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Real-time activity recognition in mobile phones based on its accelerometer data

机译：基于加速度计数据的手机实时活动识别

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Context awareness is one of the important keys in a pervasive and ubiquitous environment. Activity recognition by utilizing accelerometer sensor is one of the context aware studies that has attracted many researchers, even up until today. Inspired by these researches, we came out with this presented study, which is a continuation of our previous workswhere we explore the possibility of using accelerometer embedded in smartphones in recognizing basic user activity through client/server architecture. In this paper, we present our work in exploring the influence of training data size on recognition accuracy in building classifier model by studying two algorithms, Naïve Bayes and Instance Based classifier (IBk, k=3). The result shows that 13 out of 18 possible combinations for both algorithms gave 90% training data size as the best accuracy, thus proving the assumption that bigger size of training data gives better classification accuracy compared to small sized training data, in most cases. Based on the outcome from the study, it is then implemented in Actiware, which is an activity aware application prototype that uses built in accelerometer sensor in smartphones to perform real-time/online activity recognition. The recognition process is done by utilizing available phone resources locally, without the involvement of any external server connection. ActiWare manages to exhibit encouraging result by recognizing basic user activities with relatively small confusion when tested.
机译：在无处不在的环境中，上下文感知是重要的关键之一。利用加速度计传感器进行活动识别是情境感知研究之一，至今一直吸引着许多研究人员。受这些研究的启发，我们提出了本研究报告，这是我们以前工作的延续，我们探索了使用嵌入式智能手机中的加速度计通过客户端/服务器体系结构识别基本用户活动的可能性。在本文中，我们将通过研究两种算法Nai Bayve Bayes和基于实例的分类器（IBk，k = 3）来探索构建分类器模型中训练数据大小对识别准确性的影响。结果表明，两种算法在18种可能的组合中有13种给出了90％的训练数据大小，这是最佳准确性，因此证明了在大多数情况下，与小规模的训练数据相比，更大的训练数据具有更好的分类准确性的假设。基于研究的结果，然后在Actiware中实现，Actiware是一种活动感知应用程序原型，使用智能手机中内置的加速度传感器来执行实时/在线活动识别。识别过程是通过本地利用可用的电话资源完成的，而无需任何外部服务器连接。通过测试，ActiWare通过识别基本的用户活动而表现出令人鼓舞的结果，而困惑却很小。

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