Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data
Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data
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DOI：
10.9781/ijimai.2012.155
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发表时间：
2012-06-01
影响因子：
3.6
通讯作者：
Roning, Juha
中科院分区：
文献类型：
--
作者：
Siirtola, Pekka;Roning, Juha
本文介绍了一种在移动的手机上进行实时人体活动识别的方法。与大多数其他研究不同的是，不仅使用智能手机的加速度计收集数据，而且还在手机上实现模型，并在设备上完成整个分类过程（预处理，特征提取和分类）。该系统使用手机方向独立功能进行训练，以识别五种日常活动：步行，跑步，骑自行车，驾驶汽车和坐/站，而手机在受试者的裤子口袋里。两个分类器进行了比较，knn（k近邻）和QDA（二次判别分析）。使用从八个受试者收集的数据集离线训练用于实时活动识别的模型，并将这些离线结果与实时识别率进行比较，实时识别率是通过将模型实现到移动的活动识别应用程序而获得的，该应用程序目前支持两种操作系统：Symbian布尔AND 3和Android。实验结果表明，该方法简单易行，适合于实时识别。此外，智能手机上的识别率令人鼓舞，事实上，所获得的识别准确率与离线识别率大致相同。此外，结果表明，所提出的方法是不依赖于操作系统。
Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian boolean AND 3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent.