Department of Computing and Control Engineering, Institute of Chemical Technology in Prague, 166 28, Prague, Czech Republic,
Med Biol Eng Comput. 2014 Apr;52(4):301-8. doi: 10.1007/s11517-013-1134-6. Epub 2013 Dec 24.
The monitoring of data from global positioning system (GPS) receivers and remote sensors of physiological and environmental data allow forming an information database for observed data processing. In this paper, we propose the use of such a database for the analysis of physical activities during cycling. The main idea of the proposed algorithm is to use cross-correlations between the heart rate and the altitude gradient to evaluate the delay between these variables and to study its time evolution. The data acquired during 22 identical cycling routes, each about 130 km long, include more than 6,700 segments of length 60 s recorded with varying sampling periods. General statistical and digital signal processing methods used include mathematical tools to reject gross errors, resampling using selected interpolation methods, digital filtering of noise signal components, and estimating cross-correlations between the position data and the physiological signals. The results of a regression between GPS and physiological data include the estimate of the time delay between the heart rate change and gradient altitude of about 7.5 s and its decrease during each training route.
全球定位系统 (GPS) 接收器和生理及环境数据远程传感器的数据监测可形成用于观测数据处理的信息数据库。在本文中,我们提出使用这样的数据库来分析骑自行车时的身体活动。所提出算法的主要思想是使用心率和海拔梯度之间的互相关来评估这两个变量之间的延迟,并研究其时间演变。在 22 条相同的骑行路线上采集的数据,每条路线约 130 公里长,包括用不同的采样周期记录的 6700 多个 60 秒长的片段。使用的一般统计和数字信号处理方法包括用于拒绝粗大误差的数学工具、使用选定的插值方法进行重采样、噪声信号分量的数字滤波以及估计位置数据和生理信号之间的互相关。GPS 和生理数据之间回归的结果包括对心率变化和梯度高度之间约 7.5 秒的时间延迟的估计,以及在每条训练路线上的时间延迟的减少。