Predicting ground reaction forces in running using micro-sensors and neural networks

Measurement of ground reaction force (GRF) in running provides a direct indication of the loads to which the body is subjected at each foot-ground contact, and can provide an objective explanation for performance outcomes. Traditionally, the collection of three orthogonal component GRF data in running requires an athlete to complete a series of return loops along a laboratory based runway, within which a force platform is embedded, in order to collect data from a discrete footfall. The major disadvantages associated with this GRF data collection methodology include the inability to assess multiple consecutive foot contacts and the fact that measurements are typically confined to the laboratory. The objective of this research was to investigate the potential for wearable instrumentation to be employed, in conjunction with artificial neural network (ANN) and multiple linear regression (MLR) models, for the estimation of GRF in middle distance running. A modular wearable data acquisition system was developed to acquire in-shoe force (ISF) data. Matched data sets from wearable instrumentation (source data) and force plate (target data) records were collected from elite middle-distance runners under controlled laboratory conditions for the purposes of ANN and MLR model development (MD) and model validation (MV). In terms of statistical measures of prediction accuracy the MLR model was found to provide a superior level of accuracy for the prediction of the vertical and medio-lateral components of GRF and alternatively, the ANN model provided the most accurate predictions of the anterior-posterior component of GRF. The prediction accuracy of each component of GRF was found to be governed by the inherent signal variability, in which case the vertical and anterior-posterior components were more reliable and subsequently predicted significantly more accurately than the medio-lateral component. The emerging capability for obtaining continuous GRF records from wearable instrumentation has the potential to permit unprecedented quantification of training stress and competition demands in running.     

划船式短跑途中跑技术上肢动作描述及其数据证明

本文引用相关数据证明以下“划船式短跑途中跑技术[1]”是世界级水平部分运动员使用的技术;而低于世界级水平部分优秀运动员使用的是经典短跑途中跑技术。     

中长跑运动员赛前训练的调控研究

运用实验法对参加全国第七届大学生运动会的江苏省大学生中长跑运动员和参加第十届全运会江苏队中长跑运动员的赛前训练期间的生理学指标进行跟踪测定、评价，对训练进行了调控。研究表明：用心率、心功能指数、尿蛋白、最大吸氧量、无氧功率、斯坦格试验与汉契试验、无氧闽等指标评定中长跑运动员赛前的机能状态有较强的实用性和针对性，但需结合项目的特点和训练实际，加强监控和调控，进行综合分析；中长跑赛前训练安排应以专项素质为主，突出速度和速度耐力的训练，速度力量在中长跑项目赛前训练要给予重视，负荷的安排是赛前训练的关键。     

NRF1基因多态性可预测中国北方地区汉族男性有氧能力的差异

目的：拟探讨核呼吸因子1（NRF1）基因多态性与最大（VO2max）和次最大有氧能力（running economy，RE）的关联性。方法：102名无训练史的健康男子进行18周有氧耐力训练，测试训练前后的VO2 max和12km／h跑速下的RE。用FCR-RFLP分析NRF1基因的SNPrs2302970、rs6949152和rs10500120多态性。结果：3个多态位点均符合H—W平衡（P〉0．05）。rs2302970与rs6949152强连锁不平衡（D’=0．950，12=0．631），经haplo．em推算，主要存在4种单体型；3个SNPs及单体型与VO2max的初始值及训练敏感性均不关联；RE的初始能力与rs2302970多态性关联，携带CT基因型的群体显著高于CC基因型。RE的训练敏感性与rs2302970和rs6949152多态性关联，携带CT或AG基因型的群体显著高于CC或AA基因型。结论：NRF1基因多态性能够预测中国北方地区汉族男性次最大有氧能力的差异。     

放松技术对提高100m跑成绩的实验研究

通过文献资料法、实验法、数理统计法等方法,对20名田径运动员进行实验研究,对实验组采用放松能力训练,对照组则采用传统的速度力量训练,结果表明,在100米起跑阶段,放松技术的作用并不显著,在途中跑和冲刺跑阶段,掌握放松技术对于提高成绩的作用显著,教练员在短跑的途中跑和冲刺跑训练中应适当地加入放松跑技术的训练,让运动员体会放松跑的概念,并运用到平时的训练与比赛中,提高100米跑的成绩。     

青少年男子400m跑项目分段计时训练的研究分析

运用文献资料法、数理统计法、对比分析法、实验法等方法对男子400m跑项目进行分段计时研究。研究结果：（1）分段计时跑很好地控制了运动员的节奏,合理的分配了体力,即在第一时段以中强度跑,第二时段以高强度跑,第三时段是冲刺阶段,但往往速度最慢;（2）分段计时训练的学生在心理上和体力上相比于传统训练的学生都占有优势;（3）分段计时法还可以帮助教练员对运动员的跑进行技术诊断;（4）提高运动员的成绩主要从运动员的速度、耐力、时间分配以及训练过程中能量的供应,制定合理的、科学的训练方法。     

Metabolic cost of running is greater on a treadmill with a stiffer running platform

Exercise testing on motorised treadmills provides valuable information about running performance and metabolism; however, the impact of treadmill type on these tests has not been investigated. This study compared the energy demand of running on two laboratory treadmills: an HP Cosmos (C) and a Quinton (Q) model, with the latter having a 4.5 times stiffer running platform. Twelve experienced runners ran identical bouts on these treadmills at a range of four submaximal velocities (reported data is for the velocity that approximated 75–81% VO_2max). The stiffer treadmill elicited higher oxygen consumption (C: 46.7 ± 3.8; Q: 50.1 ± 4.3 ml·kg^?1 · min^?1), energy expenditure (C: 16.0 ± 2.5; Q: 17.7 ± 2.9 kcal · min^?1), carbohydrate oxidation (C: 9.6 ± 3.1; Q: 13.0 ± 3.9 kcal · min^?1), heart rate (C: 155 ± 16; Q: 163 ± 16 beats · min^?1) and rating of perceived exertion (C: 13.8 ± 1.2; Q: 14.7 ± 1.2), but lower fat oxidation (C: 6.4 ± 2.3; Q: 4.6 ± 2.5 kcal · min^?1) (all analysis of variance treadmill comparisons P < 0.01). This study confirms that caution is required when comparing performance and metabolic results between different treadmills and suggests that treadmills will vary in their comparability to over-ground running depending on the running platform stiffness.     

Circadian rhythm effect on physical tennis performance in trained male players

To determine the effect of circadian rhythm on neuromuscular responses and kinematics related to physical tennis performance, after a standardised warm-up, 13 highly competitive male tennis players were tested twice for serve velocity/accuracy (SVA), countermovement vertical jump (CMJ), isometric handgrip strength (IS), agility T-test (AGIL) and a 10-m sprint (10-m RUN). In a randomised, counter-balance order, tennis players underwent the test battery twice, either in the morning (i.e., AM; 9:00 h) and in the afternoon (i.e., PM; 16:30 h). Paired t-tests were used to analyse differences due to time-of-day in performance variables. Comparison of morning versus afternoon testing revealed that SVA (168.5 ± 6.5 vs. 175.2 ± 6.1 km · h^?1; P = 0.003; effect size [ES] = 1.07), CMJ (32.2 ± 0.9 vs. 33.7 ± 1.1 cm; P = 0.018; ES = 1.46), AGIL (10.14 ± 0.1 vs. 9.91 ± 0.2 s; P = 0.007; ES = 1.23) and 10-m RUN time (1.74 ± 0.1 vs. 1.69 ± 0.1 s; P = 0.021; ES = 0.67) were significantly blunted during the morning testing. However, IS was not affected by time-of-day (P = 0.891). Thus, tennis performance may be reduced when competing in the morning in comparison to early evening. Therefore, coaches and tennis players should focus on schedule the SVA, power, speed and agility training sessions in the afternoon.     

析100m速度特征之加速跑距离及其步态变化

通过对世界优秀运动员100m速度特征及其相关报道的结论与分析得出结论：1当代100m跑加速跑距离的研究室一个误区,加速跑距离长是高水平运动员最大速度大的外在表现特征,不能通过调整加速步态而达到提高成绩的目的;2运动员加速能力强的特征是加速度大而不是加速距离长;3步态特征之步长步频的变化是运动员高速跑中的自然协调,步长和步频应该作为一个整体来理解才有意义,简单的定论步长与步频谁代表了短跑的发展方向是误导;4建议100m训练重点放在技术与最大速度训练上,尽可能的发展动作速度．     

罗伯斯打破110m栏世界纪录的技术特征分析

罗伯斯在短短的两年多时间内,通过不断提高跨栏步技术.使他在过栏过程中打栏次数越来越少,从而减少了过栏时间;除此之外,随着他的栏间节奏和速度耐力的进步,再加上他天生的爆发力,这是罗伯斯成功打破世界纪录的主要原因.但是罗伯斯过栏平均时间水平与世界优秀运动员存在一定差距,其速度耐力水平还有待于进一步提高.进一步改进、完善跨栏步技术,提高跑跨结合能力和速度耐力水平,应该成为罗伯斯今后训练的重点.