文章提出了一种利用智能手机的人体运动识别算法，将手机按使用者的习惯放置在任意的口袋中(对手机的方向和位置没有任何限制)。利用手机内置的运动传感器采集人体的运动数据，然后将手机采集的数据在WEKA环境下进行特征值的挖掘，并利用其工具箱中的J48决策树、贝叶斯、序列最小优化3种分类器对实验者的数据集进行离线分析，其中J48的分类精度最高达到了90.7%。最后利用分类效果比较好的J48决策树，在手机上开发了一种实时运动分类算法。%In this paper, built-in sensors were described to automatically detect human daily activities. In contrast to the previous work, this paper intends to recognize the physical activities when the phone’s orientation and position are varying. The data collected from six positions of seven subjects were investigated and two signals that are insensitive to orientation were chosen for classification. Decision trees (J48), Naive Bayes and sequential minimal optimization (SMO) were employed to recognize ifve activities:static, walking, running, walking upstairs and walking downstairs. The classiifcation results of three classiifers were compared. The results demonstrates that the J48 classiifer produces the best performance (average recognition accuracy:90.7%). Then we chose the J48 classiifer as online classiifer.
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