{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T16:44:01Z","timestamp":1788972241517,"version":"build-2803163510"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AiF Projekt GmbH","award":["KK5119803BM3"],"award-info":[{"award-number":["KK5119803BM3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Ground contact (GC) detection is essential for sprint performance analysis. Inertial measurement units (IMUs) enable field-based assessment, but their reliability during sprint acceleration remains limited when using heuristic and recently used machine learning algorithms. This study introduces a deep learning one-dimensional convolutional neural network (1D-CNN) to improve GC event and GC times detection in sprint acceleration. Methods: Twelve sprint-trained athletes performed 60 m sprints while bilateral shank-mounted IMUs (1125 Hz) and synchronized high-speed video (250 Hz) captured the first 15 m. Video-derived GC events served as reference labels for model training, validation, and testing, using resultant acceleration and angular velocity as model inputs. Results: The optimized model (18 inception blocks, window = 100, stride = 15) achieved mean Hausdorff distances \u2264 6 ms and 100% precision and recall for both validation and test datasets (Rand Index \u2265 0.977). Agreement with video references was excellent (bias &lt; 1 ms, limits of agreement \u00b1 15 ms, r &gt; 0.90, p &lt; 0.001). Conclusions: The 1D-CNN surpassed heuristic and prior machine learning approaches in the sprint acceleration phase, offering robust, near-perfect GC detection. These findings highlight the promise of deep learning-based time-series models for reliable, real-world biomechanical monitoring in sprint acceleration tasks.<\/jats:p>","DOI":"10.3390\/s26010342","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:38:56Z","timestamp":1767616736000},"page":"342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning 1D-CNN-Based Ground Contact Detection in Sprint Acceleration Using Inertial Measurement Units"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9259-4820","authenticated-orcid":false,"given":"Felix","family":"Friedl","sequence":"first","affiliation":[{"name":"Sports and Technology, Institute of Sport Science, Otto von Guericke University Magdeburg, 39116 Magdeburg, Germany"},{"name":"Department Team, Combat & Acrobatic Sports, Institute for Applied Training Science, 04109 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thorben","family":"Menrad","sequence":"additional","affiliation":[{"name":"Department Team, Combat & Acrobatic Sports, Institute for Applied Training Science, 04109 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"Edelmann-Nusser","sequence":"additional","affiliation":[{"name":"Department Team, Combat & Acrobatic Sports, Institute for Applied Training Science, 04109 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1007\/s00421-019-04139-0","article-title":"Sprint running: From fundamental mechanics to practice\u2014A review","volume":"119","author":"Haugen","year":"2019","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1177\/17479541231200526","article-title":"Determinant biomechanical variables for each sprint phase performance in track and field: A systematic review","volume":"19","author":"Moura","year":"2024","journal-title":"Int. J. Sports Sci. Coach."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s40798-019-0221-0","article-title":"The Training and Development of Elite Sprint Performance: An Integration of Scientific and Best Practice Literature","volume":"5","author":"Haugen","year":"2019","journal-title":"Sports Med. Open"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mendicino, M., Santos, J.M.P.d.A.d., Margheriti, P., Zaffagnini, S., and Di Paolo, S. (2025). Detection of Foot Contact Using Inertial Measurement Units in Sports Movements: A Systematic Review. Appl. Sci., 15.","DOI":"10.3390\/app151810250"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Benson, L.C., R\u00e4is\u00e4nen, A.M., Clermont, C.A., and Ferber, R. (2022). Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis. Sensors, 22.","DOI":"10.3390\/s22051722"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Adesida, Y., Papi, E., and McGregor, A.H. (2019). Exploring the Role of Wearable Technology in Sport Kinematics and Kinetics: A Systematic Review. Sensors, 19.","DOI":"10.3390\/s19071597"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.proeng.2014.06.009","article-title":"Assessment of Foot Kinematics During Steady State Running Using a Foot-mounted IMU","volume":"72","author":"Bailey","year":"2014","journal-title":"Procedia Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.gaitpost.2019.07.123","article-title":"Quantification of the validity and reliability of sprint performance metrics computed using inertial sensors: A systematic review","volume":"73","author":"Macadam","year":"2019","journal-title":"Gait Posture"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1519\/JSC.0b013e31825183ef","article-title":"The contribution of step characteristics to sprint running performance in high-level male and female athletes","volume":"27","author":"Debaere","year":"2013","journal-title":"J. Strength Cond. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"376","DOI":"10.2165\/00007256-199213060-00002","article-title":"Biomechanics of sprint running. A review","volume":"13","author":"Mero","year":"1992","journal-title":"Sports Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1515\/hukin-2015-0014","article-title":"Selected determinants of acceleration in the 100m sprint","volume":"45","author":"Fostiak","year":"2015","journal-title":"J. Hum. Kinet."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/14763141.2018.1473479","article-title":"Phase analysis in maximal sprinting: An investigation of step-to-step technical changes between the initial acceleration, transition and maximal velocity phases","volume":"19","author":"Wilkau","year":"2020","journal-title":"Sports Biomech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1242\/bio.20148284","article-title":"Kinematics of transition during human accelerated sprinting","volume":"3","author":"Nagahara","year":"2014","journal-title":"Biol. Open"},{"key":"ref_15","first-page":"1253","article-title":"Kinematics of the thorax and pelvis during accelerated sprinting","volume":"58","author":"Nagahara","year":"2018","journal-title":"J. Sports Med. Phys. Fit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1055\/s-0033-1363252","article-title":"Association of acceleration with spatiotemporal variables in maximal sprinting","volume":"35","author":"Nagahara","year":"2014","journal-title":"Int. J. Sports Med."},{"key":"ref_17","first-page":"23","article-title":"Kinematic analysis of the sprint start and acceleration from the blocks","volume":"21","author":"Coh","year":"2006","journal-title":"New Stud. Athl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hurtado-Perez, A.E., Toledano-Ayala, M., Cruz-Albarran, I.A., Lopez-Z\u00fa\u00f1iga, A., Moreno-Perez, J.A., \u00c1lvarez-L\u00f3pez, A., Rodriguez-Resendiz, J., and Perez-Ramirez, C.A. (2025). Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis. Biomimetics, 10.","DOI":"10.3390\/biomimetics10050339"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.proeng.2016.06.330","article-title":"IMU-based Determination of Stance Duration During Sprinting","volume":"147","author":"Schmidt","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Blauberger, P., Horsch, A., and Lames, M. (2021). Detection of Ground Contact Times with Inertial Sensors in Elite 100-m Sprints under Competitive Field Conditions. Sensors, 21.","DOI":"10.3390\/s21217331"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Miranda-Oliveira, P., Branco, M., and Fernandes, O. (2023). Accuracy and Interpretation of the Acceleration from an Inertial Measurement Unit When Applied to the Sprint Performance of Track and Field Athletes. Sensors, 23.","DOI":"10.3390\/s23041761"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tillaar, R.v.D., Nagahara, R., Gleadhill, S., and Jim\u00e9nez-Reyes, P. (2021). Step-to-Step Kinematic Validation between an Inertial Measurement Unit (IMU) 3D System, a Combined Laser+IMU System and Force Plates during a 50 M Sprint in a Cohort of Sprinters. Sensors, 21.","DOI":"10.3390\/s21196560"},{"key":"ref_23","first-page":"603615","article-title":"Use of accelerometers for detecting foot-ground contact time during running","volume":"6036","author":"Purcell","year":"2006","journal-title":"BioMEMS Nanotechnol. II"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1016\/j.jbiomech.2011.12.020","article-title":"Estimation of temporal parameters during sprint running using a trunk-mounted inertial measurement unit","volume":"45","author":"Bergamini","year":"2012","journal-title":"J. Biomech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/TNSRE.2021.3131953","article-title":"Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge of Gait Events","volume":"30","author":"Donahue","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1038\/s41598-023-29314-4","article-title":"Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment","volume":"13","author":"Donahue","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_27","unstructured":"Fawaz, H.I., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D.F., Weber, J., Webb, G.I., Idoumghar, L., Muller, P.-A., and Petitjean, F. (2019). InceptionTime: Finding AlexNet for time series classification. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1007\/s10618-024-01022-1","article-title":"Bake off redux: A review and experimental evaluation of recent time series classification algorithms","volume":"38","author":"Middlehurst","year":"2024","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1109\/TBME.2023.3258606","article-title":"A Novel Event-Driven Spiking Convolutional Neural Network for Electromyography Pattern Recognition","volume":"70","author":"Xu","year":"2023","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gorges, T., Davidson, P., Boeschen, M., Hotho, A., and Merz, C. (2024). IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms. Sensors, 24.","DOI":"10.3390\/s24216773"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chow, D.H.K., Tremblay, L., Lam, C.Y., Yeung, A.W.Y., Cheng, W.H.W., and Tse, P.T.W. (2021). Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor. Sensors, 21.","DOI":"10.3390\/s21144633"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5287","DOI":"10.1038\/s41598-025-89716-4","article-title":"Learning based lower limb joint kinematic estimation using open source IMU data","volume":"15","author":"Hur","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3649448","article-title":"Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey","volume":"56","author":"Foumani","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4029","DOI":"10.1007\/s41060-024-00708-5","article-title":"Look into the LITE in deep learning for time series classification","volume":"20","author":"Devanne","year":"2025","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1260\/1747-9541.6.3.479","article-title":"The Science of Speed: Determinants of Performance in the 100 m Sprint","volume":"6","author":"Majumdar","year":"2011","journal-title":"Int. J. Sports Sci. Coach."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lee, H., and Shin, D. (2025). Beyond Information Distortion: Imaging Variable-Length Time Series Data for Classification. Sensors, 25.","DOI":"10.3390\/s25030621"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbiomech.2018.09.009","article-title":"Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities","volume":"81","author":"Halilaj","year":"2018","journal-title":"J. Biomech."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences, Routledge.","DOI":"10.4324\/9780203771587"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","article-title":"Statistical Methods for assessing agreement between two methods of clinical measurement","volume":"327","author":"Bland","year":"1986","journal-title":"Lancet"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1249\/MSS.0b013e31818cb278","article-title":"Progressive statistics for studies in sports medicine and exercise science","volume":"41","author":"Hopkins","year":"2009","journal-title":"Med. Sci. Sports Exerc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/342\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:45:46Z","timestamp":1767617146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26010342"],"URL":"https:\/\/doi.org\/10.3390\/s26010342","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]}}}