arXiv subjects

Abhinav Mehrotra

Publications and source records attributed to Abhinav Mehrotra.

22 records · Page 2Linked to original sources

arXiv2018

Intelligent Notification Systems: A Survey of the State of the Art and Research Challenges

Notifications provide a unique mechanism for increasing the effectiveness of real-time information delivery systems. However, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and might become a cause of potential disruption rather than proving beneficial to users. In order to address these challenges a variety of intelligent notification mechanisms based on monitoring and learning users' behavior have been proposed. The goal of such mechanisms is maximizing users' receptivity to the delivered information by automatically inferring the right time and the right context for sending a certain type of information. This article provides an overview of the current state of the art in the area of intelligent notification mechanisms that relies on the awareness of users' context and preferences. More specifically, we first present a survey of studies focusing on understanding and modeling users' interruptibility and receptivity to notifications from desktops and mobile devices. Then, we discuss the existing challenges and opportunities in developing mechanisms for intelligent notification systems in a variety of application scenarios.

cs.HC↗

arXiv2017

Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities

There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.

cs.LG↗

arXiv2017

Sensing and Modeling Human Behavior Using Social Media and Mobile Data

In the past years we have witnessed the emergence of the new discipline of computational social science, which promotes a new data-driven and computation-based approach to social sciences. In this article we discuss how the availability of new technologies such as online social media and mobile smartphones has allowed researchers to passively collect human behavioral data at a scale and a level of granularity that were just unthinkable some years ago. We also discuss how these digital traces can then be used to prove (or disprove) existing theories and develop new models of human behavior.

cs.CY↗

arXiv2015

Anticipatory Mobile Digital Health: Towards Personalised Proactive Therapies and Prevention Strategies

The last two centuries saw groundbreaking advances in the field of healthcare: from the invention of the vaccine to organ transplant, and eradication of numerous deadly diseases. Yet, these breakthroughs have only illuminated the role that individual traits and behaviours play in the health state of a person. Continuous patient monitoring and individually-tailored therapies can help in early detection and efficient tackling of health issues. However, even the most developed nations cannot afford proactive personalised healthcare at scale. Mobile computing devices, nowadays equipped with an array of sensors, high-performance computing power, and carried by their owners at all time, promise to revolutionise modern healthcare. These devices can enable continuous patient monitoring, and, with the help of machine learning, can build predictive models of patient's health and behaviour. Finally, through their close integration with a user's lifestyle mobiles can be used to deliver personalised proactive therapies. In this article, we develop the concept of anticipatory mobile-based healthcare - anticipatory mobile digital health - and examine the opportunities and challenges associated with its practical realisation.

cs.CY↗