online publication date: 13-Jul-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media