TY - GEN
T1 - Mobile Device Usage Recommendation based on User Context Inference Using Embedded Sensors
AU - Shi, Cong
AU - Guo, Xiaonan
AU - Yu, Ting
AU - Chen, Yingying
AU - Xie, Yucheng
AU - Liu, Jian
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
KW - Deep Learning
KW - Mobile Device Usage
UR - http://www.scopus.com/inward/record.url?scp=85093833282&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85093833282&partnerID=8YFLogxK
U2 - 10.1109/ICCCN49398.2020.9209697
DO - 10.1109/ICCCN49398.2020.9209697
M3 - Conference contribution
AN - SCOPUS:85093833282
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2020 - 29th International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th International Conference on Computer Communications and Networks, ICCCN 2020
Y2 - 3 August 2020 through 6 August 2020
ER -