Author: admin

Morales, E. O. & Arellano, R. C. Regression analysis model applied to age-group swimmers: 50m race component times analysis. J. Hum. Sport. Exerc. 14(2), 315–324. https://doi.org/10.14198/jhse.2019.142.05 (2019).

Google Scholar 

Gonjo, T. & Olstad, B. H. Race analysis in competitive swimming: a narrative review. Int. J. Enivron Res. Public. Health. 18 (1), 69. https://doi.org/10.3390/ijerph18010069 (2021).

Google Scholar 

Thng, S., Pearson, S., Mitchell, S. J. G., Meulenbroek, C. & Keogh, J. W. L. On-block mechanistic determinants of start performance in high performance swimmers. Sports Biomech. 23(5), 682–694. https://doi.org/10.1080/14763141.2021.1887342 (2021).

Google Scholar 

Born, D-P., Polach, M. & Staunton, C. Determining validity and reliability of an in-field performance analysis system for swimming. Sensors 24 (22), 7186. https://doi.org/10.3390/s24227186 (2024).

Google Scholar 

Barbosa, T. M. et al. The role of the biomechanics analyst in swimming training and competition analysis. Sports Biomech. 22(12), 1734–1751. https://doi.org/10.1080/14763141.2021.1960417 (2021).

Google Scholar 

Morais, J. E., Barbosa, T. M., Silva, A. J., Veiga, S. & Marinho, D. A. Profiling of elite male junior 50 m freestyle sprinters: Understanding the speed-time relationship. Scand. J. Med. Sci. Sports 32(1), 60–68. https://doi.org/10.1111/sms.14058 (2022).

Google Scholar 

Morais, J. E., Barbosa, T. M., Bragada, J. A., Nevill, A. M. & Marinho, D. A. Race analysis and determination of stroke frequency – Stroke length combinations during the 50-m freestyle event. J. Sports. Sci. Med. 22, 156–165. https://doi.org/10.52082/jssm.2023.156 (2023).

Google Scholar 

Wadrzyk, L. et al. Impact of energetic-anthropometric features on 50-m and 100-m freestyle kinematic indices in young male swimmers. Act. Bioeng. Biomech. 27 (1), 157–167. https://doi.org/10.37190/abb-02559-2024-02 (2025).

Google Scholar 

Delhaye, E. et al. Automatic swimming activity recognition and lap time assessment based on a single IMU: A deep learning approach. Sensors 22 (15), 5786. https://doi.org/10.3390/s22155786 (2022).

Google Scholar 

Donisi, L. et al. Wearable sensors and artificial intelligence for physical ergonomics: a systematic review of literature. Diagnostics 12(12), 3048. https://doi.org/10.3390/diagnostics12123048 (2022).

Google Scholar 

Morais, J. E., Oliveira, J. P., Sampaio, T. & Barbosa, T. M. Wearables in swimming for real-time feedback: a systematic review. Sensors 22 (10), 3677. https://doi.org/10.3390/s22103677 (2022).

Google Scholar 

Skorulski, M., Stachowicz, M., Kulis, S. & Gajewski, J. Accelerometric assessment of fatigue-induced changes in swimming technique in high performance adolescent athletes. Sci. Rep. 15 (1), 2409. https://doi.org/10.1038/s41598-024-83310-w (2025).

Google Scholar 

Worsey, M. T. O., Pahl, R., Espinosa, H. G., Shepherd, J. B. & Thiel, D. V. Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming? J. Sports Sci. 39 (18), 2095–2114. https://doi.org/10.1080/02640414.2021.1918432 (2021).

Google Scholar 

Ide, T. et al. Examining Stroke Efficiency: How an Olympian outperformed a masters swimmer in backstroke. Psychol. Res. 14(8), 251–258 (2024).

Google Scholar 

Biro, A., Cuesta-Vargas, A. I. & Szilagyi, L. AI-assisted fatigue and stamina control for performance sports on IMU-generated multivariate times series datasets. Sensors 24, 132. https://doi.org/10.3390/s24010132 (2023).

Google Scholar 

Serena, R. M., Hinterwimmer, F., Burgkart, R., von Eisenhart-Rothe, R. & Rueckert, D. The use of artificial intelligence and wearable inertial measurement units in medicine: systematic review. JMIR Mhealth Uhealth 29(13), 60521. https://doi.org/10.2196/60521 (2025).

Google Scholar 

Barbosa, T. M., Bartolomeu, R., Morais, J. E. & Costa, M. J. Skillful swimming in age-groups is determined by anthropometrics, biomechanics and energetics. Front. Physiol. 15 (10), 73. https://doi.org/10.3389/fphys.2019.00073 (2019).

Google Scholar 

Ruiz-Navarro, J. J. et al. Factors relating to sprint swimming performance: A systematic review. Sports Med. 55(4), 899–922. https://doi.org/10.1007/s40279-024-02172-4 (2025).

Google Scholar 

Ruiz-Navarro, J. J., Lopez-Belmonte, O., Gay, A., Cuenca-Fernandez, F. & Arellano, R. A new model of performance classification to standardize the research results in swimming. Eur. J. Sport Sci. 23(4), 478–488. https://doi.org/10.1080/17461391.2022.2046174 (2022).

Google Scholar 

Martin, R. & Saller, K. Textbook of Anthropology Vol. 1 (Gustav Fischer Verlag, 1957).

Google Scholar 

Tanner, J. Growth at Adolescence 2nd ed. (Blackwell Scientific Publications, 1962).

Google Scholar 

Kryst, L., Zeglen, M., Woronkowicz, A. & Kowal, M. Secular changes of foot dimensions among children and adolescents (3–18 years of age). Anthropol. Anz. 80(1), 31–38. https://doi.org/10.1127/anthranz/2022/1615 (2023).

Google Scholar 

Pla, R. et al. Spatial-temporal variables for swimming coaches: A comparison study between video and TritonWear sensor. Int. J. Sports Sci. Coach. 16 (6), 1–10. https://doi.org/10.1177/17479541211013755 (2021).

Google Scholar 

Hopkins, W. G. A Scale of Magnitudes for Effect Statistics: A New View of Statistics. http://www.sportsci.org/resource/stats/effectmag.html.

Ghorbani, H. Mahalanobis distance and its application for detecting multivariate outrliers. Facta Univ. Ser. Math. Inform. 34(3), 583–595. https://doi.org/10.22190/FUMI1903583G (2019).

Google Scholar 

Rea, A. & Rea, W. How many components should be retained from a multivariate time series PCA? https://arxiv.org/abs/1610.03588 (2016).

Morais, J. E., Barbosa, T. M., Nevill, A. M., Cobley, S. & Marinho, D. A. Understanding the role of propulsion in the prediction of front-crawl swimming velocity and in the relationship between stroke frequency and stroke length. Front. Physiol. 27 (13), 876838. https://doi.org/10.3389/fphys.2022.876838 (2022).

Google Scholar 

Simbana-Escobar, D., Hellard, P. & Seifert, L. Influence of stroke rate on coordination and sprint performance in elite male and female swimmers. Scand. J. Med. Sci. Sports 30(11), 2078–2091. https://doi.org/10.1111/sms.13786 (2020).

Google Scholar 

Wadrzyk, L., Staszkiewicz, R. & Strzala, M. Comparison of race performance characteristics for the 50 m and 100 m freestyle among regional-level male swimmers. Appl. Sci. 12 (24), 12577. https://doi.org/10.3390/app122412577 (2022).

Google Scholar 

Marinho, D. A., Neiva, H. P., Branquinho, L. & Ferraz, R. Anthropometric characterization and muscle strength parameters in young female swimmers at national level: The relationship with performance in the 50 m freestyle. J. Hum. Sport Exerc. 16(2), 295–306. https://doi.org/10.14198/jhse.2021.16.Proc2.15. (2021).

Google Scholar 

Alves, M., Carvalho, D. D., Fernandes, R. J. & Vilas-Boas, J. P. How anthropometrics of young and adolescent swimmers influence stroking parameters and performance? A systematic review. Int. J. Environ. Res. Public. Health. 19 (5), 2543. https://doi.org/10.3390/ijerph19052543 (2022).

Google Scholar 

Retzepis, N. O. et al. The effect of peak height velocity on strength and power development of young athletes: a scoping review. J. Funct. Morphol. Kinesiol. 10 (2), 168. https://doi.org/10.3390/jfmk10020168 (2025).

Google Scholar 

Keiner, M. et al. The influence of upper- and lower-body maximum strength on swim block start, turn, and overall swim performance in sprint swimming. J. Strength Cond. Res. 35(10), 2839–2845. https://doi.org/10.1519/JSC.0000000000003229 (2021).

Google Scholar 

Nevill, A. M., Negra, Y., Myers, T. D., Sammoud, S. & Chaabene, H. Key somatic variables associated with, and differences between the 4 swimming strokes. J. Sports Sci. 38(7), 787–794. https://doi.org/10.1080/02640414.2020.1734311 (2020).

Google Scholar 

Price, T., Cimadoro, G. & Legg, H. S. Physical performance determinants in competitive youth swimmers: a systematic review. BMC Sports Sci. Med. Rehabil. 16, 20. https://doi.org/10.1186/s13102-023-00767-4 (2024).

Google Scholar 

Dopsaj, M. et al. Body composition in international sprint swimmers: are there any relations with performance? Int. J. Environ. Res. Public. Health. 17 (24), 9464. https://doi.org/10.3390/ijerph17249464 (2020).

Google Scholar 

Gourgoulis, V. et al. The influence of the hand’s acceleration and the relative contribution of drag and lift forces in front crawl swimming. J. Sports Sci. 33(7), 696–712. https://doi.org/10.1080/02640414.2014.962571 (2015).

Google Scholar 

Takagi, H. et al. How do swimmers control their front crawl swimming velocity? Current knowledge and gaps from hydrodynamic perspectives. Sports Biomech. 22(12), 1552–1571. https://doi.org/10.1080/14763141.2021.1959946 (2023).

Google Scholar 

Koga, D. et al. Relationship between hand kinematics, hand hydrodynamic pressure distribution and hand propulsive force in sprint front crawl swimming. Front. Sports Act. Living. 15 (4), 786459. https://doi.org/10.3389/fspor.2022.786459 (2022).

Google Scholar 

Dos Santos, K. B., Bento, P. C. B. & Rodacki, A. F. L. A three-dimensional aspect of front crawl swimming in swimmers with different proficiency levels. J. Phys. Educ. Sport 23(5), 1092–1095. https://doi.org/10.7752/jpes.2023.05136 (2023).

Google Scholar 

Dos Santos, M. A. M. et al. The influence of anthropometric variables, body composition, propulsive force and maturation on 50m freestyle swimming performance in junior swimmers: An allometric approach. J. Sports Sci. 39(14), 1615–1620. https://doi.org/10.1080/02640414.2021.1891685 (2021).

Google Scholar 

Di Prampero, P. E. & Osgnach, C. Energy cost of human locomotion on land and in water. In Muscle and Exercise Physiology (ed. Zoladz, J. A.) 183–213 (Elsevier Science Publishing Co Inc, 2019).

Cortesi, M. et al. Passive drag in young swimmers: effects of body composition, morphology and gliding position. Int. J. Environ. Res. Public. Health. 17 (6), 2002. https://doi.org/10.3390/ijerph17062002 (2020).

Google Scholar 

Bartolomeu, R. F. et al. Nonlinear analysis of the hand and foot force-time profiles in the four competitive swimming strokes. J. Hum. Kinet. 90, 71–88. https://doi.org/10.5114/jhk/172616 (2024).

Google Scholar