文章基本信息

标题：On Expressive Features for Gait Analysis using Lower Limb Inertial Sensor Data
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作者：Felix Laufer ; Michael Lorenz ; Bertram Taetz 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：15990-15997
DOI：10.1016/j.ifacol.2020.12.396
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, we present a method to obtain explicit, expressive and interpretable gait feature signals from an inertial sensor, mounted on any segment of the lower limbs. The proposed method is invariant to the mounting orientation of the sensor, works without magnetometer information, requires no prior knowledge and can be used in real-time scenarios. Moreover, the constructed signals are robust for a wide variety of changing walking speeds and directions. We investigate the informational content of our three feature signals lying in the human sagittal plane with respect to the gait phase segmentation problem and compare them to other commonly used signals, such as the sagittal angular velocity and the norms of accelerations and angular velocities. To this end, we make use of the filter-based maximum relevance minimum redundancy algorithm, which is a classifier-independent feature selection method. For validating our approach, we consider gait data of twelve healthy subjects walking straight and in curves at self-chosen speeds with inertial sensors attached to either the thigh, shank or foot. Additionally, pressure measuring insoles are used to obtain ground truth toe-off and heel-strike gait events for reference. With those events as the gait phase transitions, the event detection is cast into a classification problem. To support the theoretical findings of the feature selection and ranking, we finally evaluate different choices of feature sets with a simple linear support vector machine classifier in an online fashion and obtain superior segmentation results with our feature signals.
关键词：KeywordsGait segmentationbio-signals analysisinterpretationhuman body motion captureinformationsensor fusionmotion estimationparameterstate estimationinertial sensorssignal processingmachine learning