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  1. 1

    Horse gait analysis using wearable inertial sensors and machine learning

    Rana, M., Mittal, V. Subjects: “…Sensor…”
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  2. 2

    Pacing and stroke mechanics in 400m freestyle: A hierarchical generalized additive model across performance levels

    Bideau, N., Bouvet, A. “…The analysis involves 127 trained to elite swimmers, divided into performance groups, using an IMU sensor to measure spatiotemporal parameters. A Hierarchical Generalized Additive Model identified trends and group-specific variations in speed, stroke rate, stroke length, and stroke smoothness (jerk cost). …”
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  3. 3

    Investigation of supervised machine learning for IMU-based swimming activity recognition

    Aram, S., Thompson, I. M., Hudson, D. A., Banks, J. Subjects: “…Sensor…”
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  4. 4

    Asymmetry in foot pressure distribution patterns during bend sprinting

    Ohnuma, H., Kumano, A., Chiba, Y., Yoshimoto, T.
    Published in Journal of Human Sport & Exercise (2025)
    “…Foot pressure was measured using a wireless insole pressure sensor system with 13 sensors per foot. The maximum foot pressure and the pressure of each sensor at the time of maximum foot pressure were calculated. …”
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  5. 5

    Comparative analysis of foam-only versus carbon-plated advanced footwear technology spikes in distance runners

    Wu, Y., Zhang, H., Wang, S., Lu, C., Xing, Q., Tian, Y., He, D., Sun, L., Shen, Y.
    Published in Frontiers in Physiology (2025)
    “…Running economy (RE) was assessed using a portable gas analyzer (MetaMax 3B-R2); spatiotemporal gait variables were recorded with shoe-mounted sensors (RunScribeTM); and participants rated comfort, cushioning, and perceived performance on a 10-point Likert scale. …”
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  6. 6

    Evaluating the influence of sensor configuration and hyperparameter optimization on wearable-based knee moment estimation during running

    Höschler, L., Halmich, C., Schranz, C., Koelewijn, A. D., Schwameder, H. “…Wearable sensors combined with machine learning (ML) offer a promising approach for estimating joint kinetics in real-world settings, with potential applications in athlete monitoring and injury prevention. …”
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  7. 7

    Evaluating agreement between a high-g and low-g accelerometer for measuring tibial acceleration across a range of running velocities

    Wong, J., Wyatt, H. E., Whatman, C., Cross, M. R., Yang, D., Sheerin, K.
    Published in Sports Biomechanics (2025)
    Subjects: “…Sensor…”
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  8. 8

    Open water swimming: swimmers` kinematical and neuromuscular characterisation in a 5 km swim

    Conceição, A., Marinho, D., Stastny, J., Gonçalves, C., Freitas, J., da Costa-Machado, R., Louro, H.
    Published in Sports (2025)
    Subjects: “…Sensor…”
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  9. 9

    Physiological predictors of peak velocity in the VAM-EVAL incremental test and the role of kinematic variables in running economy in triathletes

    Montraveta, J., Fernández-Jarillo, I., Iglesias, X., Feldmann, A., Chaverri, D.
    Published in Sports (2025)
    “…Maximal oxygen uptake (VO2max), ventilatory thresholds, RE, and minimum muscle oxygen saturation (SmO2min) were obtained with a portable gas analyzer and near-infrared spectroscopy (NIRS), while cadence, stride length, vertical oscillation, and contact time were recorded with a foot-mounted inertial sensor. Multiple linear regression showed that VO2max and SmO2min together accounted for 86% of the variance in Vpeak (VO2max: r = 0.76; SmO2min: r = -0.68), whereas RE at 16 km·h-1 displayed only a moderate association (r = 0.54). …”
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  10. 10

    Comparison of an IMU- and magnet timing gate- based system and an opto-electronic device for analysing running biomechanics

    Neuhaus, N., Breuker, K., Schmidt, M.
    Published in Sports Engineering (2025)
    Subjects: “…Sensor…”
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  11. 11

    Validation of step time and asymmetry detection algorithm in high-intensity running with Movesense IMU

    Martín Gómez, R., Kamstra, H., Allevard, E., Pollet-Villard, L., Cotter, J., Lamb, P.
    Published in Journal of Biomechanics (2025)
    Subjects: “…Sensor…”
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  12. 12

    Segmental external load in linear running in elite futsal players: a multifactorial and individual variability analysis using linear mixed models

    Villarejo-García, D. H., Navarro-Martínez, C., Pino-Ortega, J.
    Published in Sports (2025)
    “…Eight elite players, including six outfield players and two goalkeepers (mean age 23.9 ± 3.4 years, height 164.96 ± 4.22 cm, body mass 60.31 ± 4.56 kg), performed an incremental test and were measured using six WIMU PRO™ inertial sensors. The sensors recorded segmental PlayerLoad, speed, and training zones. …”
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  13. 13

    Trigger force and pistol motion of air pistol shooters of different skill levels

    Liao, Y., Yang, H., Hamill, J., Tang, W.
    Published in Journal of Sports Sciences (2025)
    Subjects: “…Sensor…”
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  14. 14

    Foot strike pattern detection using a loadsol® sensor insole

    Hata, K., Yamazaki, Y., Ishikawa, M., Yanagiya, T.
    Published in Sensors (2025)
    Subjects: “…Sensor…”
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  15. 15

    Effect of longitudinal bending stiffness of marathon racing shoes on the elite runners` lower limb biomechanics

    Kuang, L., Lin, T., Yang, H., Li, R., Zuo, C., Zhang, H., Cai, Y.
    Published in Footwear Science (2025)
    “…Muscle activation was recorded using Noraxon Ultium EMG sensors (Noraxon, Scottsdale, AZ, USA). Kinematic, kinetic data and Surface electromyography signals were processed with Vicon Nexus 2.7 and Visual3D. …”
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  16. 16

    Reliability, accuracy and ecological validity of a wireless pressure sensor for swimming performance analysis

    Coloretti, V., Russo, G., Bartolomei, S., Fantozzi, S., Cortesi, M.
    Published in Sports Engineering (2025)
    Subjects: “…Sensor…”
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  17. 17

    The influence of menstrual cycle in heart rate variability and performance - a case study of a highly-trained female long-distance runner

    Barría-Miranda, F., Azócar-Gallardo, J., Ñancupil-Andrade, L., Ñancupil-Andrade, A., Garcia-Carrillo, E., Campos-Uribe, V., Cresp-Barria, M.
    Published in Journal of Human Sport & Exercise (2025)
    Subjects: “…Sensor…”
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  18. 18

    Sensor fusion of high-frequency RTK GNSS and IMU improves the detection performance of running characteristics of each step in 400 m run

    Keisuke, O., Kiyoshi, H., Naoto, M., Akiko, K., Wako, K., Hiroshi, N., Shunya, U., Masaki, T. Subjects: “…Sensor…”
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  19. 19

    Profiling pacing and underlying kinematics during 400m front-crawl with embedded sensors and functional modeling of large database: highlighting the technical regulations of various performance level

    Bouvet, A., Nicolas, G., Bideau, N.
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  20. 20

    Predicting running vertical ground reaction forces using neural network models based on an IMU sensor

    Li, S., Pan, J., Wang, D., Yuan, S., Yang, J., Hao, W.
    Published in Sensors (2025)
    Subjects: “…Sensor…”
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