There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualizing each individual's muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

中文翻译：

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揭示每个人肌肉激活特征的独特特征

越来越多的证据表明，每个人都有独特的运动模式或特征。然而，这些运动特征的确切来源仍然未知。我们开发了一种方法，可以在两个运动任务（步行和踩踏）中识别个体肌肉激活特征。使用线性支持向量机根据他们在八块下肢肌肉上测量的肌电图 (EMG) 模式对 78 名参与者进行分类。为了深入了解机器学习分类模型的决策，实施了分层相关性传播 (LRP) 方法。这使得模型预测能够分解为每个单独输入值的相关性分数。换句话说，它提供了关于随时间变化的 EMG 配置文件的哪些特征对每个人来说是独一无二的信息。通过广泛的测试，我们已经表明 LRP 结果，以及在一定程度上激活签名，在条件之间和跨天高度一致。此外，它们受用于训练模型的数据集的影响最小。此外，我们提出了一种可视化每个人肌肉激活特征的方法，该方法具有多种潜在的临床和科学应用。这是第一项提供个体肌肉激活特征存在的确凿证据的研究。它具有多种潜在的临床和科学应用。这是第一项提供个体肌肉激活特征存在的确凿证据的研究。它具有多种潜在的临床和科学应用。这是第一项提供个体肌肉激活特征存在的确凿证据的研究。