Bilal Sadiq

Visualization of a Scale Free Network in a Smartphone-Based Multimedia Big Data Environment

Smartphones equipped with varieties of sensors enable them in participatory and opportunistic crowd-sourced sensing. The built-in as well as external sensors paired with modern smartphones provide an ideal multimedia big data source, where a very large crowd can share audio, video, text, SMS, location, etc. In this paper, we will illustrate our proposed big data framework that has been storing multimedia data from a very large crowd since September 2014. Our framework uses Scale Free Network (SFN) to represent the dynamics of large crowd and produces visualization metrics by running spatio-temporal queries over the proposed multimedia big data framework.

A Multimedia Big Data E-Therapy Framework

Due to the low cost and high availability of wearable health sensors and motion tracking devices, home based therapy monitoring has come to a reality. In this paper, we propose a gesture controlled e-therapy online framework that can monitor physical and occupational therapy exercises using multimedia data produced by different sensors such as Kinect2, Leap, and Myo. The multimedia therapeutic data is then stored in a big data repository with proper annotation. We have developed analytics to mine therapeutic information from the big data platform, such as finding the most appropriate therapy regime for a patient, based on her age, ethnicity, gender, disability level and geo-spatial location. We will show key queries that can be answered by our developed analytics.

Crowdsourced Multimedia Enhanced Spatio-temporal Constraint Based on-Demand Social Network for Group Mobility

This paper presents a system that enables efficient and scalable real-time user and vehicle discovery using textual, audio and video mechanisms. The system allows users to group together for shared intra-city transportation with the aid of multimedia that helps individuals to 1) find community of common interest (CoCI), 2) locate individual users in a large crowd and 3) locate vehicles for mobility in an efficient and cost effective manner. The system is a pilot project and will be deployed during Hajj 2015 when over three million pilgrims from all over the world visit Makkah, Saudi Arabia.

A constraint-aware optimized path recommender in a crowdsourced environment

Recommending an optimized path for a large crowd poses a unique challenge to existing routing algorithms due to the interactions between users and the dynamic changes over road networks. Industries, researchers and end users show an enormous interest in crowdsourced data comprising social networks and user-generated content to remain updated with their concerns. In this paper, we present a data collection framework that helps users to find optimized routes in a dynamic environment. We have developed a data collection framework to collect dynamic road conditions via a set of location-based services to support a very large Hajj crowd by capturing their locations using smartphones. We also collect geotagged social network data that provides more details about road conditions. The system leverages geotagged crowdsourced information to identify constraints such as accidents, congestions, and roadblocks. Moreover, by continuously collecting real-time geotagged data of moving users, the system can also find the flow of traffic and road conditions. We propose a spatial grid index to compute the optimized path, and to identify the affected users within impact zones. The plan is to test the whole application and back-end server during Hajj 2016, where over three million pilgrims from all over the world gather to perform their rituals.

Motion-Based Serious Games for Hand Assistive Rehabilitation

Cerebral Palsy, trauma, and strokes are common causes for the loss of hand movements and the decrease in muscle strength for both children and adults. Improving fine motor skills usually involves the synchronization of wrists and fingers by performing appropriate tasks and activities. This demo introduces a novel patient-centered framework for the gamification of hand therapies in order to facilitate and encourage the rehabilitation process. This framework consists of an adaptive therapy-driven 3D environment augmented with our motion-based natural user interface. An intelligent game generator is developed, which translates the patients gestures into navigational movements with therapy-driven goals, while adapting the level of difficulty based on the patient profile and real-time performance. A comprehensive evaluation and clinical-based assessments were conducted in a local children disability center, and highlights of the results are presented.

A therapy-driven gamification framework for hand rehabilitation

Rehabilitative therapy is usually very expensive and confined to specialized rehabilitation centers or hospitals, leading to slower recovery times for corresponding patients. Therefore, there is a high demand for the development of technology-based personalized solutions to guide and encourage patients towards performing online rehabilitation program that can help them live independently at home. This paper introduces an innovative e-health framework that develops adaptive serious games for people with hand disabilities. The aim of this work is to provide a patient-adaptive environment for the gamification of hand therapies in order to facilitate and encourage rehabilitation issues. Theoretical foundations (i.e., therapy and patient models) and algorithms to match therapy-based hand gestures to navigational movements in 3D space within the serious game environment have been developed. A novel game generation module is introduced, which translates those movements into a 3D therapy-driven route on a real-world map and with different levels of difficulty based on the patient profile and capabilities. In order to enrich the user navigation experience, a 3D spatio-temporal validation region is also generated, which tracks and adjusts the patient movements throughout the session. The gaming environment also creates and adds semantics to different types of attractive and repellent objects in space depending on the difficulty level of the game. Relevant benchmarks to assess the patient interaction with the environment along with a usability and performance testing of our framework are introduced to ensure quantitative as well as qualitative improvements. Trial tests in one disability center were conducted with a total number of five subjects, having hand motor controls problems, who used our gamified physiotherapy solution to help us in measuring the usability and users’ satisfaction levels. The obtained results and feedback from therapists and patients are very encouraging.

Spatial-crowd: A big data framework for efficient data visualization

Analyzing and visualizing large datasets generated by real-time spatio-temporal activities (e.g. vehicle mobility or large crowd movement) are a very challenging task. Recursive delays both at middleware and front end applications limit the of usefulness of the real-time analysis. In this paper, we present a framework “Spatial-Crowd” that first handles spatial-temporal data acquisition and processing by scaling up the middleware components and its infrastructure. Then, it enables filtering, fixing, enriching and summarising the acquired dataset, readily available for client interfaces which usually are not scalable or built to manage such large datasets. This framework follows published subscriber model and allows users to subscribe to aggregated data streams instead of requesting data in real time. The framework is tested with data generated by a very large simulated dataset and performance showed a significant data reduction on the client side to enhance data visualisation.

A Multi-sensory Gesture-Based Login Environment

Logging on to a system using a conventional keyboard may not be feasible in certain environments, such as, in a surgical operation theatre or in an industrial manufacturing facility. We have developed a multi-sensory gesture based login system that allows a user to access secure information using body gestures. The system can be configured to use different types of gestures according to the type of sensors available to the user. We have proposed a simple scheme to represent all alphanumeric characters required for password entry as gestures within the multi-sensory environment. Our scheme is scalable enough to support sensors that detect a large number of gestures to those that can only accept a few. This allows the system to be used in a variety of situations such as usage by disabled persons with limited ability to perform gestures. We are in the midst of deploying our developed system in a clinical environment.

Scale Free Network Analysis of a Large Crowd through Their Spatio-Temporal Activities

Many real world complex networks from different domains share a common property that their node connectivity shows a scale-free power law behavior. In such networks, highly connected nodes (Hubs) are widely believed to have special importance in network management. In this paper, we discuss an environment whereby members of a very large crowd gathered to perform spatio-temporal activities, interact with different services and with one another to form a network of interest. The context of users is captured through smartphones and is processed by a cloud based framework to identify the aforementioned Hubs. We show that initial results exhibit Scale Free Network (SFN) behavior that can be further utilized for instant diffusion of important messages within the network through successive allocation of Hubs. We will focus on two basic network analysis metrics, in particular, degree of nodes and their weighted links. We will show that weighted links are closer to have a SFN behavior. We also plan to validate the effectiveness of our proposed SFN crowd behavior during next year Hajj, where millions of pilgrims will get together to perform religious rituals.