Physical Human Activity Recognition Using Wearable Sensors.
Physical Human Activity Recognition Using Wearable Sensors.
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DOI：
10.3390/s151229858
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发表时间：
2015-12-11
期刊：
影响因子：
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通讯作者：
Amirat Y
中科院分区：
文献类型：
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作者：
Attal F;Mohammed S;Dedabrishvili M;Chamroukhi F;Oukhellou L;Amirat Y
本文提出了一种审查不同的分类技术，用于识别人类活动的可穿戴惯性传感器数据。本研究中使用了三个惯性传感器单元，并由健康受试者佩戴在上/下肢的关键点（胸部、右大腿和左脚踝）。三个主要步骤描述了活动识别过程：传感器的放置，数据预处理和数据分类。四个监督分类技术，即k-最近邻（k-NN），支持向量机（SVM），高斯混合模型（GMM），和随机森林（RF），以及三个无监督分类技术，即k均值，高斯混合模型（GMM）和隐马尔可夫模型（HMM），在正确分类率，F-测量，召回率，精度和特异性方面进行了比较。原始数据和提取的特征分别用作每个分类器的输入。使用基于RF算法的包装器方法来执行特征选择。基于我们的实验，得到的结果表明，k-NN分类器提供了最好的性能相比，其他监督分类算法，而HMM分类器是一个，给出最好的结果之间的无监督分类算法。这种比较突出了哪种方法在有监督和无监督的上下文中具有更好的性能。应该注意的是，所获得的结果仅限于本研究的背景，本研究涉及使用放置在受试者胸部、右小腿和左脚踝处的三个可穿戴加速度计对主要日常人类活动进行分类。
This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.