Xensible interruptions from your mobile phone

Hofte, G. Henri ter

doi:10.1145/1377999.1378003

Cited by 37 publications

(32 citation statements)

References 11 publications

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“…However, they want to have control with whom they share this information. This confirms previous studies [4,8,13] report that the social relation has a significant impact on sharing behavior. Similar to Khalil et al [8], men tend to more likely share contextual information than women.…”

Section: Discussionsupporting

confidence: 93%

“…The same trend holds for users that use Internet on their mobile phones compared to users that do not use the Internet on their cell phones. The results confirm Ter Hofte [13] that the majority of users would share their abstract location and would not share whether they are 'in a conversation?' or 'in company?'.…”

Section: Discussionsupporting

confidence: 63%

“…Ter Hofte [13], for example, reported an disclosure rate of 20% for 'in company' and 34% for 'in conversation', while Khalil et al [8] reported 74% for 'in company' and 69% for 'in conversation'. Previous work neither consider all cues that can be determined by current phones nor the trade-off between usefulness and privacy.…”

Section: Related Workmentioning

confidence: 99%

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Utilizing contextual information for mobile communication

Knittel

Shirazi

Henze

et al. 2013

CHI '13 Extended Abstracts on Human Factors in Computing Systems

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Mobile phones enable us to be reachable by phone calls anywhere and anytime. However, it is not always appropriate to answer a phone call. Even a ringing or vibrating phone can be inappropriate in some situations. The information required to assess if a call is appropriate is split between the caller and the callee. Only the caller knows the importance of the call and only the callee knows her context. Sharing parts of this context with the potential caller would enable the caller to make a better decision. Based on previous work we conducted a survey to learn about the contextual information that users believe to be important for this decision. We derive context information that users will to share and consider relevant and helpful. Further, we present a mobile application that augments users' address book with contextual information that we aim to study in the large.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 63%

See 1 more Smart Citation

Utilizing contextual information for mobile communication

Knittel

Shirazi

Henze

et al. 2013

CHI '13 Extended Abstracts on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…A common method for capturing this has been through self-reports, also known as Experience Sampling Methods (ESM) (e.g., [15,22]). On the positive side, due to the smartphone's ubiquity, this enables the collection of user opinion in situ, however it has several drawbacks for interruptibility studies.…”

Section: Labelling Response Behaviourmentioning

confidence: 99%

Push or Delay? Decomposing Smartphone Notification Response Behaviour

Turner

Allen

Whitaker

2015

Lecture Notes in Computer Science

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Abstract. Smartphone notifications are often delivered without considering user interruptibility, potentially causing frustration for the recipient. Therefore research in this area has concerned finding contexts where interruptions are better received. The typical convention for monitoring interruption behaviour assumes binary actions, where a response is either completed or not at all. However, in reality a user may partially respond to an interruption, such as reacting to an audible alert or exploring which application caused it. Consequently we present a multi-step model of interruptibility that allows assessment of both partial and complete notification responses. Through a 6-month in-the-wild case study of 11,346 to-do list reminders from 93 users, we find support for reducing false-negative classification of interruptibility. Additionally, we find that different response behaviour is correlated with different contexts and that these behaviours are predictable with similar accuracy to complete responses.

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“…After the software installation on the device, the participant immediately started with the experiment, which means that they did not start and stop at exactly the same time. In week 1 and week 5, the participants received questions investigating the interruptability of the participant [4]. After roughly five weeks, we collected the data from the SD cards and uninstalled the measurement platform 1 .…”

Section: User Experimentsmentioning

confidence: 99%

Density estimation for out-of-range events on personal mobile devices

Peddemors

Eertink

Niemegeers

2008

Proceedings of the 1st ACM SIGMOBILE Workshop on Mobility Models

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Over the years, personal mobile devices have obtained increasing capabilities to concurrently connect to surrounding networks and nearby other devices. Good knowledge on the dynamics in the availability of these heterogeneous entities constitutes essential input for various data communication problems, ranging from the adaptation of applications on mobile devices to multi-homed situations, to the optimization of routing protocols for delay tolerant networks. In this paper, we focus on a method for the prediction in time of the loss in visibility of currently in-range network entities, as observed on a personal mobile device. We are interested in estimating the full probability density function of the time of these out-of-range events, as this allows us to ask arbitrary questions such as: what is the probability of losing connection X in the next Y minutes? To do so, we model the mobility of the user by applying kernel density estimation on previously observed mobility traces collected during a user experiment we ran with 12 participants in a six week period, logging cellular, 802.11 wireless LAN, Bluetooth, and various other events on the participant's mobile device.

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Xensible interruptions from your mobile phone

Cited by 37 publications

References 11 publications

Utilizing contextual information for mobile communication

Utilizing contextual information for mobile communication

Push or Delay? Decomposing Smartphone Notification Response Behaviour

Density estimation for out-of-range events on personal mobile devices

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