Akhlaq Ahmad

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 spatio-temporal 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 mod-eling 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. Those routes are tailored to the patient preferences and capabilities. 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. To the best of our knowledge, this is the first GIS-based game recommender framework for online physical therapy. The prototype has been deployed to a disability center. The obtained results and feedback from therapists and patients are encouraging.

Mobile crowdsourcing offers great opportunities when considering users' contexts with the aim of recognizing their interests, locations, history, and activities. This paper introduces a mobile data collection scheme that takes into account users' spatio-temporal activities, as well as other contextual dimensions towards the design of quality and context-aware crowdsourcing framework. We propose a framework that offers a set of cloud-based services as an incentive to support a very large crowd in a real scenario by capturing their contexts using smartphones. The proposed framework captures data from an individual, validates its quality by different measures to define his context and provides a set of adapted services to end users. The framework allows being in touch with a subset of one's community of interest. We leverage an approach requiring restricted spatio-temporal zoning model that applies to the scenario where millions of users need to perform some activities in a...

–We propose a context aware framework that offers a set of cloud-based services to support a very large Hajj and Umrah crowd by capturing their contexts using smartphones. The proposed framework captures the individual's context, provides a set of adapted services, and allows being in touch with a subset of one's community of interest. We leverage the spatio-temporal sensory data captured by our framework to define users' contexts for optimized services. Our proposed framework is also envisioned to assist the Hajj and Umrah authorities to (1) improve Hajj & Umrah documentation, (2) improve Hajj organization through better understanding of pilgrims' (individual and crowd) spatial and temporal behavior and needs, and (3) protect pilgrims' environment through environmental monitoring. In particular, the developed methods, techniques, and algorithms will support the pilgrimage quality of experience. We have tested our system through end-user subjects and due to apply for the upcoming Hajj events. We present our implementation details and the general impression of end users about our system.

Capturing users’ spatio-temporal context by recognizing their interests, locations, history and activities, and thereafter providing context-aware services is a challenging task. In this paper, we propose a spatio-temporal zoning model that takes different context dimensions into account and try to recommend necessary services to users in a personalized way. First, we propose a generic zoning model with unrestricted set of contexts where both spatial and temporal dimensions are relaxed, followed by two semi-restricted zoning models in which either spatial or temporal dimension is relaxed, while the other one is restricted. Finally, we show the model requiring restricted spatio-temporal zoning that applies to the scenario where millions of users need to perform some activities that have to be performed in a certain location and at a certain temporal period. We use the above zoning model for Hajj and Umrah events to define pilgrim’s spatio-temporal contexts by capturing their real-time and historic activities through their smartphones’ sensory data. This allows to intelligently recommend a set of necessary services to the users. We present a few of the implementations introduced in our proposed system.

—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.

Customer service improvement is directly related with organizational standards and productivity. Employees activities have certain objectives to be followed but sometimes outcomes are different than expected. Human error while performing regular job activities cause sufficient losses and difficult to address. Management faces real challenges while dealing with employee related issues and sometimes becomes unproductive. We propose Brahms Model with multi layered alert filtration and feedback cycle which would address the human error in the system and generate the filtered alerts in the form of sms or by emails. This is especially useful for Human-Computer Interfacing (HCI) Scenarios. Controlling human error and addressing real issues may protect any institution from severe damage and losses. Efficiency of a customer service department can be improved and maximized by multi-layered alert filtration system using Brahms Model.

Every organisation set some objectives to achieve success which is closely related with the quality assurance. Human behavior brought many challenges in variety of situations in an organisation which is sometimes untraceable phenomena. The real challenge is to control the collective human behavior at customer service department in an organisation which may pose a serious threat to the top management in terms of its defined objectives. Human behavioral activities can be monitored using Brahms Model Technique along with the Warning Alert Generation System which would maximize the efficiency and improve the system performance at customer service level. In this paper, we propose context aware security measure which would complement the system performance in terms of human error detection and rectification. Generating timely alerts using context aware security measure along with Brahms Model would improve the system performance.