Modeling and discovering human behavior from smartphone sensing life-log data for identification purpose

Mafrur, Rischan; Nugraha, I Gde Dharma; Choi, Deokjai

doi:10.1186/s13673-015-0049-7

Cited by 47 publications

(31 citation statements)

References 21 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these techniques do not take into account the freshness of rules, i.e., rules that represent recent patterns, in which we are interested to output a complete set of updated behavioral rules based on recency for individual mobile phone users utilizing their contextual smartphone datasets. In order to mine users' contextual mobile phone data to model their behavior, a number of authors use a static period of phone log data, such as phone call logs [22][23][24][25], SMS Log [26], mobile application (apps) usages logs [3,27,28], mobile phone notification logs [10], web logs [29][30][31], game Log [32], context logs [4], and smartphone life log [33,34] etc. for various purposes.…”

Section: Background and Related Workmentioning

confidence: 99%

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

2019

View full text Add to dashboard Cite

IntroductionNowadays, smartphones are considered as essential devices in our daily life. Due to the recent advanced features in smartphones and the popularity of context-awareness in mobile technologies, individual's behavioral activities with their phones, such as phone call activities, mobile applications usage, mobile notification responses, social networking, and corresponding contextual information are recorded through the device logs. An individual smartphone's ability to store user's such diverse activities and associated contexts with their phones enables the study on data-driven smartphone usage behavior modeling and prediction [1]. In this paper, we aim to mine a set of personalized recent behavioral patterns, i.e., recency, based rules utilizing individual's contextual phone log data, for the purpose of building an effective context-aware personalized usage behavior prediction model. To illustrate the efficacy of our proposed approach, in this paper, we Abstract Due to the advanced features in recent smartphones and context-awareness in mobile technologies, users' diverse behavioral activities with their phones and associated contexts are recorded through the device logs. Behavioral patterns of smartphone users may vary greatly between individuals in different contexts-for example, temporal, spatial, or social contexts. However, an individual's phone usage behavior may not be static in the real-world changing over time. The volatility of usage behavior will also vary from user-to-user. Thus, an individual's recent behavioral patterns and corresponding machine learning rules are more likely to be interesting and significant than older ones for modeling and predicting their phone usage behavior. Based on this concept of recency, in this paper, we present an approach for mining recency-based personalized behavior, and name it "RecencyMiner" for short, utilizing individual's contextual smartphone data, in order to build a context-aware personalized behavior prediction model. The effectiveness of RecencyMiner is examined by considering individual smartphone user's real-life contextual datasets. The experimental results show that our proposed recency-based approach better predicts individual's phone usage behavior than existing baseline models, by minimizing the error rate in various context-aware test cases.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

2019

View full text Add to dashboard Cite

show abstract

“…It allows individuals to access web-enabled mobile devices for managing their healthcare conveniently. Even though this technology makes m-health possible, many open issues still exist within the mobile healthcare environment, such as the security of electronic data transactions, mobile user authentication and secure data storage on a mobile device with privacy protection [19,20].…”

Section: Related Workmentioning

confidence: 99%

An Energy-Efficient Virtualization-Based Secure Platform for Protecting Sensitive User Data

Lim

Park

2017

Sustainability

View full text Add to dashboard Cite

Currently, the exchange cycles of various computers, smartphones, tablets, and others have become shorter, because new high-performance devices continue to roll out rapidly. However, existing legacy devices are not old-fashioned or obsolete to use. From the perspective of sustainable information technology (IT), energy-efficient virtualization can apply a way to increase reusability for special customized devices and enhance the security of existing legacy devices. It means that the virtualization can customize a specially designed purpose using the guest domain from obsolete devices. Thus, this could be a computing scheme that keeps energy supplies and demands in balance for future sustainable IT. Moreover, energy-efficient virtualization can be the long-term and self-sustainable solution such as cloud computing, big data and so forth. By separating the domain of the host device based on virtualization, the guest OS on the segmented domain can be used as a Trusted Execution Environment to perform security features. In this paper, we introduce a secure platform to protect sensitive user data by domain isolation utilizing virtualization. The sensitive user data on our secure platform can protect against the infringement of personal information by malicious attacks. This study is an effective solution in terms of sustainability by recycling them for special purposes or enhancing the security of existing devices.

show abstract

“…In our previous work (Mafrur et al, 2015), we already conducted data collection. We used that data for our activity recognition and human behavior research.…”

Section: Related Workmentioning

confidence: 99%

Exploring Significant Motion Sensor for Energy-efficient Continuous Motion and Location Sampling in Mobile Sensing Application

Muthohar

Nugraha²,

Choi

2016

IJTech

Self Cite

View full text Add to dashboard Cite

The significant motion sensor is a new sensor that promises motion detection at low power consumption. Despite that promise, no known research has explored the usage of this sensor, especially in mobile sensing research. In this study, we explore the utilization of this significant motion sensor for continuous motion and location sampling in a mobile sensing application. A location sensor is known for its expensive power consumption in retrieving the location data, and continuously sampling from it will quickly deplete a smartphone battery. We experiment with two sampling strategies that utilize this significant motion sensor to achieve low power consumption during continuous sampling. One strategy involves utilizing the sensor naively, while the other involves combining with the duty cycle. Both strategies achieve low energy consumption, but the one that combines with the duty cycle achieves lower energy consumption. By utilizing this sensor, mobile sensing research especially that samples data from location or motion sensors, will be able to achieve lower energy consumption.

show abstract

Modeling and discovering human behavior from smartphone sensing life-log data for identification purpose

Cited by 47 publications

References 21 publications

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

RecencyMiner: mining recency-based personalized behavior from contextual smartphone data

An Energy-Efficient Virtualization-Based Secure Platform for Protecting Sensitive User Data

Exploring Significant Motion Sensor for Energy-efficient Continuous Motion and Location Sampling in Mobile Sensing Application

Contact Info

Product

Resources

About