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Pueo, Basílio; Jiménez-Olmedo, José Manuel; Penichet-Tomás, Alfonso; Bernal-Soriano, Mari Carmen

doi:10.7752/jpes.2018.02121

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Una de las más importantes es que la aplicación requiere que el investigador seleccione visualmente los fotogramas de los momentos de despegue y aterrizaje, lo que puede dar lugar a un desacuerdo entre los evaluadores y, por tanto, a resultados potencialmente inexactos. Sin embargo, un estudio reciente mostró una buena concordancia entre calificadores entrenados y no entrenados para la evaluación de la altura del salto, lo que indica que la aplicación de vídeo tiene una buena fiabilidad entre calificadores (Pueo, Jimenez-Olmedo, Penichet-Tomas, & Bernal-Soriano, 2018). Otra desventaja, es el riesgo de que el sujeto prolongue de forma artificial el tiempo de vuelo debido a la flexión (dorsiflexión) de rodilla y tobillo justo antes del aterrizaje, lo que le daría un mayor tiempo de vuelo y por consiguiente una mayor altura del salto alcanzada (Gençoğlu, et al, 2023).…”

Section: Evaluación Del Salto Verticalunclassified

Evaluación y prescripción del salto vertical y horizontal en futbolistas. Revisión narrativa (Assessment and prescription of vertical and horizontal jumping in soccer players. Narrative review)

Fernández-Galván,

Casado,

Domínguez

2024

Retos

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Las acciones balísticas, tales como el salto, son determinantes en el resultado de un partido de fútbol. Por lo tanto, su evaluación, cuantificación y optimización podrían ser concluyentes para el deportista. El objetivo del presente trabajo de revisión narrativa ha sido detallar los tipos de saltos verticales y horizontales más usados y confiables para la evaluación de las acciones balísticas, exponiendo la definición de los protocolos y sugiriendo metodologías de entrenamiento basadas en la evidencia científica. La metodología siguió las recomendaciones PRISMA, para ello se realizó una búsqueda bibliográfica en las bases de datos de Pubmed y Web of Science. Los resultados mostraron que el salto vertical y el salto con contra movimiento, son los saltos más utilizados para la evaluación y optimización del rendimiento, así como el salto horizontal con contra movimiento con las manos fijadas a la cintura y con las manos libres. Se ha observado correlaciones significativas con efectos moderados entre los valores de sprint de 5 m y 15 m con el salto vertical (r = -0,47 a -0,54; p < 0,01) y con el salto horizontal (r = -0,60 a -0,68; p < 0,01). Siendo el salto horizontal, mejor predictor en el sprint de 20 m que el salto vertical (r = -0,73 a -0,86 vs -0,52 a -0,73 p < 0,01). Por tanto, se concluye que la evaluación de la altura del salto vertical u horizontal puede ayudar a los profesionales del deporte a planificar los estímulos más apropiados para la optimización del rendimiento. Palabras clave: Acciones balísticas, salto en cuclillas, salto con contra movimiento y fútbol. Abstract. Ballistic actions, such as jumping, are decisive in the outcome of a soccer match. Therefore, their evaluation, quantification and optimisation could be determinant for the athlete. Thus, the aim of the present narrative review work has been to detail the most used and reliable types of vertical and horizontal jumps for the evaluation of ballistic actions, exposing the definition of the protocols and suggesting training methodologies based on scientific evidence. The methodology followed the PRISMA recommendations for which a bibliographic search was carried out in Pubmed and Web of Science databases. Results showed that vertical jump (SJ) and the countermovement jump (CMJ) are the most commonly used jumps for the evaluation and optimisation of performance, as well as the horizontal jump with countermovement with the hands fixed at the waist and with the hands free. Significant correlations with moderate effects were observed between 5 m and 15 m sprint values and vertical jump (r = -0.47 to -0.54, p < 0.01) and horizontal jump (r = -0.60 to -0.68, p < 0.01). Horizontal jump being a better predictor of the 20 m sprint than vertical jump (r = -0.73 to -0.86 vs -0.52 to -0.73 p < 0.01). Therefore, it is concluded that the evaluation of the height of the vertical or horizontal jump can help sports professionals to plan the appropriate stimuli for the optimization of performance. Keywords: Ballistic actions, squat jump, countermovement jump, soccer and football.

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Section: Evaluación Del Salto Verticalunclassified

Evaluación y prescripción del salto vertical y horizontal en futbolistas. Revisión narrativa (Assessment and prescription of vertical and horizontal jumping in soccer players. Narrative review)

Fernández-Galván,

Casado,

Domínguez

2024

Retos

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“…Finally, video technology has led to the rise of applications aimed at controlling velocity and execution in weightlifting exercises (Balsalobre-Fernández et al, 2017;Balsalobre-Fernández, Marchante, Muñoz-López, & Jiménez, 2018). Despite the ease of use of this type of analysis, an analysis of the video after the recording must be performed, which requires a correct observation protocol with experienced observers (Pueo, Jimenez-Olmedo, Penichet-Tomas, & Bernal-Soriano, 2018). For this reason, the use of high-speed video has led to the development of analytical methodologies that have been validated for the analysis of flight time in vertical jumps (Balsalobre-Fernández, Tejero-González, Campo-Vecino, & Bavaresco, 2014).…”

Section: Introductionmentioning

confidence: 99%

Validity and reliability of smartphone high-speed camera and Kinovea for velocity-based training measurement

Jiménez-Olmedo¹,

Penichet-Tomás²,

Villalon-Gasch³

et al. 2020

jhse

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The aim of this study was to validate the combination of smartphone high-speed camera and motion analysis software Kinovea methodology (SHSC-Kinovea) to measure kinematic variables of velocity-based training during back squat exercises. Fifteen athletes were voluntarily recruited for the study (age 22.8 ± 2.9 years, height 182.9 ± 8.9 cm, body mass 79.5 ± 9.6 kg). High-speed video recordings with a smartphone at 240 fps were used against a criterion linear force transducer (LPT) for measuring displacement of the barbell (RB), mean velocity (MV), maximum velocity (Vmax) and concentric phase time (CPT). The intra-class correlations coefficient between LPT and SHSC-Kinovea showed almost perfect agreement for consistency (.992, .995, .997, .993) and absolute agreement (.975, .978, .980, .964) for RB, MV, Vmax and CPT, respectively. The mean differences between instruments were 1.11 mm for RB, 0.03 m/s for MV, 0.05 m/s for Vmax and 65.91 ms for CPT, all p < .001. Bland-Altman plots showed low systematic bias ± random error for RB: 1.11 ± 1.50 cm (r 2 : .006), MV: 0.03 ± 0.33 m/s (r 2 : .001) and Vmax: 65.91 ± 63.82 m/s (r 2 : .11), whereas Vmax showed overestimation for the high range of measures: 0.55 ± 0.42 m/s (r 2 : .31). Pearson's product moment correlation coefficient showed almost perfect association between all variables: (r = .985-.990) (p < .001). The SHSC-Kinovea methodology resulted in similar kinematic values than criterion so it can be considered as a trustworthy instrument for measuring velocity-based training.

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Machine learning aided jump height estimate democratization through smartphone measures

Mascia

Lazzari²,

Camomilla³

2023

Front. Sports Act. Living

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IntroductionThe peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively embed inertial sensors.MethodsFor this purpose, 43 participants performed 4 countermovement jumps (172 in total) on two force platforms (gold standard). While jumping, participants held a smartphone in their hands, whose inertial sensor measures were recorded. After peak height was computed for both instrumentations, twenty-nine features were extracted, related to jump biomechanics and to signal time-frequency characteristics, as potential descriptors of soft tissues or involuntary arm swing artifacts. A training set (129 jumps – 75%) was created by randomly selecting elements from the initial dataset, the remaining ones being assigned to the test set (43 jumps – 25%). On the training set only, a Lasso regularization was applied to reduce the number of features, avoiding possible multicollinearity. A multi-layer perceptron with one hidden layer was trained for estimating the jump height from the reduced feature set. Hyperparameters optimization was performed on the multi-layer perceptron using a grid search approach with 5-fold cross validation. The best model was chosen according to the minimum negative mean absolute error.ResultsThe multi-layer perceptron greatly improved the accuracy (4 cm) and precision (4 cm) of the estimates on the test set with respect to the raw smartphone measures estimates (18 and 16 cm, respectively). Permutation feature importance was performed on the trained model in order to establish the influence that each feature had on the outcome. The peak acceleration and the braking phase duration resulted the most influential features in the final model. Despite not being accurate enough, the height computed through raw smartphone measures was still among the most influential features.DiscussionThe study, implementing a smartphone-based method for jump height estimates, paves the way to method release to a broader audience, pursuing a democratization attempt.

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Untitled

Cited by 3 publications

References 13 publications

Evaluación y prescripción del salto vertical y horizontal en futbolistas. Revisión narrativa (Assessment and prescription of vertical and horizontal jumping in soccer players. Narrative review)

Evaluación y prescripción del salto vertical y horizontal en futbolistas. Revisión narrativa (Assessment and prescription of vertical and horizontal jumping in soccer players. Narrative review)

Validity and reliability of smartphone high-speed camera and Kinovea for velocity-based training measurement

Machine learning aided jump height estimate democratization through smartphone measures

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