Funding Agency : HBKU Innovation Centre (Rapid Response Innovation Call)

The idea of this project builds on the existing work that has been carried out over the last six years.

The first version of the serious game prototype provided vocabulary learning of 12 categories (fruits, animals, and birds among others) to children with Autism Spectrum Disorder (ASD). The prototype has been extensively tested with the participants over the course of 3 months and results showed improvement in the children’s vocabulary.

The goal is to transform the learning of vocabulary through mobile augmented reality (MAR).

The advantage of using MAR is its ubiquity i.e., learning anything supported through an app, anytime, anywhere.

The MAR app could possibly be the first of its kind for the research community as well as for children with ASD in Qatar, their caregivers and teachers.

Project Team Members:

Funding Agency : QNRF (RRC-COVID-19)

This project aims to develop, evaluate, and launch an interactive educational platform utilizing the role of mixed-reality (real-time teaching and virtual teaching) by enabling remote learning for children with Autism Spectrum Disorder (ASD). The idea of this project is a product of our continuous collaboration with Shafallah and Mada Center in which we have developed an augmented reality (AR) vocabulary learning application for children with ASD in English and Arabic.

This project aims to develop a mobile application that can help autistic adults to communicate with their friends and family.

Diabetes is one of the leading causes of death in developing countries. The existing mHealth app design guidelines do not support self-monitoring of health status, and behavior change to improve and adopt a healthy lifestyle.

This project will focus on the development of a specialized checklist to evaluate the design of such applications.

Funding Agency : Qatar National Research Fund (QNRF) and Doha International Family InstituteI (OSRA 5th Cycle)

Older persons are an integral part of the family unit in Qatar. Family members are increasingly expected to take on caring roles, sometimes at the expense of their own health and wellbeing. Studies have shown that family caregiving is physically and psychologically demanding, with 75% of caregivers experiencing psychological illness (e.g. depression). Older persons and caregivers stand to benefit from the emerging information and communication technologies (ICT) that support daily activities and social inclusion. Findings related to the emotional, social, cognitive, and daily and work-related activity outcomes of ICT used by older persons and caregivers have been very promising. Older persons’ use of ICT was found to improve self-esteem, and reduce isolation, and depression; and support health monitoring, thus enabling a sense of control and independent living at home. The aim of this project is to examine the experiences of older persons and their caregivers in the local context and to explore their perspectives on ICT, in supporting caregiving tasks and the independent living of older persons living at home. In this project, we will address the following research questions, focusing on older persons and their caregivers: What are the opportunities for ICT to support daily activity participation and social inclusion of older persons and their caregivers, and what are the challenges, concerns, and enablers for ICT access and use to meet these opportunities?

Funding Agency : HBKU Innovation Centre

The main aim of this project is to develop a technology for supporting and managing chronic patients using online peer support groups mechanisms. The technology will help to create, manage, and support online peer support groups in the form of small social networks of 10 to 15 people, where they can talk to others who are like themselves, and who truly understand their problems and can share the type of practical insights that can only come from first-hand experience. The technology will consist of a mobile application and a backend that includes a data management platform and an AI-based support engine. It will also continuously monitor the patients’ lifestyle, such as physical activities, nutrition, and vital signs using their smartphones’ embedded sensors, such as gyroscope, accelerometer, magnetometer, and light sensors. to provide real-time recommendations, alerts, and reminders.

This project aims at improving an entity’s cybersecurity by detecting vulnerable or malicious users through their behavior. Through this research, we develop an AI-based UEBA system that learns the baseline of each user and detects any deviations (anomaly) from their usual behavioral patterns within a network. Anomalies can signify malicious intent, compromised or vulnerable users. This information is then sent to the appropriate personnel for action.

With more than half of the world’s population residing in cities, the need for smarter, more accurate insights into a city’s workings is imperative. Crowd detection and crowd management is an integral aspect of safety and planning for any city.

This project aims to build an intelligent model that is capable of detecting crowds and predicting their behavior using video surveillance and IoT sensors.

Providing proper personnel with information on crowds and their behavior allows proactive measures to be taken in the event of adverse behavior being predicted or detected.

This project aims to understand how humans respond to information and directives. In particular, if individuals or crowds are given certain directives, we want to understand their response to the information.

Understanding this will enable decision makers to;

1. Alter information to optimize responses,
2. Effectively prepare for non-adherence.

A simple example is the current situation in Qatar amid the ongoing COVID-19 pandemic.

Many people do not follow government instructions and directives to stay home and practice social distancing, , leading to an increasing number of COVID-19 cases.

The ability to anticipate when such directives would not be accepted by a large number of the population can help decision makers take other courses of action.

Funding agency : HBKU Innovation Fund Cycle 2.

Global Positioning System (GPS) technology provides accurate and timely services, based on precise location estimations. However, GPS has proven to be characterized by an intrinsic insecure design, thus being subject to several security attacks. Indeed, hackers equipped with cheap and commercially-available tools can launch threatening attacks, causing users to move away from their intended path.

This project investigates the adoption of crowd-sourced information from the mobile cellular infrastructure and WiFi networks to detect GPS spoofing attacks.

This project focuses on machine learning attacks that exploit network patterns - although encrypted by several security layers -to infer user behavior on the internet. Indeed, standard encryption techniques alone are not sufficient to protect the privacy of the end-user.More work is required to prevent non-trusted third parties to detect and identify smart home devices, services, and the behavior of the connected user.