Bialystok University of Technology, Faculty of Mechanical Engineering, Bialystok, Poland.
University of Bialystok, Institute of Computer Science, Bialystok, Poland.
Sci Data. 2023 Jan 30;10(1):60. doi: 10.1038/s41597-023-01964-z.
For years, researchers have been recognizing patterns in gait for purposes of medical diagnostics, rehabilitation, and biometrics. A method for observing gait is to measure ground reaction forces (GRFs) between the foot and solid plate with tension sensors. The presented dataset consists of 13,702 measurements of bipedal GRFs of one step of normal gait of 324 students wearing shoes of various types. Each measurement includes raw digital signals of two force plates. A signal comprises stance-related samples but also preceding and following ones, in which one can observe noise, interferences, and artifacts caused by imperfections of devices and walkway. Such real-world time series can be used to study methods for detecting foot-strike and foot-off events, and for coping with artifacts. For user convenience, processed data are also available, which describe only the stance phase of gait and form ready-to-use patterns suitable for experiments in GRF-based recognition of persons and footwear, and for generating synthetic GRF waveforms. The dataset is accompanied by Matlab and Python programs for organizing and validating data.
多年来，研究人员一直在通过步态模式识别来实现医学诊断、康复和生物识别等目的。一种观察步态的方法是使用张力传感器测量脚和固体板之间的地面反作用力（GRF）。本数据集包含 324 名穿鞋学生的正常步态一步的 13702 次双足 GRF 测量值。每个测量值都包括两个测力板的原始数字信号。一个信号包含与站立相关的样本，但也包括之前和之后的样本，在这些样本中可以观察到由于设备和步道的不完美而导致的噪声、干扰和伪影。这样的真实世界时间序列可用于研究检测足触地和足离地事件的方法，并处理伪影。为了方便用户，还提供了经过处理的数据，这些数据仅描述步态的站立阶段，并形成了可用于基于 GRF 的人员和鞋类识别以及生成合成 GRF 波形的实验的即用型模式。该数据集附有用于组织和验证数据的 Matlab 和 Python 程序。
