Abstract
Time Series data collected from wearable sensors such as Inertial Measurement Units (IMU) are becoming popular for use in classification tasks in the exercise domain. The data from these IMU sensors tend to have multiple channels of data as well as the potential to augment new time series based features. However, this data also tends to have high correlations between the channels which means that often only a small subset of features are required for classification. A challenge in working with human movement data is that there tends to be inter-subject variabilities which makes it challenging to build a generalised model that works across subjects. In this work, the feasibility of generating generalisable feature subsets to predict fatigue in runners using a correlation based feature subset selection approach was investigated. It is shown that personalised classification systems where the feature selection is also tuned to the individual provides the best overall performance.
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References
Asegawa, H.I.H., Amauchi, T.A.Y., Raemer, W.I.J.K.: Foot strike patterns of runners at the 15-Km. Strength Conditioning 21(3), 888–893 (2007)
Bagnall, A., et al.: The UEA multivariate time series classification archive, pp. 1–36 (2018). http://arxiv.org/abs/1811.00075
Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606–660 (2016). https://doi.org/10.1007/s10618-016-0483-9
Buckley, C., et al.: Binary classification of running fatigue using a single inertial measurement unit. In: 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN, pp. 197–201. IEEE (2017)
Dempster, A., Petitjean, F., Webb, G.I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Disc. 34(5), 1454–1495 (2020). https://doi.org/10.1007/s10618-020-00701-z
Hall, M.: Correlation-based feature selection for machine learning. Ph.D. thesis, Department of Computer Science, University of Waikato Hamilton (1999)
Ircio, J., Lojo, A., Mori, U., Lozano, J.A.: Mutual information based feature subset selection in multivariate time series classification. Pattern Recogn. 108, 107525 (2020). https://doi.org/10.1016/j.patcog.2020.107525
Isabelle, G., Andre, E.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003). https://doi.org/10.1016/j.aca.2011.07.027
Kathirgamanathan, B., Cunningham, P.: Correlation based feature subset selection for multivariate time-series data. arXiv preprint arXiv:2112.03705 (2021)
Löning, M., Kazakov, V., Bagnall, A., Lines, J., Ganesh, S., Király, F.J.: Sktime: a unified interface for machine learning with time series. arXiv (2019)
Maas, E., De Bie, J., Vanfleteren, R., Hoogkamer, W., Vanwanseele, B.: Novice runners show greater changes in kinematics with fatigue compared with competitive runners. Sports Biomech. 17(3), 350–360 (2018)
Mizrahi, J., Verbitsky, O., Isakov, E., Daily, D.: Effect of fatigue on leg kinematics and impact acceleration in long distance running. Hum. Mov. Sci. 19(2), 139–151 (2000)
O’Reilly, M.A., Johnston, W., Buckley, C., Whelan, D., Caulfield, B.: The influence of feature selection methods on exercise classification with inertial measurement units. In: 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2017, pp. 193–196 (2017). https://doi.org/10.1109/BSN.2017.7936039
Pasos, A., Michael, R., James, F., Middlehurst, M., Bagnall, A.: The great multivariate time series classification bake off : a advances. Springer, US (2020).https://doi.org/10.1007/s10618-020-00727-3
Tavenard, R., et al.: Tslearn, a machine learning toolkit for time series data. J. Mach. Learn. Res. 21(118), 1–6 (2020). http://jmlr.org/papers/v21/20-091.html
Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010)
Whelan, D.F., O’Reilly, M.A., Ward, T.E., Delahunt, E., Caulfield, B.: Technology in rehabilitation: comparing personalised and global classification methodologies in evaluating the squat exercise with wearable IMUs. Methods Inf. Med. 56(5), 361–369 (2017). https://doi.org/10.3414/ME16-01-0141
Yamato, T.P., Saragiotto, B.T., Lopes, A.D.: A consensus definition of running-related injury in recreational runners: a modified Delphi approach. J. Orthop. Sports Phys. Ther. 45(5), 375–380 (2015). https://doi.org/10.2519/jospt.2015.5741
Yang, K., Yoon, H., Shahabi, C.: CLeVer: a Feature Subset Selection Technique for Multivariate Time Series (Full Version). Tech. rep. (2005)
Yoon, H., Shahabi, C.: Feature subset selection on multivariate time series with extremely large spatial features. In: Proceedings - IEEE International Conference on Data Mining, ICDM 0238560, pp. 337–342 (2006). https://doi.org/10.1109/icdmw.2006.81
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This work has emanated from research conducted with the financial support of Science Foundation Ireland under the Grant number 18/CRT/6183. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.
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Kathirgamanathan, B., Buckley, C., Caulfield, B., Cunningham, P. (2022). Feature Subset Selection for Detecting Fatigue in Runners Using Time Series Sensor Data. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_44
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