Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy.
Comput Math Methods Med. 2013;2013:343084. doi: 10.1155/2013/343084. Epub 2013 Nov 27.
Two approaches to the classification of different locomotor activities performed at various speeds are here presented and evaluated: a maximum a posteriori (MAP) Bayes' classification scheme and a Support Vector Machine (SVM) are applied on a 2D projection of 16 features extracted from accelerometer data. The locomotor activities (level walking, stair climbing, and stair descending) were recorded by an inertial sensor placed on the shank (preferred leg), performed in a natural indoor-outdoor scenario by 10 healthy young adults (age 25-35 yrs.). From each segmented activity epoch, sixteen features were chosen in the frequency and time domain. Dimension reduction was then performed through 2D Sammon's mapping. An Artificial Neural Network (ANN) was trained to mimic Sammon's mapping on the whole dataset. In the Bayes' approach, the two features were then fed to a Bayes' classifier that incorporates an update rule, while, in the SVM scheme, the ANN was considered as the kernel function of the classifier. Bayes' approach performed slightly better than SVM on both the training set (91.4% versus 90.7%) and the testing set (84.2% versus 76.0%), favoring the proposed Bayes' scheme as more suitable than the proposed SVM in distinguishing among the different monitored activities.
最大后验 (MAP) 贝叶斯分类方案和支持向量机 (SVM)。将从放置在小腿（惯用腿）上的惯性传感器记录的运动活动（水平行走、爬楼梯和下楼梯）的加速度计数据中提取的 16 个特征的 2D 投影应用于这两种方法。这些运动活动是在自然的室内外环境中由 10 位健康的年轻成年人（年龄 25-35 岁）进行的。从每个分段的活动时段中，选择了 16 个频域和时域特征。然后通过 2D Sammon 映射进行降维。人工神经网络 (ANN) 经过训练可以模仿 Sammon 映射在整个数据集上的行为。在贝叶斯方法中，将这两个特征输入到一个贝叶斯分类器中，该分类器包含一个更新规则，而在 SVM 方案中，ANN 被视为分类器的核函数。在训练集（91.4%对 90.7%）和测试集（84.2%对 76.0%）上，贝叶斯方法的表现均略优于 SVM，这表明所提出的贝叶斯方案比所提出的 SVM 更适合区分不同的监测活动。
