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首页> 外文期刊>Medical engineering & physics. >An adaptive Kalman-based Bayes estimation technique to classify locomotor activities in young and elderly adults through accelerometers.
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An adaptive Kalman-based Bayes estimation technique to classify locomotor activities in young and elderly adults through accelerometers.

机译：一种基于卡尔曼的自适应贝叶斯估计技术，可通过加速度计对年轻人和老年人的运动活动进行分类。

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摘要

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 naive 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.
机译：本文介绍了一种基于加速度计的系统，该系统能够在现实生活条件下的不同运动活动之间进行分类，并对其性能进行评估。通过使用朴素的2D-Bayes分类器的改编来检测，分割和分类以不同速度，坡度以及楼梯下降和上升的行走时期，该分类器通过估计活动的历史记录进行在线更新，采用基于卡尔曼的方案。从每个活动时期从加速度计数据中提取的16个特征的集合中映射出用于分类的特征对。提出了两个不同版本的分类器，以结合加速度计数据的多维性质，并根据分段活动的正确识别率对两个不同年龄的样本进行比较。该分类算法分别为年轻人和老年人实现了正确的分类率，分别高于90％和92％。

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