In-situ investigation of notifications in multi-device environments

Weber, Dominik; Voit, Alexandra; Kratzer, Philipp; Henze, Niels

doi:10.1145/2971648.2971732

Cited by 54 publications

(33 citation statements)

References 16 publications

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“…These results indicate that when used in tandem with a smartphone, a smartwatch offers the same functionalities for receiving information, in a quicker and more convenient way. This quantified result is in tandem with the user preferences reported by Weber et al [38]. Figure 16 summarises our overall findings.…”

Section: Temporal Contextsupporting

confidence: 87%

“…PrefMiner is an application developed by Mehrotra et al [19] for creating personal notification preferences, but is limited to smartphones. Weber et al [38] studied users' notification preferences in multi-device environments with over 50% of their participants wanting to receive notifications via their smartwatch instead of their larger devices.…”

Section: Purpose and Usage Of Smartwatchesmentioning

confidence: 99%

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Quantifying Sources and Types of Smartwatch Usage Sessions

Visuri

Sarsenbayeva

Berkel

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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We seek to quantify smartwatch use, and establish differences and similarities to smartphone use. Our analysis considers use traces from 307 users that include over 2.8 million notifications and 800,000 screen usage events, and we compare our findings to previous work that quantifies smartphone use. The results show that smartwatches are used more briefly and more frequently throughout the day, with half the sessions lasting less than 5 seconds. Interaction with notifications is similar across both types of devices, both in terms of response times and preferred application types. We also analyse the differences between our smartwatch dataset and a dataset aggregated from four previously conducted smartphone studies. The similarities and differences between smartwatch and smartphone use suggest effect on usage that go beyond differences in form factor.

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Section: Temporal Contextsupporting

confidence: 87%

Section: Purpose and Usage Of Smartwatchesmentioning

confidence: 99%

Quantifying Sources and Types of Smartwatch Usage Sessions

Visuri

Sarsenbayeva

Berkel

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…found that users prefer a smartphone for receiving noti cations among the four devices (a tablet, a smartwatch, a smartphone, and a PC) regardless of the noti cation type [33]. is is because the smartphone is a key device for online connectivity and communication with other people.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…erefore, based on previous works [33], a smartphone is the key device that checks for notifications when users carry four devices including a tablet, a PC, a smartwatch, and a smartphone. Basically, users can check all the notifications on their smartphones even if they are not relayed to the smartwatches.…”

Section: Evaluating Prediction Modelsmentioning

confidence: 99%

Reducing Smartwatch Users’ Distraction with Convolutional Neural Network

Lee

Kwon

Kim

2018

Mobile Information Systems

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Smartwatches provide a useful feature whereby users can be directly aware of incoming notifications by vibration. However, such prompt awareness causes high distractions to users. To remedy the distraction problem, we propose an intelligent notification management for smartwatch users. e goal of our management system is not only to reduce the annoying notifications but also to provide the important notifications that users will swiftly react to. To analyze how to respond to the notifications daily, we have collected 20,353 in-the-wild notifications. Subsequently, we trained the convolutional neural network models to classify important notifications according to the users' contexts. Finally, the proposed management allows important notifications to be forwarded to a smartwatch. As experiment results show, the proposed method can reduce the number of unwanted notifications on smartwatches by up to 81%.

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“…Deferring to breakpoints Physical activities [1,76,77] Device interaction [78,91] Tasks & Mental Workload [11,50,51] Mitigating Choosing output device Estimating device for receiving notifications [115] Using IoT infrastructure for notifications delivery [56] Choosing output modality Inferring preferred output modalities [123] Indicating…”

Section: Mediatingmentioning

confidence: 99%

A Survey of Attention Management Systems in Ubiquitous Computing Environments

Anderson

Hübener

Seipp

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Today's information and communication devices provide always-on connectivity, instant access to an endless repository of information, and represent the most direct point of contact to almost any person in the world. Despite these advantages, devices such as smartphones or personal computers lead to the phenomenon of attention fragmentation, continuously interrupting individuals' activities and tasks with notifications. Attention management systems aim to provide active support in such scenarios, managing interruptions, for example, by postponing notifications to opportune moments for information delivery. In this article, we review attention management system research with a particular focus on ubiquitous computing environments. We first examine cognitive theories of attention and extract guidelines for practical attention management systems. Mathematical models of human attention are at the core of these systems, and in this article, we review sensing and machine learning techniques that make such models possible. We then discuss design challenges towards the implementation of such systems, and finally, we investigate future directions in this area, paving the way for new approaches and systems supporting users in their attention management.:2 • C. Anderson et al. Key takeaway points⋆ Attention is captured and steered by external and internal stimuli, while stimulus properties, such as its duration, location, intensity, etc., impact the user's reaction to the stimulus. Multimodal alert types, e.g., sound, light, vibration, and multiple device environments call for a coordinated judicious use of alerting in ubiquitous computing. ⋆ Limited cognitive capacities and threaded task processing imply that, in order to minimize disruptions, interruptions need to arrive at task boundaries or during routine tasks, should allow for task state rehearsal, and support context retrieval through hints presented to the user after an interruption. ⋆ Sensor data from ubiquitous computing devices reveals a user's location, physical activity, collocation with other people, and other information of a user's context that can be related to interruptibility. Next generation wearable devices and personalized machine learning models promise to bring us closer to direct inference of a user's cognitive processes. ATTENTION AND INTERRUPTION: DEFINITIONS AND STRATEGIESIn this section, we review definitions of the terms attention and interruption. We examine the connection between attention shifting and interruptions and discuss how interruptions can be handled through attention management systems in ubiquitous environments. What is Attention?There is no common understanding of attention in the literature. Attention is often considered as selective processing of incoming sensory information [33], with limited capacity [23] and reactive and deliberate processes [92]. Attention is also referred to as the ability to ignore irrelevant information [24]. The process of selecting stimuli can be voluntary or be steered by external events. The former ty...

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In-situ investigation of notifications in multi-device environments

Cited by 54 publications

References 16 publications

Quantifying Sources and Types of Smartwatch Usage Sessions

Quantifying Sources and Types of Smartwatch Usage Sessions

Reducing Smartwatch Users’ Distraction with Convolutional Neural Network

A Survey of Attention Management Systems in Ubiquitous Computing Environments

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