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首页> 外文期刊>IEEE sensors journal >Personalized and Nonparametric Framework for Detecting Changes in Gait Cycles
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Personalized and Nonparametric Framework for Detecting Changes in Gait Cycles

机译：用于检测步态周期变化的个性化和非参数框架

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摘要

Gait analysis is a standard practice used by clinicians and researchers to identify abnormalities, examine disease progression, or assess the success of interventions. Traditionally, assessments were performed with visual inspection by a trained professional. However, with the recent breakthroughs in sensing technologies, there is a growing body of literature that uses features extracted from sensing data as inputs to machine learning methods. These models require a large representative sample of gait cycles labeled according to each category of interest (e.g. standard, anomalous) for model training. This paper provides a personalized, nonparametric statistical framework that can be used for detecting and interpreting gait changes in individuals while requiring only a small number of baseline gait cycles. This framework can be applied using the acceleration trajectory or features from a single Intertial Measurement Unit (IMU). The individualized framework does not require the gait cycles to be labeled and does not require the assumption that the observed patterns are consistent across subjects. The personalized framework is applied to gait cycles extracted from a material handling task that simulates moving heavy loads in a warehouse. Twelve subjects were monitored and significant changes in personalized gait patterns consistent with perceived exertion were observed. Further interpretation of the changes illustrates that participants exhibit individualized patterns in gait as they approach the fatigued state.
机译：步态分析是临床医生和研究人员使用的标准做法，以识别异常，检查疾病进展，或评估干预措施的成功。传统上，通过训练有素的专业人员进行评估。然而，随着近期传感技术的突破性，有一个越来越多的文献体，使用从传感数据提取的功能作为机器学习方法的输入。这些模型需要根据每类兴趣（例如标准，异常）的兴趣（例如标准，异常）标记的大型代表性样本。本文提供了一种个性化的非参数统计框架，可用于检测和解释个人的步态变化，同时只需要少量基线步态周期。可以使用来自单个第二个间隔测量单元（IMU）的加速轨迹或特征来应用该框架。个性化框架不需要标记步态周期，并且不需要假设观察到的模式跨对象一致。个性化框架应用于从物料处理任务中提取的步态周期，该任务模拟仓库中移动的重负荷。监测12个受试者，观察到与感知劳动一致的个性化步态模式的显着变化。进一步解释变化说明了参与者在步态中表现出各种形状的模式，因为它们接近疲劳状态。

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