Department of Applied Electronics, Roma Tre University, Rome, Italy.
Med Eng Phys. 2010 Oct;32(8):849-59. doi: 10.1016/j.medengphy.2010.05.009. Epub 2010 Jun 23.
An accelerometer-based system able to classify among different locomotor activities during real life conditions is here presented, and its performance evaluated. Epochs of walking at different speeds, and with different slopes, and stair descending and ascending, are detected, segmented, and classified by using an adaptation of a naïve 2D-Bayes classifier, which is updated on-line through the history of the estimated activities, in a Kalman-based scheme. The feature pair used for classification is mapped from an ensemble of 16 features extracted from the accelerometer data for each activity epoch. Two different versions of the classifier are presented to combine the multi-dimensional nature of the accelerometer data, and their results are compared in terms of correct recognition rate of the segmented activities, on two population samples of different age. The classification algorithm achieves correct classification rates higher than 90% and higher than 92%, for young and elderly adults, respectively.
这里提出了一种基于加速度计的系统，能够在现实生活条件下对不同的运动活动进行分类，并对其性能进行评估。使用一种经过改进的朴素 2D-Bayes 分类器，通过基于卡尔曼滤波的方案，对不同速度、不同坡度的行走、下楼梯和上楼梯的活动进行检测、分割和分类，该分类器通过所估计活动的历史进行在线更新。用于分类的特征对是从每个活动周期的加速度计数据中提取的 16 个特征的集合映射而来的。为了结合加速度计数据的多维性质，提出了两种不同版本的分类器，并根据分段活动的正确识别率对它们的结果进行了比较，这两个群体样本的年龄不同。分类算法在年轻和老年成年人中分别实现了高于 90%和高于 92%的正确分类率。
