{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:10:04Z","timestamp":1778602204572,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T00:00:00Z","timestamp":1579219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"LabCom 'CWD-Vetlab'","award":["contract ANR 16-LCV2-0002-01"],"award-info":[{"award-number":["contract ANR 16-LCV2-0002-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the emergence of numerical sensors in sports, there is an increasing need for tools and methods to compute objective motion parameters with great accuracy. In particular, inertial measurement units are increasingly used in the clinical domain or the sports one to estimate spatiotemporal parameters. The purpose of the present study was to develop a model that can be included in a smart device in order to estimate the horse speed per stride from accelerometric and gyroscopic data without the use of a global positioning system, enabling the use of such a tool in both indoor and outdoor conditions. The accuracy of two speed calculation methods was compared: one signal based and one machine learning model. Those two methods allowed the calculation of speed from accelerometric and gyroscopic data without any other external input. For this purpose, data were collected under various speeds on straight lines and curved paths. Two reference systems were used to measure the speed in order to have a reference speed value to compare each tested model and estimate their accuracy. Those models were compared according to three different criteria: the percentage of error above 0.6 m\/s, the RMSE, and the Bland and Altman limit of agreement. The machine learning method outperformed its competitor by giving the lowest value for all three criteria. The main contribution of this work is that it is the first method that gives an accurate speed per stride for horses without being coupled with a global positioning system or a magnetometer. No similar study performed on horses exists to compare our work with, so the presented model is compared to existing models for human walking. Moreover, this tool can be extended to other equestrian sports, as well as bipedal locomotion as long as consistent data are provided to train the machine learning model. The machine learning model\u2019s accurate results can be explained by the large database built to train the model and the innovative way of slicing stride data before using them as an input for the model.<\/jats:p>","DOI":"10.3390\/s20020518","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T07:39:02Z","timestamp":1579246742000},"page":"518","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Method to Estimate Horse Speed per Stride from One IMU with a Machine Learning Method"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2523-0411","authenticated-orcid":false,"given":"Amandine","family":"Schmutz","sequence":"first","affiliation":[{"name":"Lim France, Chemin Fontaine de Fanny, 24300 Nontron, France"},{"name":"CWD-Vetlab, Ecole Nationale V\u00e9t\u00e9rinaire d\u2019Alfort, F-94700 Maisons-Alfort, France"},{"name":"LBMC UMR T9406, Universit\u00e9 de Lyon, Lyon 1, 69364 Lyon, France"},{"name":"ERIC EA3083, Universit\u00e9 de Lyon, Lyon 2, 69007 Lyon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2265-9781","authenticated-orcid":false,"given":"Laurence","family":"Ch\u00e8ze","sequence":"additional","affiliation":[{"name":"LBMC UMR T9406, Universit\u00e9 de Lyon, Lyon 1, 69364 Lyon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4808-2781","authenticated-orcid":false,"given":"Julien","family":"Jacques","sequence":"additional","affiliation":[{"name":"ERIC EA3083, Universit\u00e9 de Lyon, Lyon 2, 69007 Lyon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3571-4244","authenticated-orcid":false,"given":"Pauline","family":"Martin","sequence":"additional","affiliation":[{"name":"Lim France, Chemin Fontaine de Fanny, 24300 Nontron, France"},{"name":"CWD-Vetlab, Ecole Nationale V\u00e9t\u00e9rinaire d\u2019Alfort, F-94700 Maisons-Alfort, France"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1242\/jeb.01658","article-title":"A method for deriving displacement data during cyclical movement using an inertial sensor","volume":"208","author":"Pfau","year":"2005","journal-title":"J. Exp. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1080\/17461391.2018.1463397","article-title":"Accuracy of human motion capture systems for sport applications; state-of-the-art review","volume":"18","author":"Reijine","year":"2018","journal-title":"Eur. J. Sport Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.tvjl.2016.12.018","article-title":"Effects of the rider on the kinematics of the equine spine under the saddle during the trot using inertial measurement units: Methodological study and preliminary results","volume":"221","author":"Martin","year":"2017","journal-title":"Vet. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1111\/eve.12400","article-title":"Agreement between two inertial sensor gait analysis systems for lameness examinations in horses","volume":"28","author":"Pfau","year":"2016","journal-title":"Equine Vet. Educ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Filippeschi, A., Schmitz, N., Miezal, M., Bleser, G., Ruffaldi, E., and Stricker, D. (2017). Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion. Sensors, 17.","DOI":"10.3390\/s17061257"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1016\/j.jbiomech.2008.02.021","article-title":"Measurement of stride parameters using a wearable GPS and inertial measurement unit","volume":"41","author":"Tan","year":"2008","journal-title":"J. Biomech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.measurement.2015.05.023","article-title":"A cascaded Kalman filter based GPS\/MEMS-IMU integration for sports applications","volume":"73","author":"Zihajehzadeh","year":"2015","journal-title":"Measurement"},{"key":"ref_8","first-page":"169","article-title":"Consumer-Grade Global Positioning System (GPS) Accuracy and Reliability","volume":"103","author":"Wing","year":"2005","journal-title":"J. For."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Duong, H.T., and Suh, Y.S. (2017, January 19\u201322). Walking distance estimation of a walker user using a wrist-mounted IMU. Proceedings of the 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Kanazawa, Japan.","DOI":"10.23919\/SICE.2017.8105462"},{"key":"ref_10","unstructured":"Murphy, J., Carr, H., and O\u2019Neill, M. (2010). Animating horse gaits and transitions. Symposium on Theory and Practice of Computer Graphics, The Eurographics Association."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.bspc.2018.01.002","article-title":"Novel approach to human walking speed enhancement based on drift estimation","volume":"42","author":"Brzostowski","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bosch, S., Serra Bragan\u00e7a, F., Marin-Perianu, M., Marin-Perianu, R., Van der Zwaag, B.J., Voskamp, J., Back, W., Van Weeren, R., and Havinga, P. (2018). EquiMoves: A Wireless Networked Inertial Measurement System for Objective Examination of Horse Gait. Sensors, 18.","DOI":"10.3390\/s18030850"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sabatini, A.M., and Mannini, A. (2016). Ambulatory Assessment of Instantaneous Velocity during Walking Using Inertial Sensor Measurements. Sensors, 16.","DOI":"10.3390\/s16122206"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zihajehzadeh, S., and Park, E.J. (2017, January 11\u201315). A Gaussian process regression model for walking speed estimation using a head-worn IMU. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea.","DOI":"10.1109\/EMBC.2017.8037326"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zihajehzadeh, S., Aziz, O., Tae, C., and Park, E.J. (2018, January 17\u201321). Combined Regression and Classification Models for Accurate Estimation of Walking Speed Using a Wrist-worn IMU. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513013"},{"key":"ref_16","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics), Springer."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Camomilla, V., Bergamini, E., Fantozzi, S., and Vannozzi, G. (2018). Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review. Sensors, 18.","DOI":"10.3390\/s18030873"},{"key":"ref_18","unstructured":"Martin, P. (2015). Saddle In Motion: Back Biomechanics of the Ridden Horse: Analysis of the Interactions Between the Saddle and the Back, and Application to the Development of News Prototypes of Saddles. [Ph.D. Thesis, Universit\u00e9 Claude Bernard\u2013Lyon I]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1111\/j.1365-2656.2006.01127.x","article-title":"Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: the case of the cormorant","volume":"75","author":"Wilson","year":"2006","journal-title":"J. Anim. Ecol."},{"key":"ref_20","unstructured":"R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_22","unstructured":"Ferraty, F., and Vieu, P. (2006). Nonparametric Functional Data Analysis, Springer."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ramsay, J.O., and Silverman, B.W. (2005). Functional Data Analysis, Springer. [2nd ed.].","DOI":"10.1007\/b98888"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A Tutorial on Support Vector Regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_25","first-page":"5","article-title":"e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071)","volume":"1","author":"Meyer","year":"2008","journal-title":"R Packag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1177\/096228029900800204","article-title":"Measuring agreement in method comparison studies","volume":"8","author":"Bland","year":"1999","journal-title":"Stat. Methods Med Res."},{"key":"ref_27","unstructured":"Lehnert, B. (2020, January 17). BlandAltmanLeh: Plots (Slightly Extended) Bland-Altman Plots. R Package Version 0.3.1. Available online: https:\/\/cran.r-project.org\/web\/packages\/BlandAltmanLeh\/index.html."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.proeng.2012.04.101","article-title":"Towards high-precision IMU\/GPS based stride-parameter determination in an outdoor runners\u2019 scenario","volume":"34","author":"Bichler","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.medengphy.2014.07.022","article-title":"Walking speed estimation using foot-mounted inertial sensors: Comparing machine learning and strap-down integration methods","volume":"36","author":"Mannini","year":"2014","journal-title":"Med Eng. Phys."},{"key":"ref_30","unstructured":"Chung, P., Soltoggio, A., Dawson, C.W., Meng, Q., and Pain, M. (2019, January 8\u201310). Stance Phase Detection for Walking and Running Using an IMU Periodicity based Approach. Proceedings of the 10th International Symposium on Computer Science in Sports (ISCSS), Moscow, Russia."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zihajehzadeh, S., and Park, E.J. (2016). Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0165211"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tvjl.2016.06.004","article-title":"Body lean angle in sound dressage horses in-hand, on the lunge and ridden","volume":"217","author":"Greve","year":"2016","journal-title":"Vet. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/518\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:26:46Z","timestamp":1760365606000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/2\/518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,17]]},"references-count":32,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20020518"],"URL":"https:\/\/doi.org\/10.3390\/s20020518","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,17]]}}}