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Recognizing physical contexts of mobile video learners via smartphone sensors

机译：通过智能手机传感器识别移动视频学习者的物理环境

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摘要

Current studies can effectively recognize several human activities in a single semantic context, but don't recognize the semantics of a single activity in different contexts. The main challenge is the conflicting phone usages as well as the special requirements of the energy consumption. This paper tests a classic learning scenario regarding mobile video viewing and validates the proposed recognition method by comprehensively taking the recognizing accuracy, effectiveness and the energy consumption into consideration. Readings of four carefully-selected sensors are collected and a wide range of machine learning algorithms are investigated. The results show the combination of accelerometer, light and sound sensors is better than that of acceleration, light and gyroscope sensors, the features with respect to energy spectral don't improve the recognition accuracy, and the system reaches robustness in a few minutes. The proposed method is simple, effective and practical in real applications of pervasive learning. (C) 2017 Elsevier B.V. All rights reserved.
机译：当前的研究可以有效地识别单个语义上下文中的多个人类活动，但是不能识别单个上下文在不同上下文中的语义。主要的挑战是电话使用冲突以及能耗的特殊要求。本文测试了有关移动视频观看的经典学习场景，并通过综合考虑识别准确性，有效性和能耗来验证所提出的识别方法。收集了四个精心选择的传感器的读数，并研究了多种机器学习算法。结果表明，加速度传感器，光和声音传感器的组合优于加速度传感器，光传感器和陀螺仪传感器，并且能谱方面的功能没有提高识别精度，并且系统在几分钟内达到了鲁棒性。该方法在普适学习的实际应用中简单，有效，实用。 （C）2017 Elsevier B.V.保留所有权利。

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