Background and Objective

Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes.

Methods

While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion.

Results

With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes.

Conclusions

A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.

中文翻译：

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人类行走是网络医学问题吗？使用符号回归模型和遗传编程进行分析

背景和目的

通常使用放置在下背部或臀部的传感器来评估人类行走。这些分析常常忽略了手臂、腿和身体躯干的运动在行走过程中都起着重要作用。换句话说，这些带有加速度计的身体节点形成了身体传感器网络（BSN）。BSN是指人体上收集生理信号的可穿戴传感器或设备网络。我们的研究提出，人类运动可以被视为一个由连接良好的节点组成的网络。

方法

虽然假设加速度计数据可以模拟该 BSN，但我们从执行认知任务的 10 名健康参与者的六个身体区域收集了加速度计信号。基于遗传编程的机器学习被用来产生一系列人类运动的非线性符号模型。

结果

对于精准医学的影响，我们的主要发现是，与所有其他模型相比，我们的 BSN 模型能够拟合来自下背部加速度计的数据，并能最好地描述特定于受试者的数据。在不同的受试者中，由于人体尺寸的多样性，模型的效果较差。

结论

所有六个身体节点之间的 BSN 关系已被证明可以描述特定于受试者的数据，这表明这些节点之间的网络医学关系对于充分描述人类行走至关重要。我们的步态分析可用于多种临床应用，例如医学诊断以及为有或没有认知负荷的健康行走创建基线。