Smartphone Interruptions: Are Yours Relentless and Annoying?

Rutgers study reveals that personality traits influence and help predict receptiveness to smartphone notifications

Does your smartphone spew a relentless stream of text messages, push alerts, social media messages and other noisy notifications?

Well, Rutgers experts have developed a novel model that can predict your receptiveness to smartphone interruptions. It incorporates personality traits and could lead to better ways to manage a blizzard of notifications and limit interruptions – if smartphone manufacturers get on board.

“Ideally, a smartphone notification management system should be like an excellent human secretary who knows when you want to be interrupted or left alone,” said Janne Lindqvist, an assistant professor in the Department of Electrical and Computer Engineering in Rutgers’ School of Engineering. “We know that people struggle with time management all the time, so a smartphone, instead of being a nuisance, could actually help with things.”

Currently, smartphone users can limit interruptions by turning off their ringers, but no system figures out when you want to receive notifications. “Preferably, your smartphone would recognize your patterns of use and behavior and schedule notifications to minimize interruptions,” said Lindqvist, who leads a research group focusing on human-computer interaction and security engineering.

Studies have shown that inappropriate or untimely smartphone interruptions annoy users, decrease productivity and affect emotions, he said. So it’s important to choose the right time to interrupt people.

Lindqvist began thinking about how to reduce smartphone distractions several years ago, so he and his doctoral students, Fengpeng Yuan and Xianyi Gao, conducted a peer-reviewed study: “How Busy Are You? Predicting the Interruptibility Intensity of Mobile Users.” The pioneering study will be formally published in May at the ACM CHI Conference on Human Factors in Computing Systems in Denver, Colorado. It’s the premier international conference on human-computer interaction.

For their study, the researchers developed and evaluated a two-stage model to predict the degree to which people are interruptible by smartphones. The first stage is aimed at predicting whether a user is available at all or unavailable. The second stage gauges whether people are not interruptible, highly not interruptible, highly interruptible, interruptible or neutral toward interruptions, according to Lindqvist.

They collected more than 5,000 smartphone records from 22 participants at Rutgers University over four weeks, and they were able to predict how busy people were. That’s important because people can respond to different kinds of interruptions based on their level of busyness.

In a first, the researchers used major personality traits to help predict how interruptible people were. Study participants took a standard test to see how their personalities aligned with the “Big Five” personality traits in psychological theory – extroversion, agreeableness, conscientiousness, neuroticism and openness.

In addition to building a model for interruptibility, the researchers studied the situations when participants’ interruptibility varied. When participants were in a pleasant mood, they were likely to be more interruptible than if they were in an unpleasant mood, the study showed. The study also found that participants’ willingness to be interrupted varied based on their location. A few participants were highly interruptible at locations such as health care and medical facilities, possibly because they were waiting to see doctors. But participants were reluctant to be interrupted when they were studying and, compared with other activities, were less interruptible when exercising.

Lindqvist and his team are working on next steps that could lead to smarter smartphone notifications.

“We could, for example, optimize our model to allow smartphone customization to match different preferences, such as always allowing someone to interrupt you,” he said. “This would be something an excellent human secretary would know. A call from your kids or their daycare should always pass through, no matter the situation, while some people might want to ignore their relatives, for example.”

“Ideally, smartphones would learn automatically,” he said. “As it is today, the notification management system is not smart or only depends on a user’s setting, such as turning on or off certain notifications.  Our model is different because it collects users’ activity data and preferences. This allows the system to learn automatically like a ‘human secretary,’ so it enables smart prediction.”


Story by Rutgers science communicator Todd B. Bates at tbates@ucm.rutgers.edu or 848-932-0550.

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ECE Welcomes New Faculty

The Department of Electrical and Computer Engineering welcomes two new faculty members to the Rutgers engineering. These new instructors bring extensive experience to Rutgers, having performed research in several exciting fields. We look forward to their continued growth and innovation as part of the Rutgers Engineering faculty.
 
 
 
Assistant Professor
Electrical and Computer Engineering
PhD, Electrical and Computer Engineering, 2012
McGill University
Maryam Dehnavi recently worked as a postdoctoral researcher at the Massachusetts Institute of Technology, where she worked on machine learning and stencil computations. Her research included improving previous methods for finite-element method computations and other algorithms. Her goal is to create more efficient methods by which data-heavy or otherwise large-scale computations are performed by parallel systems. Previously she worked at Qualcomm Incorporated, where she developed and optimized code to improve applications. She has also been a visiting scholar at the University of California Berkeley and Irvine, where she likewise studied methods of improving software to work more effectively with hardware. She has been granted multiple scholarships and grants by the Natural Sciences and Engineering Research Council of Canada, and most recently earned a postdoctoral fellowship by the Quebec Research Fund.
 
Assistant Professor
Electrical and Computer Engineering
PhD, Electrical Engineering, 2010
University of Maryland

Vishal Patel’s key research interests are in machine learning, signal/image processing, computer vision processing, and security and privacy. He is a co-principal investigator on a Defense Advanced Research Projects Agency (DARPA) study analyzing fingerprints as a security feature for mobile devices. After earning his doctorate studying visual and atomic representations of signals, he worked and taught at the University of Maryland’s Center for Automation Research. Other research areas include approximation theory with wavelets, face recognition by computers, and biometrics. He is the principal investigator on a LADAR imaging study hosted by an Army Research Laboratory with General Dynamics. He was the recipient of a 2015 Computer Vision and Pattern Recognition Outstanding Reviewer Award.

ECE Faculty.jpg

Tahsina Farah Sanam Awarded Scholarship to the 2019 CRA-W Grad Cohort Workshop for Women

Tahsina Farah Sanam, an ECE PhD student working with Prof. Hana Godrich, has been awarded a scholarship to attend the CRA-W Grad Cohort for Women Workshop on April 12-13, 2019 at the Hilton Chicago in Chicago, IL.

CRA-W, established in 1991, strives to ensure that our activities have a positive impact on all underrepresented groups in CSE. We are committed to improving the working environment and increasing the success of all Computer Scientists and Engineers, without regard for gender, race, sexual orientation or socioeconomic background. The CRA-W Grad Cohort workshop, initiated in 2004, is generously funded by sponsors from industry, academia, the National Science Foundation, and the computing community. Grad Cohort aims to increase the ranks of senior women in computing-related studies and research by building and mentoring nationwide communities of women through their graduate studies.

Congratulations Tahsina on this achievement !

Spring 2019 Career & Internship Mega Fair

Join us at one of the largest and most diverse recruiting opportunities in the nation. An anticipated group of nearly 300 employers (different employers each day) will be available to network with candidates to discuss full-time, part-time, and internship opportunities from a wide variety of fields. This event is only open to Rutgers University (New Brunswick, Camden, Newark, and RBHS) students and alumni from all academic disciplines.

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