我院学生日常活动及膳食调查

为促进高校学生身心健康成长,随机抽取122名学生,对其进行日常活动及膳食情况的调查,结果显示,能量的消耗与摄入处于不平衡状态.对此提出了相应的对策与建议.     

体操运动员的心理应激与控制

应激是一种对运动员有害的心理反应。探讨体操运动员应激的成因及危害，并且提出一些有针对性的控制方法。     

论学校武术在素质教育中的作用与发展对策

以素质教育为立足点，论述武术在中小学实施素质教育中的地位、作用与存在的问题，并提出发展对策。     

试论篮球运动的节奏

现代篮球运动寓竞技化、智谋化、技艺化于一体,节奏正是这一趋势的最好体现.能否正确而深刻理解篮球运动的节奏并且运用节奏变化来争取比赛的主动权,是一个球队实力的反映,也是一个球队是否成熟的标志.运用文献资料、录像观察、逻辑分析等方法,对篮球运动节奏的表现形式、影响因素及节奏的运用等方面进行分析研究,为篮球运动训练中的节奏训练提供理论参考.     

Nakula’s extraordinary talent

An attempt is made here to provide a mathematical model which can explain an intriguing talent of Nakula. On the way, some simple and interesting properties of the real number system and related material are discussed.     

大学体育后进生的成因与对策研究

体育后进生是大学生中的一个特殊群体.研究这一特殊群体产生的原因,并从中探寻帮助他们摆脱后进的方法,以期为体育教学改革和素质教育提供参考依据.     

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.