Enabling large-scale human activity inference on smartphones using community similarity networks (csn)

Lane, Nicholas D.; Xu, Ye; Lu, Hong; Hu, Shaohan; Choudhury, Tanzeem; Campbell, Andrew T.; Zhao, Feng

doi:10.1145/2030112.2030160

Cited by 140 publications

(96 citation statements)

References 20 publications

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“…This is in-line with existing studies [31] that showed that classification accuracy lowers when considering a large scale and diverse set of participants. Although the average accuracy per user approximates the one per self report, we found high differences between groups of users: these differences are likely due to users' diversity and possibly low number of observations for some users.…”

Section: Resultssupporting

confidence: 92%

Mobile Sensing at the Service of Mental Well-being

Servia-Rodríguez

Rachuri

Mascolo

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

130

203

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Measuring mental well-being with mobile sensing has been an increasingly active research topic. Pervasiveness of smartphones combined with the convenience of mobile app distribution platforms (e.g., Google Play) provide a tremendous opportunity to reach out to millions of users. However, the studies at the confluence of mental health and mobile sensing have been longitudinally limited, controlled, or confined to a small number of participants. In this paper we report on what we believe is the largest longitudinal in-the-wild study of mood through smartphones. We describe an Android app to collect participants' self-reported moods and system triggered experience sampling data while passively measuring their physical activity, sociability, and mobility via their device's sensors. We report the results of a large-scale analysis of the data collected for about three years from ∼ 18, 000 users.The paper makes three primary contributions. First, we show how we used physical and software sensors in smartphones to automatically and accurately identify routines. Then, we demonstrate the strong correlation between these routines and users' personality, well-being perception, and other psychological variables. Finally, we explore predictability of users' mood using their passive sensing data. Our findings show that, especially for weekends, mobile sensing can be used to predict users' mood with an accuracy of about 70%. These results have the potential to impact the design of future mobile apps for mood/behavior tracking and interventions.

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Section: Resultssupporting

confidence: 92%

Mobile Sensing at the Service of Mental Well-being

Servia-Rodríguez

Rachuri

Mascolo

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

130

203

View full text Add to dashboard Cite

show abstract

“…This approach is used in collaborative approaches for video editing [4], group activities [3], and a generic opportunistic framework [9]. To increase accuracy, one approach [18] shares classifiers among users with similar behaviors. Other works which use backend servers [29] [12] provide sensing quality tradeoffs, such as an adaptive sampling rate, to save energy.…”

Section: Related Workmentioning

confidence: 99%

“…In our Bagging classifiers, each weak classifier is a Naive Bayes classifier trained from the training data of a single sensor [16]. Other sharing approaches use more complex techniques, such as GMMs trained offline [24] or Boosting [18].…”

Section: Sharing-aware Classificationmentioning

confidence: 99%

Remora: Sensing resource sharing among smartphone-based body sensor networks

Keally

Zhou

Xing

et al. 2013

2013 IEEE/ACM 21st International Symposium on Quality of Service (IWQoS)

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In many body sensor network (BSN) applications, such as activity recognition for assisted living residents or physical fitness assessment of a sports team, users spend a significant amount of time with one another while performing many of the same activities. We exploit this physical proximity with Remora, a smartphone-based Body Sensor Network activity recognition system which shares sensing resources among neighboring BSNs. Compared to individual BSNs, Remora resource sharing provides increased accuracy and significant energy savings. To increase classification accuracy, Remora BSNs share sensors by overhearing neighbors' sensor data transmissions. When sharing, fewer on-body sensors are needed to achieve high accuracy, resulting in energy savings by turning off unneeded sensors. To save phone energy, neighboring BSNs share classifiers: only one classifier is active at a time classifying activities for all neighbors. Remora addresses three major challenges of sharing with physical neighbors: 1) Sharing only when the energy benefit outweighs the cost, 2) Finding and utilizing the shared sensors and classifiers which produce the best combination of accuracy improvement and energy savings, and 3) Providing a lightweight and collaborative classification approach, without the use of a backend server, which adapts to the dynamics of available neighbors. In a two week evaluation with 6 subjects, we show that sharing provides up to a 30% accuracy increase while extending phone battery lifetime by over 65%.

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“…Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensordata using classification models underpins these emerging applications [27]. The key contribution of community similarity networks (CSN) is that it makes the personalization of classification models practical by significantly lowering the burden to the user through a combination of crowd-sourced data and leveraging networks that measure the similarity between users.…”

Section: Introductionmentioning

confidence: 99%

The Geometry and Usage of the Supplementary Fisheye Lenses in Smartphones

Sahin¹

2017

Smartphones From an Applied Research Perspective

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Nowadays, mobile phones are more than a device that can only satisfy the communication need between people. Since fisheye lenses integrated with mobile phones are lightweight and easy to use, they are advantageous. In addition to this advantage, it is experimented whether fisheye lens and mobile phone combination can be used in a photogrammetric way, and if so, what will be the result. Fisheye lens equipment used with mobile phones was tested in this study. For this, standard calibration of 'Olloclip 3 in one' fisheye lens used with iPhone 4S mobile phone and 'Nikon FC-E9' fisheye lens used with Nikon Coolpix8700 are compared based on equidistant model. This experimental study shows that Olloclip 3 in one fisheye lens developed for mobile phones has at least the similar characteristics with classic fisheye lenses. The dimensions of fisheye lenses used with smart phones are getting smaller and the prices are reducing. Moreover, as verified in this study, the accuracy of fisheye lenses used in smartphones is better than conventional fisheye lenses. The use of smartphones with fisheye lenses will give the possibility of practical applications to ordinary users in the near future.

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Enabling large-scale human activity inference on smartphones using community similarity networks (csn)

Cited by 140 publications

References 20 publications

Mobile Sensing at the Service of Mental Well-being

Mobile Sensing at the Service of Mental Well-being

Remora: Sensing resource sharing among smartphone-based body sensor networks

The Geometry and Usage of the Supplementary Fisheye Lenses in Smartphones

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