Mobile Device Usage Recommendation based on User Context Inference Using Embedded Sensors
Mobile Device Usage Recommendation based on User Context Inference Using Embedded Sensors
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DOI：
10.1109/icccn49398.2020.9209697
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Cong Shi;Xiaonan Guo;Ting Yu;Yingying Chen;Yucheng Xie;Jian Liu
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文献类型：
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作者：
Cong Shi;Xiaonan Guo;Ting Yu;Yingying Chen;Yucheng Xie;Jian Liu
移动设备的激增及其丰富的功能/应用程序已经使人们形成了上瘾和潜在有害的使用行为。虽然这个问题已经引起了相当大的关注，但现有的解决方案（如文本通知或设置使用限制）是不够的，不能提供及时的建议或控制移动设备的不当使用。本文提出了一个广义的上下文推理框架，该框架支持使用移动设备中的低功耗传感器及时提供使用建议。与依赖于检测单一类型用户上下文（例如，仅在位置或活动上）的现有方案相比，我们的框架派生出更大规模的用户上下文，这些用户上下文表征了手机的使用，特别是那些导致分心或导致危险情况的用户上下文。我们建议用上下文基础来统一描述一般用户上下文，即物理环境、社会情境和人类运动，它们是不同一般用户上下文的潜在组成单位。为了减轻不同环境、设备和个人的分析工作，我们开发了一种基于深度学习的架构，以学习与上下文基础相关的传感器读数衍生的可转移表示。基于派生的上下文基础，我们的框架量化了推断的用户上下文导致分心/危险情况的可能性，并为移动设备访问/使用提供及时的建议。在为期7个月的大量实验中，该系统在提供不同环境、设备和用户之间的可移植性的同时，在用户上下文推理方面可以达到95%的准确率。
The proliferation of mobile devices along with their rich functionalities/applications have made people form addictive and potentially harmful usage behaviors. Though this problem has drawn considerable attention, existing solutions (e.g., text notification or setting usage limits) are insufficient and cannot provide timely recommendations or control of inappropriate usage of mobile devices. This paper proposes a generalized context inference framework, which supports timely usage recommendations using low-power sensors in mobile devices Comparing to existing schemes that rely on detection of single type user contexts (e.g., merely on location or activity), our framework derives a much larger-scale of user contexts that characterize the phone usages, especially those causing distraction or leading to dangerous situations. We propose to uniformly describe the general user context with context fundamentals, i.e., physical environments, social situations, and human motions, which are the underlying constituent units of diverse general user contexts. To mitigate the profiling efforts across different environments, devices, and individuals, we develop a deep learning-based architecture to learn transferable representations derived from sensor readings associated with the context fundamentals. Based on the derived context fundamentals, our framework quantifies how likely an inferred user context would lead to distractions/dangerous situations, and provides timely recommendations for mobile device access/usage. Extensive experiments during a period of 7 months demonstrate that the system can achieve 95% accuracy on user context inference while offering the transferability among different environments, devices, and users.