Horst Fabian, Slijepcevic Djordje, Simak Marvin, Horsak Brian, Schöllhorn Wolfgang Immanuel, Zeppelzauer Matthias
Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Mainz, Germany.
Institute of Creative Media Technologies, Department of Media & Digital Technologies, St. Pölten University of Applied Sciences, St. Pölten, Austria.
Comput Struct Biotechnol J. 2023 Jun 13;21:3414-3423. doi: 10.1016/j.csbj.2023.06.009. eCollection 2023.
Human gait is a complex and unique biological process that can offer valuable insights into an individual's health and well-being. In this work, we leverage a machine learning-based approach to model individual gait signatures and identify factors contributing to inter-individual variability in gait patterns. We provide a comprehensive analysis of gait individuality by (1) demonstrating the uniqueness of gait signatures in a large-scale dataset and (2) highlighting the gait characteristics that are most distinctive to each individual. We utilized the data from three publicly available datasets comprising 5368 bilateral ground reaction force recordings during level overground walking from 671 distinct healthy individuals. Our results show that individuals can be identified with a prediction accuracy of 99.3% by using the bilateral signals of all three ground reaction force components, with only 10 out of 1342 recordings in our test data being misclassified. This indicates that the combination of bilateral ground reaction force signals with all three components provides a more comprehensive and accurate representation of an individual's gait signature. The highest accuracy was achieved by (linear) Support Vector Machines (99.3%), followed by Random Forests (98.7%), Convolutional Neural Networks (95.8%), and Decision Trees (82.8%). The proposed approach provides a powerful tool to better understand biological individuality and has potential applications in personalized healthcare, clinical diagnosis, and therapeutic interventions.
人类步态是一个复杂而独特的生物过程，能够为了解个体的健康状况提供有价值的见解。在这项工作中，我们利用基于机器学习的方法来建模个体步态特征，并识别导致步态模式个体间差异的因素。我们通过（1）在大规模数据集中证明步态特征的独特性，以及（2）突出每个个体最具特色的步态特征，对步态个体性进行了全面分析。我们使用了来自三个公开可用数据集的数据，这些数据包含了671名不同健康个体在水平地面行走时的5368条双侧地面反作用力记录。我们的结果表明，通过使用所有三个地面反作用力分量的双侧信号，可以以99.3%的预测准确率识别个体，在我们的测试数据中1342条记录中只有10条被错误分类。这表明，包含所有三个分量的双侧地面反作用力信号的组合能够更全面、准确地呈现个体的步态特征。（线性）支持向量机的准确率最高（99.3%），其次是随机森林（98.7%）、卷积神经网络（95.8%）和决策树（82.8%）。所提出的方法为更好地理解生物个体性提供了一个强大的工具，在个性化医疗、临床诊断和治疗干预方面具有潜在应用价值。
