Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 5 de 5
Mais filtros

Base de dados
Intervalo de ano de publicação
J Sports Sci ; 38(1): 53-61, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31623521


This study aimed 1) to examine the validity of inertial measurement unit (IMU)-based hip flexion strength test, and 2) to investigate the hip flexion strength test as an indicator of sprint performance. Eight males performed five repeated hip flexion-extension, while leg motion was recorded using an IMU and a motion capture system (Mocap). As the second experiment, 24 male athletes performed the IMU-based hip flexion strength test and sprinted 50 m, during which step-to-step ground reaction force (GRF) was recorded. The strength test variables were calculated using IMU and Mocap data including angular impulse, mean moment, and positive and negative work and power. Using GRF data, step-to-step spatiotemporal variables were obtained. The results showed high intra-class correlation coefficient and correlation coefficient (both >0.909) between IMU and Mocap for angular impulse, mean moment, positive work and power. The hip flexion mean moment showed significant correlation with running speed from the 5th-8th step section onwards. The angular impulse, mean moment, positive work and power are recommended to be used for the IMU-based hip flexion strength test variables in terms of accuracy and validity. Moreover, the proposed IMU-based hip flexion strength test can be an indicator for better sprinting performance.

Desempenho Atlético/fisiologia , Teste de Esforço/métodos , Quadril/fisiologia , Força Muscular/fisiologia , Corrida/fisiologia , Aceleração , Adulto , Fenômenos Biomecânicos , Humanos , Masculino , Reprodutibilidade dos Testes , Estudos de Tempo e Movimento , Adulto Jovem
Physiol Meas ; 41(1): 01NT02, 2020 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-31851953


OBJECTIVE: Length-tension relationships are widely reported in research, rehabilitation and performance settings; however, several isometric contractions at numerous angles are needed to understand these muscular outputs. Perhaps a more efficient way to determine torque-angle characteristics is via isokinetic dynamometry; however, little is known about the variability of isokinetic measurements besides peak torque and optimal angle. This paper examines the variability of angle-specific isokinetic torque and impulse measures. APPROACH: Three sessions of concentric (60°·s-1) knee extensions were performed by both limbs of 32 participants. Assessments were repeated on three occasions, separated by 5-8 d. To quantify variability, the standardized typical error of measurement (TEM) was doubled and thresholds of 0.2-0.6 (small), 0.6-1.2 (moderate), 1.2-2.0 (large), 2.0-4.0 (very large) and >4.0 (extremely large) were applied. Additionally, variability was deemed large when the intraclass correlation coefficient (ICC) was <0.67 and coefficient of variation (CV) > 10%; moderate when ICC > 0.67 or CV < 10% (but not both); and small when both ICC > 0.67 and CV < 10%. MAIN RESULTS: Isokinetic torque and angular impulse show small to medium variability (ICC = 0.75-0.96, CV = 6.4%-15.3%, TEM = 0.25-0.53) across all but the longest (100°) and shortest (10°) muscle lengths evaluated. However, moderate to large variability was found for the optimal angle (ICC = 0.58-0.64, CV = 7.3%-8%, TEM = 0.76-0.86), and torque and impulse at the beginning and end of the range of motion (ICC = 0.57-0.85, CV = 11-42.9%, TEM = 0.40-0.89). Intersession variability of isokinetic torque and impulse were small to moderate at medium (90-20°) joint angles. SIGNIFICANCE: Researchers and practitioners can examine the muscle torque-angle relationship and activity-specific torque outputs within these ranges, without resorting to more strenuous and time-consuming isometric evaluations.

J Strength Cond Res ; 2019 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-31593034


Oranchuk, DJ, Storey, AG, Nelson, AR, Neville, JG, and Cronin, JB. Variability of multiangle isometric force-time characteristics in trained men. J Strength Cond Res XX(X): 000-000, 2019-Measurements of isometric force, rate of force development (RFD), and impulse are widely reported. However, little is known about the variability and reliability of these measurements at multiple angles, over repeated testing occasions in a homogenous, resistance-trained population. Thus, understanding the intersession variability of multiangle isometric force-time characteristics provides the purpose of this article. Three sessions of isometric knee extensions at 40°, 70°, and 100° of flexion were performed by 26 subjects across 51 limbs. All assessments were repeated on 3 occasions separated by 5-8 days. Variability was qualified by doubling the typical error of measurement (TEM), with thresholds of 0.2-0.6 (small), 0.6-1.2 (moderate), 1.2-2.0 (large), 2.0-4.0 (very large), and >4.0 (extremely large). In addition, variability was deemed large when the intraclass correlation coefficient (ICC) was <0.67 and coefficient of variation (CV) >10%; moderate when ICC >0.67 or CV <10% (but not both); and small when both ICC >0.67 and CV <10%. Small to moderate between-session variability (ICC = 0.68-0.95, CV = 5.2-18.7%, TEM = 0.24-0.49) was associated with isometric peak force, regardless of angle. Moderate to large variability was seen in early-stage (0-50 ms) RFD and impulse (ICC = 0.60-0.80, CV = 22.4-63.1%, TEM = 0.62-0.74). Impulse and RFD at 0-100 ms, 0-200 ms, and 100-200 ms were moderately variable (ICC = 0.71-0.89, CV = 11.8-42.1%, TEM = 0.38-0.60) at all joint angles. Isometric peak force and late-stage isometric RFD and impulse measurements were found to have low intersession variability regardless of joint angle. However, practitioners need to exercise caution when making inferences about early-stage RFD and impulse measures due to moderate-large variability.

J Sports Sci ; 37(11): 1220-1226, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30543315


Fast bowlers are at a high risk of overuse injuries. There are specific bowling frequency ranges known to have negative or protective effects on fast bowlers. Inertial measurement units (IMUs) can classify movements in sports, however, some commercial products can be too expensive for the amateur athlete. As a large number of the world's population has access to an IMU (e.g. smartphones), a system that works on a range of different IMUs may increase the accessibility of automated workload monitoring in sport. Seventeen elite fast bowlers in a training setting were used to train and/or validate five machine learning models by bowling and performing fielding drills. The accuracy of machine learning models trained using data from all three bowling phases (pre-delivery, delivery and post-delivery) were compared to those trained using only the delivery phase at a sampling rate of 250 Hz. Next, models were trained using data down-sampled to 125 Hz, 50 Hz, and 25 Hz to mimic results from lower specification sensors. Models trained using only the delivery phase showed similar accuracy (> 95%) to those trained using all three bowling phases. When delivery-phase data were down-sampled, the accuracy was maintained across all models and sampling frequencies (>96%).

Monitores de Aptidão Física , Aprendizado de Máquina , Destreza Motora/fisiologia , Condicionamento Físico Humano/instrumentação , Esportes/fisiologia , Acelerometria/instrumentação , Fenômenos Biomecânicos , Estudos Transversais , Desenho de Equipamento , Humanos , Masculino , Movimento , Adulto Jovem
Med Sci Sports Exerc ; 50(12): 2595-2602, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30048411


INTRODUCTION: Accurately monitoring 24-h movement behaviors is a vital step for progressing the time-use epidemiology field. Past accelerometer-based measurement protocols are either hindered by lack of wear time compliance, or the inability to accurately discern activities and postures. Recent work has indicated that skin-attached dual-accelerometers exhibit excellent 24-h uninterrupted wear time compliance. This study extends this work by validating this system for classifying various physical activities and sedentary behaviors in children and adults. METHODS: Seventy-five participants (42 children) were equipped with two Axivity AX3 accelerometers; one attached to their thigh, and one to their lower back. Ten activity trials (e.g., sitting, standing, lying, walking, running) were performed while under direct observation in a lab setting. Various time- and frequency-domain features were computed from raw accelerometer data, which were then used to train a random forest machine learning classifier. Model performance was evaluated using leave-one-out cross-validation. The efficacy of the dual-sensor protocol (relative to single sensors) was evaluated by repeating the modeling process with each sensor individually. RESULTS: Machine learning models were able to differentiate between six distinct activity classes with exceptionally high accuracy in both adults (99.1%) and children (97.3%). When a single thigh or back accelerometer was used, there was a pronounced drop in accuracy for nonambulatory activities (up to a 26.4% decline). When examining the features used for model training, those that took the orientation of both sensors into account concurrently were more important predictors. CONCLUSIONS: When previous wear time compliance results are taken together with our findings, it represents a promising step forward for monitoring and understanding 24-h time-use behaviors. The next step will be to examine the generalizability of these findings in a free-living setting.

Acelerometria/métodos , Exercício , Aprendizado de Máquina , Acelerometria/instrumentação , Adolescente , Adulto , Dorso , Criança , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Teóricos , Comportamento Sedentário , Coxa da Perna