The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 33, 56127 Pisa, Italy.
Gait Posture. 2012 Sep;36(4):657-61. doi: 10.1016/j.gaitpost.2012.06.017. Epub 2012 Jul 15.
In this paper we present a classifier based on a hidden Markov model (HMM) that was applied to a gait treadmill dataset for gait phase detection and walking/jogging discrimination. The gait events foot strike, foot flat, heel off, toe off were detected using a uni-axial gyroscope that measured the foot instep angular velocity in the sagittal plane. Walking/jogging activities were discriminated by processing gyroscope data from each detected stride. Supervised learning of the classifier was undertaken using reference data from an optical motion analysis system. Remarkably good generalization properties were achieved across tested subjects and gait speeds. Sensitivity and specificity of gait phase detection exceeded 94% and 98%, respectively, with timing errors that were less than 20 ms, on average; the accuracy of walking/jogging discrimination was approximately 99%.
在本文中，我们提出了一种基于隐马尔可夫模型（HMM）的分类器，该分类器应用于步态跑步机数据集，用于步态阶段检测和行走/慢跑区分。使用测量矢状面足背角速度的单轴陀螺仪检测步态事件，包括足触地、足放平、脚跟离地和脚趾离地。通过处理每个检测到的步幅的陀螺仪数据来区分行走/慢跑活动。使用来自光学运动分析系统的参考数据进行分类器的监督学习。在测试的受试者和步态速度范围内，分类器表现出了非常好的泛化能力。步态阶段检测的灵敏度和特异性分别超过 94%和 98%，平均定时误差小于 20ms；行走/慢跑区分的准确率约为 99%。
