Washington: Do random push alerts, social media messages and noisy notifications on smartphones annoy you?
Relax! Researchers from Rutgers University in US are trying to help your phone understand you better, suggesting that by incorporating personality traits, the notification management system, it can help to manage notifications about when to interrupt or leave you alone.
The findings revealed that when participants were in a pleasant mood, they were likely to be more interruptible than if they were in an unpleasant mood and participants’ willingness to be interrupted varied based on their location.
“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 lead researcher Janne Lindqvist.
“Preferably, your smartphone would recognise your patterns of use and behaviour and schedule notifications to minimise interruptions,” Lindqvist added.
The study will be formally published in May at the ACM CHI Conference on Human Factors in Computing Systems in Denver, Colorado.
The team developed and evaluated a two-stage model to predict the degree to which people are interruptible by smartphones.
They collected more than 5,000 smartphone records from 22 participants over four weeks, and they were able to predict how busy people were.
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.
A few participants were highly interruptible at locations such as health care and medical facilities, possibly because they were waiting to see doctors.
But the participants were reluctant to be interrupted when they were studying and, compared with other activities, were less interruptible when exercising.
“We could, for example, optimise our model to allow smartphone customisation to match different preferences, such as always allowing someone to interrupt you,” he said.
“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,” he explained. (ANI)