Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone
Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone
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DOI：
10.3390/s120912588
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发表时间：
2012-09-17
期刊：
影响因子：
--
通讯作者：
Lee S
中科院分区：
文献类型：
--
作者：
Han M;Vinh LT;Lee YK;Lee S
智能手机的最新发展提高了处理能力，并为这些设备配备了许多内置的多模式传感器，包括加速度计，陀螺仪，GPS接口，Wi-Fi接入和接近传感器。尽管许多研究已经调查了使用智能手机开发用户上下文感知应用程序的事实，但这些应用程序目前只能使用单一类型的传感器识别简单上下文。因此，在这项工作中，我们介绍了一个全面的方法，上下文感知的应用程序，利用智能手机中的多模态传感器。所提出的系统不仅能够以高精度识别不同类型的上下文，而且还能够优化功耗，因为功耗高的传感器可以在适当的时间被激活或停用。此外，该系统能够识别智能手机在人体上的任何地方的活动，即使用户正在使用手机打电话，操作应用程序，玩游戏或听音乐。此外，我们还提出了一种新的加速度计分类模块的特征选择算法。所提出的特征选择算法有助于选择好的特征并消除坏的特征，从而提高加速度计分类器的整体精度。实验结果表明，该系统可以分类八个活动的准确率为92.43%。
Recent developments in smartphones have increased the processing capabilities and equipped these devices with a number of built-in multimodal sensors, including accelerometers, gyroscopes, GPS interfaces, Wi-Fi access, and proximity sensors. Despite the fact that numerous studies have investigated the development of user-context aware applications using smartphones, these applications are currently only able to recognize simple contexts using a single type of sensor. Therefore, in this work, we introduce a comprehensive approach for context aware applications that utilizes the multimodal sensors in smartphones. The proposed system is not only able to recognize different kinds of contexts with high accuracy, but it is also able to optimize the power consumption since power-hungry sensors can be activated or deactivated at appropriate times. Additionally, the system is able to recognize activities wherever the smartphone is on a human's body, even when the user is using the phone to make a phone call, manipulate applications, play games, or listen to music. Furthermore, we also present a novel feature selection algorithm for the accelerometer classification module. The proposed feature selection algorithm helps select good features and eliminates bad features, thereby improving the overall accuracy of the accelerometer classifier. Experimental results show that the proposed system can classify eight activities with an accuracy of 92.43%.
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影响因子：
8.9
作者：
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通讯作者：
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DOI：
10.1109/titb.2010.2051955
发表时间：
2010-09-01
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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