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首页> 外文期刊>Biocybernetics and biomedical engineering >Human gait pattern changes detection system: A multimodal vision-based and novelty detection learning approach
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Human gait pattern changes detection system: A multimodal vision-based and novelty detection learning approach

机译：人体步态模式改变检测系统：基于多模式视觉和新奇检测方法

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Abstract This paper proposes a novel gait rehabilitation analysis system, based on an innovative multimodal vision-based sensor setup, focused on detecting gait pattern changes over time. The proposed setup is based on inexpensive technologies, without compromising performance, and was designed to be deployed on walkers, which are a typical assistive aid used in gait rehabilitation. In the medical field the evaluation of a patient's rehabilitation progress is typically performed by a medical professional through subjective techniques based on the professional's visual perception and experience. In this context, we are proposing an automatic system to detect the progress of patients undergoing rehabilitation therapy. Our approach is able to perform novelty detection for gait pattern classification based on One-Class Support Vector Machines (OC-SVM). Using point-cloud and RGB-D data, we detect the lower limbs (waist, legs and feet) by using Weighted Kernel-Density Estimation and Weighted Least-Squares to segment the legs into several parts (thighs and shins), and by fitting 3D ellipsoids to model them. Feet are detected using k -means clustering and a Circular Hough Transform. A temporal analysis of the feet's depth is also performed to detect heel strike events. Spatiotemporal and kinematic features are extracted from the lower limbs’ model and fed to a classifier based on the fusion of several OC-SVMs. Experiments with volunteers using the robotic walker platform ISR-AIWALKER, where the proposed system was deployed, revealed a lower limbs tracking accuracy of 3° and that our novelty detection approach successfully identified novel gait patterns, evidencing an overall 97.89% sensitivity.
机译：摘要本文提出了一种新的步态康复分析系统，基于创新的多模式视觉视觉传感器设置，重点是检测步态模式随时间的变化。建议的设置基于廉价的技术，而不会损害性能，旨在部署在步行者上，这是一种用于步态康复的典型辅助辅助。在医学领域，患者康复进展的评估通常通过基于专业人员的视觉感知和经验的主观技术来执行医疗专业人员。在这种情况下，我们提出了一种自动系统，以检测接受康复治疗的患者的进展情况。我们的方法能够基于单级支持向量机（OC-SVM）对步态模式分类进行新颖的检测。使用点云和RGB-D数据，我们通过使用加权内核密度估计和加权最小二乘来检测下肢（腰部，腿和脚），以将腿部分割成几个部分（大腿和胫），并通过配件3D椭圆体模拟它们。使用k -means聚类和圆形霍夫变换来检测脚。还执行了对脚深度的时间分析以检测脚跟罢工事件。从下肢模型中提取了时尚和运动学特征，并根据几种OC-SVM的融合进入分类器。使用机器人Walker平台ISR-Aiwalker的志愿者进行了实验，其中提出了建议的系统，揭示了3°的较低肢体跟踪精度，并且我们的新颖性检测方法成功地确定了新型步态模式，敏感度为97.89％。

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