Mannini Andrea, Sabatini Angelo Maria
The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4369-73. doi: 10.1109/IEMBS.2011.6091084.
In this paper, we describe an application of hidden Markov models (HMMs) to the problem of time-locating specific events in normal gait movement patterns. The use of HMMs in this paper is mainly related to the opportunity they offer to segment gait data collected at different walking speeds and inclinations of the walking surface. A simple four-state left-right HMM is trained on a dataset of signals collected from a mono-axial gyro during treadmill walking trials performed at different speed and incline values. The gyro is mounted at the foot instep, with its sensitivity axis oriented in the medio-lateral direction. A rule based method applied to gyro signals is used for data annotation. Sensitivity and specificity of phase classification detection higher than 95% are obtained. The estimation accuracy of heel strike, flat foot, heel off and toe off events is about 35 ms on average.
在本文中，我们描述了隐马尔可夫模型（HMM）在正常步态运动模式中对特定事件进行时间定位问题上的一种应用。本文中HMM的使用主要与它们提供的机会相关，即对在不同步行速度和步行表面倾斜度下收集的步态数据进行分段。在一个数据集上训练了一个简单的四状态左右HMM，该数据集是在不同速度和倾斜度值的跑步机行走试验期间从单轴陀螺仪收集的信号。陀螺仪安装在脚背处，其敏感轴沿中外侧方向定向。一种应用于陀螺仪信号的基于规则的方法用于数据标注。获得了高于95%的相位分类检测灵敏度和特异性。脚跟撞击、平足、脚跟离地和脚趾离地事件的估计精度平均约为35毫秒。
