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 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

Salim El Rouayheb selected for the A. Walter Tyson Assistant Professorship Award

Dean Tom Farris of the School of Engineering has announced that Assistant Professor Salim El Rouayheb has been selected for the A. Walter Tyson Assistant Professorship Award. The Tyson fund, established by A. Walter Tyson, a 1952 alumnus of the School, is used to recruit promising junior faculty. Funds made available through the generosity of the Tyson Family are used to offset the School's investments in talented young faculty. With this award, funds will be used toward the School commitments that were made toward Professor El Rouayheb's start-up package.

Salim will be publicly recognized at the SOE Faculty Recognition Event planned for September 19, 2019.

Congratulations on this well-deserved recognition, Salim!

Anand Sarwate receives NSF Grant

ECE Assistant Professor Anand Sarwate has received a new NSF award for the project titled "Between Shannon and Hamming." This is a three-year $500,000 collaborative award led by Rutgers (Anand Sarwate, PI) with the University at Buffalo (Michael Langberg, co-PI). Rutgers' share of this award is $250,000. 

Anand and his collaborators will develop theoretical foundations for the study of new intermediate communication models, code designs, and capacity concepts with applications to vehicular networks and Internet of Things (IoT).  Over the last 70 years, information theory and coding has enabled communication technologies that have had an astounding impact on our lives. This is possible due to the match between encoding/decoding strategies and corresponding models of the communication channel. Traditional models fall at two ends of a spectrum. Models which assume that the channel is random, such as those involving channel noise governed by a memoryless stochastic process, take an average-case view of the channel: such models are the basis of Shannon theory. At the other extreme, ``Hamming''-like models take a worst-case view of the channel: the noise can be chosen adversarially with respect to the communication scheme. However, for several existing and emerging communication systems, the Shannon/average-case view may be too optimistic, whereas the Hamming/worst-case view may be too pessimistic. This project takes up the challenge of studying models that lie between the Shannon and Hamming extremes. The goal is to (a) design optimal rate coding schemes that exploit channel limitations; (b) design secure communication schemes to improve traditional tradeoffs between capacity and security; and (c) inform the design of future practical codes. The outcomes of this research will inform the design of codes for settings where average-case interference models may be too optimistic and worst-case models may be too pessimistic, such as wireless multiple-frame communication systems in vehicular networks (VANETS) or IoT.  
Congratulations, Anand!

Kristin Dana receives USDA-NIFA Grant

Professor Kristin Dana has received a 3 year grant from the USDA-NIFA Food and Agriculture Cyberinformatics and Tools (FACT) Initiative entitled "FACT: Deep Learning for Image-based Agriculture Evaluation". The award amount is $499,989. Kristin is the PI and the co-PI's are Peter Oudemans at Philip E. Marucci Blueberry and Cranberry Research and Extension Center at Rutgers University, and Aditi Roy at Siemens Corporate Technology.

Kristin and her team will leverage advances in machine learning, imaging and data science to target new opportunities for applications in computational agriculture. In this project, the team will combine computer vision with plant biology to create new, paradigm-shifting approaches for quantitatively evaluating plant health using imagery data. They will apply and develop cutting edge algorithms using machine learning methods on datasets collected using multi-spectral drones. The proposed work will focus on cranberry crops at Philip E. Marucci Blueberry and Cranberry Research and Extension Center at Rutgers University and include data collection (drone collection over cranberry fields) and machine learning to extract knowledge (deep learning image segmentation and classification).The long term goal is to enable and support real time crop assessment to facilitate management and to optimize crop yields.This project is a public and private partnership integrating multi-disciplinary research at Rutgers University and research at Siemens Corporation.

Congratulations, Kristin!

Hafiz Imtiaz selected as 2019-2020 Fellow in the PreDoctoral Leadership Development Academy

ECE PhD student Hafiz Imtiaz has been selected as a 2019-2020 Fellow in the Rutgers PreDoctoral Leadership Development Academy (PLDA). PLDA Fellows are a select group of graduate students who will receive a discipline-based study on experiential and classroom opportunities that emphasize leadership styles and strategies, collaborative decision-making, planning and organizational assessment, communication with internal and external constituencies, and other skill-sets that are important to informal and formal leadership and professional advancement. Through participation in the Institute, students can become more effective members of the academic community, more capable leaders and collaborators within their disciplines and their future places of employment, and for these reasons, more marketable and well-prepared for influential careers. Hafiz will complete his PhD, supervised by Professor Anand Sarwate, during the coming academic year. His research focuses on distributed and privacy-preserving machine learning, with applications to neuroimaging.

ECE Graduate Student Mengmei Ye wins "Samsung Breakthroughs That Matter" award at the MIT Hacking Medicine Grand Hack 2019

ECE PhD student Mengmei Ye working under the supervision of Assistant Professor Sheng Wei is part of a team that has won a "Samsung Breakthroughs That Matter" award at the MIT Hacking Medicine Grand Hack 2019 ( The "Samsung Breakthroughs that Matter" awards were created to highlight the potential for partnerships between the United States Veterans Health Administration (VHA), industry, and academia in the cultivation of innovative entrepreneurs, who embrace Samsung hardware and software to assist the VHA with improving Veterans’ quality of care and access to useful and innovative healthcare solutions for Veterans.

Mengmei was a member of the team of researchers/practitioners from industry/academia/hospital/government that was recognized for a system concept they developed, called Simulacron, for being the most effective and transformative digital health solution with the potential to improve access to and quality of, cancer care for veterans. Simulacron employs deep learning to realistically simulate privacy-sensitive medical data, including patient demographics and diagnostic codes, so that important properties of the data can be extracted and adopted in cancer prediction tools, instead of having to directly access the real data itself. The team also proposes to integrate Simulacron with the Samsung Knox mobile security platform to protect the proprietary datasets and models.

The announcement of the award has been covered by the following outlets:

Samsung Business Insights:

Samsung Newsroom:


Congratulations to Mengmei and Dr. Sheng Wei on this outstanding recognition!


Announcing the 2019 Recipient of the Paul Panayotatos Endowed Scholarship in Sustainable Energy

2019 Paul Panayotatos Endowed Scholarship in Sustainable Energy will be awarded to ECE graduate student Yuxuan Li.

Yuxuan Li is a PhD student working with Professor Yicheng Lu in the Department of Electrical and Computer Engineering. He will receive $5,000 in support of his project “Transparent MgZnO Based High Voltage Thin Film Transistor for Renewable Energy”. His research focuses on developing, fabricating, and analyzing the prototype of a transparent MZO-based HVTFT device on glass. In contrast to the conventional state-of-the-art SiC and GaN power transistors that require epitaxial growth on the lattice-matched single crystalline substrates at high temperature, the TFT technology is particularly beneficial to the PV system on glass as it offers low-temperature process (≤400°C in this work) and low material and fabrication cost. By developing and optimizing the MZO HVTFT on glass, the PV system efficiency can be improved because of higher on-currents, and the overall system cost can be reduced from both the production and installation aspects due to the compactness and small size of the distributed micro-inverter topology. As a result, more interests will attract to the BIPV system from the market and rapid development will happen, leading to wider use of solar energy in the society. He received his BS degree at UESTC in 2016.

Paul Panayotatos Endowed Scholarship was established in memory of Professor Paul Panayotatos who served for 30 years as a professor in the Department of Electrical and Computer Engineering. The Scholarship is awarded to graduate students demonstrating academic excellence and pursuing an advanced degree in the sustainable energy areas including renewable energy, energy efficiency, energy conversion or a related area.

Congratulations Yuxuan!


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