Support vector machine for classification of walking conditions using miniature kinematic sensors
Support vector machine for classification of walking conditions using miniature kinematic sensors
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DOI：
10.1007/s11517-008-0327-x
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发表时间：
2008-06-01
影响因子：
3.2
通讯作者：
Zhu, Hailong
中科院分区：
文献类型：
--
作者：
Lau, Hong-Yin;Tong, Kai-Yu;Zhu, Hailong
一种用于日常生活活动评估的便携式步态分析和活动监测系统可以促进临床和研究。本研究开发了一种由加速度计和陀螺仪组成的小型传感器单元，用于检测不同行走条件下小腿和足部的运动和方向。利用预摆阶段获得的运动学数据，对爬楼梯、下楼梯、平地、上坡和下坡五种行走状态进行分类。运动学数据包括从小腿和足段测量的前后加速度和角速度。采用支持向量机（SVM）作为机器学习技术对行走状态进行分类。并与人工神经网络（ANN）、径向基函数网络（RBF）、贝叶斯信念网络（BBN）等其他机器学习方法进行了比较。结果表明，支持向量机的分类性能优于其他三种方法。基于支持向量机的研究结果表明，使用附着在腿段上的单个传感器单元，可以区分楼梯上升和楼梯下降以及其他行走状态，准确率为100%。对于五种步行条件下的分类结果，使用来自小腿传感器单元的运动信号的性能从78%提高到添加来自足部传感器单元的信号的84%。基于便携式运动传感器单元的支持向量机技术可以自动识别行走状态，定量分析运动模式。
A portable gait analysis and activity-monitoring system for the evaluation of activities of daily life could facilitate clinical and research studies. This current study developed a small sensor unit comprising an accelerometer and a gyroscope in order to detect shank and foot segment motion and orientation during different walking conditions. The kinematic data obtained in the pre-swing phase were used to classify five walking conditions: stair ascent, stair descent, level ground, upslope and downslope. The kinematic data consisted of anterior-posterior acceleration and angular velocity measured from the shank and foot segments. A machine learning technique known as support vector machine (SVM) was applied to classify the walking conditions. SVM was also compared with other machine learning methods such as artificial neural network (ANN), radial basis function network (RBF) and Bayesian belief network (BBN). The SVM technique was shown to have a higher performance in classification than the other three methods. The results using SVM showed that stair ascent and stair descent could be distinguished from each other and from the other walking conditions with 100% accuracy by using a single sensor unit attached to the shank segment. For classification results in the five walking conditions, performance improved from 78% using the kinematic signals from the shank sensor unit to 84% by adding signals from the foot sensor unit. The SVM technique with the portable kinematic sensor unit could automatically recognize the walking condition for quantitative analysis of the activity pattern.