Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France.
Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France.
Sensors (Basel). 2023 Apr 14;23(8):4000. doi: 10.3390/s23084000.
This paper presents a novel approach to creating a graphical summary of a subject's activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.
本文提出了一种新方法，可在半自由生活环境中创建主体在方案期间活动的图形摘要。有了这种新的可视化效果，人类行为，特别是运动，现在可以被压缩成一个易于阅读和用户友好的输出。由于在半自由生活环境中监测患者时收集的时间序列通常很长且很复杂，因此我们的贡献依赖于信号处理方法和机器学习算法的创新流水线。一旦学习完成，图形表示就能够总结数据中存在的所有活动，并可以快速应用于新获取的时间序列。简而言之，惯性测量单元的原始数据首先使用自适应变点检测程序分割成均匀的区间，然后自动标记每个区间。然后，从每个区间中提取特征，最后使用这些特征计算分数。最终的视觉摘要由活动的分数及其与健康模型的比较构建而成。这种图形输出是一种详细、自适应且结构化的可视化效果，可以帮助更好地理解复杂步态方案中的突出事件。
