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Validation of two hybrid approaches for clustering age-related groups based on gait kinematics data.
Medical Engineering & Physics ( IF 1.7 ) Pub Date : 2020-02-19 , DOI: 10.1016/j.medengphy.2020.02.001
Rafael Caldas 1 , Rebeca Sarai 2 , Fernando Buarque de Lima Neto 2 , Bernd Markert 1
Affiliation  

Age-associated changes in walking parameters are relevant to recognize functional capacity and physical performance. However, the sensible nuances of slightly different gait patterns are hardly noticeable by inexperienced observers. Due to the complexity of this evaluation, we aimed at verifying the efficiency of applied hybrid-adaptive algorithms to cluster groups with similar gait patterns. Based on self-organizing maps (SOM), k-means clustering (KM), and fuzzy c-means (FCM), we compared the hybrid algorithms to a conventional FCM approach to cluster accordingly age-related groups. Additionally, we performed a relevance analysis to identify the principal gait characteristics. Our experiments, based on inertial-sensors data, comprised a sample of 180 healthy subjects, divided into age-related groups. The outcomes suggest that our methods outperformed the FCM algorithm, demonstrating a high accuracy (88%) and consistent sensitivity also to distinguish groups that presented a significant difference (p < .05) only in one of the six observed gait features. The applied algorithms showed a compatible performance, but the SOM + KM required less computation cost and, therefore, was more efficient. Furthermore, the results indicate the overall importance of cadence, as a measurement of physical performance, especially when clustering subjects by their age. Such output provides valuable information to healthcare professionals, concerning the subject's physical performance related to his age, supporting and guiding the physical evaluation.

中文翻译:

验证两种基于步态运动学数据对年龄相关人群进行聚类的混合方法。

步行参数与年龄相关的变化与识别功能能力和身体表现有关。但是,没有经验的观察者几乎看不到步态略有不同的明智差异。由于评估的复杂性,我们旨在验证将混合自适应算法应用于步态相似的集群的效率。基于自组织图(SOM),k均值聚类(KM)和模糊c均值(FCM),我们将混合算法与常规FCM方法进行了比较,从而对年龄相关的人群进行了聚类。此外,我们进行了相关性分析以识别主要步态特征。我们基于惯性传感器数据的实验包括180位健康受试者的样本,分为年龄相关组。结果表明,我们的方法优于FCM算法,显示出较高的准确性(88%)和一致的敏感性,也可以区分仅在六个观察到的步态特征中表现出显着差异(p <.05)的组。应用的算法显示出兼容的性能,但是SOM + KM需要较少的计算成本,因此效率更高。此外,结果表明了节奏的总体重要性,作为衡量身体机能的指标,尤其是在按年龄对受试者进行分组时。这样的输出为医疗保健专业人员提供了有关受试者与年龄相关的身体表现的宝贵信息,支持并指导了身体评估。证明了很高的​​准确性(88%)和一致的敏感性,也可以区分仅在六个观察到的步态特征中表现出显着差异(p <.05)的组。应用的算法显示出兼容的性能,但是SOM + KM需要较少的计算成本,因此效率更高。此外,结果表明了节奏的总体重要性,作为衡量身体机能的指标,尤其是在按年龄对受试者进行分组时。这样的输出为医疗保健专业人员提供了有关受试者与年龄相关的身体表现的宝贵信息,支持并指导了身体评估。证明了较高的准确性(88%)和一致的敏感性,也可以区分仅在六个观察到的步态特征中表现出显着差异(p <.05)的组。应用的算法显示出兼容的性能,但是SOM + KM需要较少的计算成本,因此效率更高。此外,结果表明了节奏的整体重要性,作为身体表现的一种衡量标准,尤其是在按年龄分组受试者时。这样的输出为医疗保健专业人员提供了有关受试者与年龄相关的身体表现的宝贵信息,支持并指导了身体评估。但是SOM + KM需要较少的计算成本,因此效率更高。此外,结果表明了节奏的整体重要性,作为身体表现的一种衡量标准,尤其是在按年龄分组受试者时。这样的输出为医疗保健专业人员提供了有关受试者与年龄相关的身体表现的有价值的信息,支持并指导了身体评估。但是SOM + KM需要较少的计算成本,因此效率更高。此外,结果表明了节奏的整体重要性,作为身体表现的一种衡量标准,尤其是在按年龄分组受试者时。这样的输出为医疗保健专业人员提供了有关受试者与年龄相关的身体表现的宝贵信息,支持并指导了身体评估。
更新日期:2020-02-19
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