{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T16:30:01Z","timestamp":1791304201413,"version":"4.3.1"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Consolidator Excellence Researcher Program of Obuda University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral\u2013entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen\u2019s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R2 up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems.<\/jats:p>","DOI":"10.3390\/s26020525","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T11:42:58Z","timestamp":1768304578000},"page":"525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bioinformatics-Inspired IMU Stride Sequence Modeling for Fatigue Detection Using Spectral\u2013Entropy Features and Hybrid AI in Performance Sports"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0430-9932","authenticated-orcid":false,"given":"Attila","family":"Bir\u00f3","sequence":"first","affiliation":[{"name":"Physiological Controls Research Center, Obuda University, 1034 Budapest, Hungary"},{"name":"Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, 1034 Budapest, Hungary"},{"name":"Department of Physiotherapy, University of Malaga, 29071 Malaga, Spain"},{"name":"Computational Intelligence Research Group, Sapientia Hungarian University of Transylvania, 540485 Targu Mures, Romania"},{"name":"Department of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Str. Nicolae Iorga, Nr. 1, 540088 Targu Mures, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-0800","authenticated-orcid":false,"given":"Levente","family":"Kov\u00e1cs","sequence":"additional","affiliation":[{"name":"Physiological Controls Research Center, Obuda University, 1034 Budapest, Hungary"},{"name":"John von Neumann Faculty of Informatics, Biomatics and Applied Artificial Intelligence Institute, Obuda University, 1034 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6722-2642","authenticated-orcid":false,"given":"L\u00e1szl\u00f3","family":"Szil\u00e1gyi","sequence":"additional","affiliation":[{"name":"Physiological Controls Research Center, Obuda University, 1034 Budapest, Hungary"},{"name":"Computational Intelligence Research Group, Sapientia Hungarian University of Transylvania, 540485 Targu Mures, Romania"},{"name":"John von Neumann Faculty of Informatics, Biomatics and Applied Artificial Intelligence Institute, Obuda University, 1034 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,13]]},"reference":[{"key":"ref_1","first-page":"1377","article-title":"Influence of fatigue on the modification of biomechanical parameters in endurance running: A systematic review","volume":"17","year":"2024","journal-title":"Int. J. Exerc. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106187","DOI":"10.1016\/j.ress.2018.06.015","article-title":"The evaluation of fatigue as a performance shaping factor in the petro-hra method","volume":"194","author":"Rasmussen","year":"2020","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"894","DOI":"10.14198\/jhse.2022.174.16","article-title":"The effect of self-selected music on endurance running capacity and performance in a mentally fatigued state","volume":"17","author":"Lam","year":"2021","journal-title":"J. Hum. Sport Exerc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Marotta, L., Buurke, J., Beijnum, B., and Reenalda, J. (2021). Towards machine learning-based detection of running-induced fatigue in real-world scenarios: Evaluation of imu sensor configurations to reduce intrusiveness. Sensors, 21.","DOI":"10.3390\/s21103451"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"762","DOI":"10.17309\/tmfv.2023.5.15","article-title":"Effects of fatigue induced by the running-based anaerobic sprint test on the performance in execution time and decision-making in technical-tactical actions in soccer (passing and driving) in a laboratory situation","volume":"23","author":"Palacio","year":"2023","journal-title":"Teor\u00ec\u00e2 Ta Metod. F\u00eczi\u010dnogo Vihovann\u00e2"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bir\u00f3, A., Cuesta-Vargas, A.I., and Szil\u00e1gyi, L. (2024). AI-Assisted Fatigue and Stamina Control for Performance Sports on IMU-Generated Multivariate Times Series Datasets. Sensors, 24.","DOI":"10.3390\/s24010132"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.4085\/1062-6050-531-19","article-title":"Fatigue-induced hip-abductor weakness and changes in biomechanical risk factors for running-related injuries","volume":"55","author":"Radzak","year":"2020","journal-title":"J. Athl. Train."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ghai, S., Ghai, I., and Narciss, S. (2024). Influence of taping on joint proprioception: A systematic review with between and within group meta-analysis. BMC Musculoskelet. Disord., 25.","DOI":"10.1186\/s12891-024-07571-2"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ramsey, D., and Crotin, R. (2022). Stride length impacts on sagittal knee biomechanics in flat ground baseball pitching. Appl. Sci., 12.","DOI":"10.3390\/app12030995"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111714","DOI":"10.1016\/j.jbiomech.2023.111714","article-title":"Challenges and advances in the use of wearable sensors for lower extremity biomechanics","volume":"157","author":"Hafer","year":"2023","journal-title":"J. Biomech."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jan, Y., Lung, C., Liau, B., and Hernandez, M. (2024). Editorial for the special issue on sensors and wearable technologies in sports biomechanics. Sensors, 24.","DOI":"10.3390\/s24196219"},{"key":"ref_12","first-page":"1","article-title":"Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance","volume":"10","author":"Alzahrani","year":"2024","journal-title":"Digit. Health"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sperlich, B., Aminian, K., D\u00fcking, P., and Holmberg, H. (2019). Editorial: Wearable sensor technology for monitoring training load and health in the athletic population. Front. Physiol., 10.","DOI":"10.3389\/fphys.2019.01520"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"51669","DOI":"10.1021\/acsami.4c10253","article-title":"Silk fabric functionalized by nanosilver enabling the wearable sensing for biomechanics and biomolecules","volume":"16","author":"Xing","year":"2024","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Verdel, N., Nogra\u0161ek, N., Drobni\u010d, M., Papuga, I., Strojnik, V., and Supej, M. (2025). Influence of running speed, inclination, and fatigue on calcaneus angle in female runners. Front. Physiol., 16.","DOI":"10.3389\/fphys.2025.1505263"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, J.-Y., and Sun, R.-S. (2023). Pilot fatigue survey: A study of the mutual influence among fatigue factors in the \u201cwork\u201d dimension. Front. Public Health, 11.","DOI":"10.3389\/fpubh.2023.1014503"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.4028\/p-F2MUdc","article-title":"Literature review of the behaviour of adhesive joint fatigue performance","volume":"1153","author":"Weltsch","year":"2025","journal-title":"Mater. Sci. Forum"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hwang, S., Kwon, N., Lee, D., Kim, J., Yang, S., Youn, I., Moon, H.J., Sung, J.K., and Han, S. (2025). A multimodal fatigue detection system using semg and imu signals with a hybrid cnn-lstm-attention model. Sensors, 25.","DOI":"10.3390\/s25113309"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chang, P., Wang, C., Chen, Y., Wang, G., and Lu, A. (2023). Identification of runner fatigue stages based on inertial sensors and deep learning. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1302911"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2452","DOI":"10.1109\/JBHI.2020.2999902","article-title":"Evaluation of machine learning models for classifying upper extremity exercises using inertial measurement unit-based kinematic data","volume":"24","author":"Hua","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Hernandez, V., Venture, G., Kuli\u0107, D., and Chen, B. (2021). A data-driven approach to predict fatigue in exercise based on motion data from wearable sensors or force plate. Sensors, 21.","DOI":"10.3390\/s21041499"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mart\u00edns, F., Gomes, R., Lopes, V., Silva, F., and Mendes, R. (2020). Node and network entropy\u2014A novel mathematical model for pattern analysis of team sports behavior. Mathematics, 8.","DOI":"10.3390\/math8091543"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kolomeets, M., Desnitsky, V., Kotenko, I., and Chechulin, A. (2023). Graph visualization: Alternative models inspired by bioinformatics. Sensors, 23.","DOI":"10.3390\/s23073747"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1111\/aphw.12547","article-title":"Influence of running on incidental and integral vitality and fatigue: An intensive longitudinal intervention in young women\u2019s daily life","volume":"16","author":"Buchner","year":"2024","journal-title":"Appl. Psychol. Health Well-Being"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mart\u00edn-Mart\u00edn, J., Jim\u00e9nez-Partinen, A., De-Torres, I., Escriche-Escuder, A., Gonz\u00e1lez-S\u00e1nchez, M., Muro-Culebras, A., Rold\u00e1n-Jim\u00e9nez, C., Ruiz-Munoz, M., Mayoral-Cleries, F., and Bir\u00f3, A. (2022). Reliability Study of Inertial Sensors LIS2DH12 Compared to ActiGraph GT9X: Based on Free Code. J. Pers. Med., 12.","DOI":"10.3390\/jpm12050749"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, R., Wang, S., and Li, J. (2025). How coach leadership behavior influences athletes\u2019 performance: The chain-mediated role of the coach-athlete relationship and psychological fatigue. Front. Psychol., 15.","DOI":"10.3389\/fpsyg.2024.1500867"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sengupta, N., Begg, R., Rao, A., Bajelan, S., Said, C., and Palaniswami, M. (2024). Predicting improvement in biofeedback gait training using short-term spectral features from minimum foot clearance data. Front. Bioeng. Biotechnol., 12.","DOI":"10.3389\/fbioe.2024.1417497"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e14709","DOI":"10.1111\/sms.14709","article-title":"Mechanical deviations in stride characteristics during running in the severe intensity domain are associated with a decline in muscle oxygenation","volume":"34","author":"Chalitsios","year":"2024","journal-title":"Scand. J. Med. Sci. Sports"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s42979-024-02794-5","article-title":"Optimized convolutional neural network using hierarchical particle swarm optimization for sensor based human activity recognition","volume":"5","author":"Ankalaki","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e1429","DOI":"10.1016\/j.eats.2020.06.003","article-title":"Using wearable technology to evaluate the kinetics and kinematics of the overhead throwing motion in baseball players","volume":"9","author":"Lizzio","year":"2020","journal-title":"Arthrosc. Tech."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5824","DOI":"10.1109\/JSEN.2022.3148313","article-title":"Mode-independent stride length estimation with imus in smartphones","volume":"22","author":"Bo","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1007\/s10439-023-03290-2","article-title":"Smartwatch-based prediction of single-stride and stride-to-stride gait outcomes using regression-based machine learning","volume":"51","author":"Bailey","year":"2023","journal-title":"Ann. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Olaya-Cuartero, J., Pueo, B., Villal\u00f3n-Gasch, L., and Jim\u00e9nez-Olmedo, J. (2024). Stability of running stride biomechanical parameters during half-marathon race. Appl. Sci., 14.","DOI":"10.3390\/app14114807"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kathirgamanathan, B., Caulfield, B., and Cunningham, P. (2023, January 9\u201311). Towards Globalised Models for Exercise Classification using Inertial Measurement Units. Proceedings of the IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA.","DOI":"10.1109\/BSN58485.2023.10331612"},{"key":"ref_35","unstructured":"Kathirgamanathan, B., Caulfield, B., and Cunningham, P. (2023). Multivariate Time Series data of Fatigued and Non-Fatigued Running from Inertial Measurement Units (0.0) [Data Set], Zenodo."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bir\u00f3, A., Szil\u00e1gyi, S.M., Szil\u00e1gyi, L., Mart\u00edn-Mart\u00edn, J., and Cuesta-Vargas, A.I. (2023). Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer. Sensors, 23.","DOI":"10.3390\/s23073595"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3329\/ganit.v40i1.48193","article-title":"Global spectral collocation method with fourier transform to solve differential equations","volume":"40","author":"Akter","year":"2020","journal-title":"Ganit J. Bangladesh Math. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Clark, N., and Pethick, J. (2022). Variability and complexity of knee neuromuscular control during an isometric task in uninjured physically active adults: A secondary analysis exploring right\/left and dominant\/nondominant asymmetry. Appl. Sci., 12.","DOI":"10.3390\/app12094762"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"55","DOI":"10.5958\/2394-2800.2022.00011.6","article-title":"Wearable sensors: An aid for amassing biomechanical data","volume":"9","author":"Kapri","year":"2022","journal-title":"Indian J. Health Sci. Care"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e17437","DOI":"10.1016\/j.heliyon.2023.e17437","article-title":"Stride-to-stride variability and fluctuations at intensities around lactate threshold in distance runners","volume":"9","author":"Watanabe","year":"2023","journal-title":"Heliyon"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1109\/TCBB.2024.3395777","article-title":"Analysis of cancer-associated mutations of polb using machine learning and bioinformatics","volume":"21","author":"Alkhanbouli","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e11414","DOI":"10.7717\/peerj.11414","article-title":"Partr2: Partitioning r2in generalized linear mixed models","volume":"9","author":"Stoffel","year":"2021","journal-title":"PeerJ"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-Grande, D., Atashzar, S., Martinez-Valdes, E., and Falla, D. (2021). Muscle network topology analysis for the classification of chronic neck pain based on emg biomarkers extracted during walking. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0252657"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mart\u00edn-Mart\u00edn, J., Wang, L., De-Torres, I., Escriche-Escuder, A., Gonz\u00e1lez-S\u00e1nchez, M., Muro-Culebras, A., Rold\u00e1n-Jim\u00e9nez, C., Ruiz-Mu\u00f1oz, M., Mayoral-Cleries, F., and Bir\u00f3, A. (2022). The Validity of the Energy Expenditure Criteria Based on Open Source Code through two Inertial Sensors. Sensors, 22.","DOI":"10.3390\/s22072552"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Teixeira, J., Branquinho, L., Leal, M., Marinho, D., Ferraz, R., Barbosa, T., Monteiro, A.M., and Forte, P. (2022). Modeling the major influencing factor on match running performance during the in-season phase in a portuguese professional football team. Sports, 10.","DOI":"10.3390\/sports10080121"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hoegberg, Z., Donahue, S., and Major, M. (2025). An open-source wearable system for real-time human biomechanical analysis. Sensors, 25.","DOI":"10.20944\/preprints202503.1815.v1"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"20721","DOI":"10.1038\/s41598-023-47828-9","article-title":"Muscle activity variability patterns and stride to stride fluctuations of older adults are positively correlated during walking","volume":"13","author":"Stergiou","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.59670\/ml.v20i6.5079","article-title":"The effect of training stride length and stride frequency on increasing sprint speed","volume":"20","author":"Sobarna","year":"2023","journal-title":"Migr. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1002\/admt.202201662","article-title":"Wireless battery-free flexible sensing system for continuous wearable health monitoring","volume":"8","author":"Wu","year":"2023","journal-title":"Adv. Mater. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Becker, S., Simon, S., Dindorf, C., Dully, J., Bartaguiz, E., Schmitz, L., Kothe, N., Fr\u00f6hlich, M., and Ludwig, O. (2023). Fatigue as a key factor for testing knee stability with single leg drop landing for injury prevention and return to play tests. Front. Sports Act. Living, 5.","DOI":"10.3389\/fspor.2023.1243732"},{"key":"ref_51","unstructured":"(2025, December 09). Sunbears Cloud Campus. Available online: https:\/\/www.sunbears.com."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/525\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T11:44:59Z","timestamp":1768304699000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,13]]},"references-count":51,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26020525"],"URL":"https:\/\/doi.org\/10.3390\/s26020525","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,13]]}}}