Faizan Ur Rehman

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 GIS-based serious game recommender for online physical therapy

As human-centered interactive technologies, serious games are getting popularity in a variety of fields such as training simulations, health, national defense, and education. To build the best learning experience when designing a serious game, a system requires the integration of accurate spatio-temporal information. Also, there is an increasing need for intelligent medical technologies, which enable patients to live independently at home. This paper introduces a novel e-Health framework that leverages GIS-based serious games for people with disabilities. This framework consists of a spatial map-browsing environment augmented with our newly introduced multi-sensory Natural User Interface. We propose a comprehensive architecture that includes a sensory data manager, a storage layer, an information processing and computational intelligence layer, and a user interface layer. Detailed mathematical modeling as well as mapping methodology to convert different therapy-based hand-gestures into navigational movements within the serious game environment are also presented. Moreover, an Intelligent Game Recommender has been developed for generating optimized navigational routes based on therapeutic gestures. Motion data is stored in a repository throughout the different sessions for offline replaying and advanced analysis; and different indicators are displayed in a live manner. This framework has been tested with Nokia, Google maps, ESRI map, and other maps whereby a subject can visualize and browse the 2D and 3D map of the world through therapy-based gestures. To the best of our knowledge, this is the first GIS-based game re-commender framework for online physical therapy. The prototype has been deployed to a disability center. The obtained results and feedback from therapists and patients are very encouraging.

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.

A Multimedia E-Health Framework Towards An Interactive And Non-Invasive Therapy Monitoring Environment

This paper presents a multimedia e-health framework to conduct therapy sessions by collecting live therapeutic data of patients in a non-invasive way. Using our proposed framework, a therapist can model complex gestures by mapping them to a set of primitive actions and generate high-level therapies. Two inexpensive 3D motion tracking sensors, a Kinect and a Leap, are used to collect motion data of a given subject. Data can then be displayed on screen in a live manner or recorded for offline replaying and analysis. The system incorporates an intelligent authoring tool for therapy design, and produces live plots that show the quality of improvement metrics for a given patient.

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.

Building Multi-Resolution Event-Enriched Maps From Social Data.

This paper discusses the next generation of digital maps, by positing that maps in future will intelligently self-update themselves based on distinctive events extracted dynamically from social media streams or other crowd-sourced data. To realize this concept, the challenges include developing a scalable and efficient system to deal with a variety of unstructured data streams, applying NLP and clustering techniques to extract relevant information from these streams, and inferring the spatio-temporal scope of detected events. This paper demonstrates Hadath, a system that extracts live events from social data by encapsulating incoming unstructured data into generic data packets. The system implements a hierarchical in-memory indexing scheme to support efficient access to data packets, as well as for memory flushing purposes. Data packets are then processed to extract Events of Interest (EoI), based on a multi-dimensional clustering technique. Next, we establish the spatial scope and the level of abstraction of each event. This allows us to show live events in correspondence to the scale of the view – when viewing at a city scale, we see events of higher significance, while zooming in to a neighborhood highlights events of a more local interest. The final output creates a unique and dynamic map browsing experience.

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.

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.

Context-aware services based on spatio-temporal zoning and crowdsourcing

ABSTRACT Crowdsourcing offers great opportunities to recognise user context and prescribe relevant services for both offline and real-time activities. In this work, we present a zoning model that leverages spatio-temporal dimensions and then employs different contexts to recommend necessary customised services. The context model takes into consideration three context sets: fully restricted, fully unrestricted and semi-restricted with respect to both spatial and temporal dimensions. As a proof of concept, we apply this zoning model in a scenario where a very large crowd get together to perform spatio-temporal activities. The user context of the heterogeneous crowd is captured using the carried smartphones, i.e. via crowdsourcing. Depending on the context sets and zone, the system can recommend a set of services to each user. The system has been deployed since 2014 to support the spatio-temporal activities of a very large crowd. We present our implementation details and the user feedback, which is very encouraging.

Understanding the Spatio-Temporal Scope of Multi-scale Social Events

The age-old practice of cartography has undergone fundamental shifts with the advent of the digital age. Todays digital maps are often crowd-sourced, allow interactive route planning, and may contain live updates, such as traffic congestion state. In this paper, we take the concept of maps one step further by introducing a new generation of maps, which contain the additional functionality of showing multi-resolution spatio-temporal events, extracted dynamically from social media streams. Building such maps requires developing a scalable and efficient system to deal with a variety of unstructured data streams, applying sentiment analysis and multi-dimensional clustering techniques to extract relevant Events of Interest (EoI) at different map scales, and inferring the spatio-temporal scope of detected events. This paper presents a novel system that extracts events from social data at different levels of spatio-temporal granularities. The system implements a hierarchical in-memory spatio-temporal indexing scheme to support efficient access to data streams, as well as for memory flushing purposes. Data streams are first processed to extract events at a local scale. Next, we determine the proper spatio-temporal scope and the level of abstraction for detected events at a global scale. This allows us to show live multi-resolution events in correspondence to the scale of the view -- when viewing at a city scale, we see events of higher significance, while zooming in to a neighborhood highlights events of a more local interest.