Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing
Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing
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DOI：
10.1109/tsmc.2016.2562509
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发表时间：
2017-12-01
影响因子：
8.7
通讯作者：
Malekian, Reza
中科院分区：
文献类型：
--
作者：
Wannenburg, Johan;Malekian, Reza
通过使用智能手机加速度计数据，对坐、站、躺、走和慢跑等日常活动进行身体活动识别。通过使用机器学习算法，在远程服务器上完成活动分类，并通过无线方式从智能手机接收数据。在收集数据时，智能手机被放在受试者的裤兜里。使用大样本集训练分类器，然后使用测试集验证算法的准确性。评估了十种不同的分类器算法配置，以确定哪种算法总体上表现最好，以及哪种算法对特定活动类表现最好。基于所获得的结果，可以对离线活动识别做出非常准确的预测。kNN和kStar算法的总体准确率均达到99.01%。
Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.