Human Behavior Analysis from Smartphone Data Streams

Jalali, Laleh; Oh, Hyungik; Moazeni, Ramin; Jain, Ramesh

doi:10.1007/978-3-319-46843-3_5

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…The authors reported that when the proposed behavioral recognition system was applied to human identification, a recognition accuracy of 80% was achieved. Jalali et al [17] used the concept of lattice-based data fusion for recognizing events. In addition, they showed a framework that extracts frequent co-occurrence patterns as parallel and sequential relations among their events from some event streams.…”

Section: Spatial Analysis Platformmentioning

confidence: 99%

Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

Kang¹,

Kim²

2021

Computers, Materials &Amp; Continua

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In this study, we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment. We focus on the users' app usage to analyze unusual behavior, especially in indoor spaces. This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased. Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied for effective singular movement analysis. To analyze high-level mobile phone usage, the t-distributed stochastic neighbor embedding (t-SNE) algorithm was employed. These two clustering algorithms can effectively detect outlier behaviors in terms of movement and app usage in indoor spaces. The experimental results showed that our system enables effective spatial behavioral analysis at a low cost when applied to logs collected in actual living spaces. Moreover, large volumes of data required for outlier detection can be easily acquired. The system can automatically detect the unusual behavior of a user in an indoor space. In particular, this study aims to reflect the recent trend of the increasing use of smartphones in indoor spaces to the behavioral analysis.

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Section: Spatial Analysis Platformmentioning

confidence: 99%

Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

Kang¹,

Kim²

2021

Computers, Materials &Amp; Continua

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“…the authors were and are highly involved into several multimedia related international events in the field of the workshop: Measuring Behaviour 2016, PervasiveHealth 2014, SenseCam Symposium 2010, Beyond QuantifiedSelf at CHI 2014, among others. The organizers are working in the different fields of multimedia in health such as tracking of nutrition [1], digital interventions for personal health [2], activity recognition from Smart phones [4] and in lifelogging [3] using statistical methods, from multimodal heterogeneous data [4] and interpretation for personal health [6].…”

Section: Organizersmentioning

confidence: 99%

MMHealth 2017

Böll

Ebrahimi

Gurrin

et al. 2017

Proceedings of the 25th ACM International Conference on Multimedia

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Ever since the emergence of digitization, we've used the term multimedia to represent a combination of different kinds of media types, such as images, audio, and videos. As new sensing technologies emerge and are now becoming omnipresent in daily lives, the definition, role and significance of multimedia is changing. Multimedia now represents the means for communicating, cooperating, and also for monitoring numerous aspects of daily life, at various levels of granularity and application, ranging from personal to societal. With this shift, we have since moved from comprehending single media and its state toward comprehending media in terms of its use context. Multimedia is thus no longer confined to documentation and preservation, entertainment or personal media collections; rather, it has become an integral part of the tools and systems that are providing solutions to today's societal challengesincluding challenges related to health, aging, education, societal participation, sustainable energy, and intelligent transportation. Multimedia has thus evolved into a core enabler for future interactive and cooperative applications at the heart of society. In this workshop we explore the relevance and contribution of multimedia to health care and personal media.

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“…Additional short papers are complementing the workshop. [2], activity recognition from Smart phones [4] and in lifelogging [3] using statistical methods, from multimodal heterogeneous data [4] and interpretation for personal health [6].…”

mentioning

confidence: 99%

MMHealth 2017: Workshop on Multimedia for Personal Health and Health Care

et al. 2018

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Ever since the emergence of digitization, we've used the term multimedia to represent a combination of different kinds of media types, such as images, audio, and videos. As new sensing technologies emerge and are now becoming omnipresent in daily lives, the definition, role and significance of multimedia is changing. Multimedia now represents the means for communicating, cooperating, and also for monitoring numerous aspects of daily life, at various levels of granularity and application, ranging from personal to societal. With this shift, we have since moved from comprehending single media and its state toward comprehending media in terms of its use context.Multimedia is thus no longer confined to documentation and preservation, entertainment or personal media collections; rather, it has become an integral part of the tools and systems that are providing solutions to today's societal challengesincluding challenges related to health, aging, education, societal participation, sustainable energy, and intelligent transportation. Multimedia has thus evolved into a core enabler for future interactive and cooperative applications at the heart of society. In this workshop we explore the relevance and contribution of multimedia to health care and personal media.

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Human Behavior Analysis from Smartphone Data Streams

Cited by 9 publications

References 26 publications

Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

MMHealth 2017

MMHealth 2017: Workshop on Multimedia for Personal Health and Health Care

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