Muhammad Bilal Amin

Health Fog: a novel framework for health and wellness applications

In the past few years the role of e-health applications has taken a remarkable lead in terms of services and features inviting millions of people with higher motivation and confidence to achieve a healthier lifestyle. Induction of smart gadgetries, people lifestyle equipped with wearables, and development of IoT has revitalized the feature scale of these applications. The landscape of health applications encountering big data need to be replotted on cloud instead of solely relying on limited storage and computational resources of handheld devices. With this transformation, the outcome from certain health applications is significant where precise, user-centric, and personalized recommendations mimic like a personal care-giver round the clock. To maximize the services spectrum from these applications over cloud, certain challenges like data privacy and communication cost need serious attention. Following the existing trend together with an ambition to promote and assist users with healthy lifestyle we propose a framework of Health Fog where Fog computing is used as an intermediary layer between the cloud and end users. The design feature of Health Fog successfully reduces the extra communication cost that is usually found high in similar systems. For enhanced and flexible control of data privacy and security, we also introduce the cloud access security broker (CASB) as an integral component of Health Fog where certain polices can be implemented accordingly. The modular framework design of Health Fog is capable of engaging data from multiple resources together with adequate level of security and privacy using existing cryptographic primitives.

An Adaptive Semantic based Mediation System for Data Interoperability among Health Information Systems

Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteRInteroperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.

The Mining Minds digital health and wellness framework

BackgroundThe provision of health and wellness care is undergoing an enormous transformation. A key element of this revolution consists in prioritizing prevention and proactivity based on the analysis of people’s conducts and the empowerment of individuals in their self-management. Digital technologies are unquestionably destined to be the main engine of this change, with an increasing number of domain-specific applications and devices commercialized every year; however, there is an apparent lack of frameworks capable of orchestrating and intelligently leveraging, all the data, information and knowledge generated through these systems.MethodsThis work presents Mining Minds, a novel framework that builds on the core ideas of the digital health and wellness paradigms to enable the provision of personalized support. Mining Minds embraces some of the most prominent digital technologies, ranging from Big Data and Cloud Computing to Wearables and Internet of Things, as well as modern concepts and methods, such as context-awareness, knowledge bases or analytics, to holistically and continuously investigate on people’s lifestyles and provide a variety of smart coaching and support services.ResultsThis paper comprehensively describes the efficient and rational combination and interoperation of these technologies and methods through Mining Minds, while meeting the essential requirements posed by a framework for personalized health and wellness support. Moreover, this work presents a realization of the key architectural components of Mining Minds, as well as various exemplary user applications and expert tools to illustrate some of the potential services supported by the proposed framework.ConclusionsMining Minds constitutes an innovative holistic means to inspect human behavior and provide personalized health and wellness support. The principles behind this framework uncover new research ideas and may serve as a reference for similar initiatives.

On curating multimodal sensory data for personalized health and wellness services

In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user’s lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user’s sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user’s lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data.

Behavior Life Style Analysis for Mobile Sensory Data in Cloud Computing through MapReduce

Cloud computing has revolutionized healthcare in todays world as it can be seamlessly integrated into a mobile application and sensor devices. The sensory data is then transferred from these devices to the public and private clouds. In this paper, a hybrid and distributed environment is built which is capable of collecting data from the mobile phone application and store it in the cloud. We developed an activity recognition application and transfer the data to the cloud for further processing. Big data technology Hadoop MapReduce is employed to analyze the data and create user timeline of users activities. These activities are visualized to find useful health analytics and trends. In this paper a big data solution is proposed to analyze the sensory data and give insights into user behavior and lifestyle trends.

Personalized-Detailed Clinical Model for Data Interoperability Among Clinical Standards

OBJECTIVE Data interoperability among health information exchange (HIE) systems is a major concern for healthcare practitioners to enable provisioning of telemedicine-related services. Heterogeneity exists in these systems not only at the data level but also among different heterogeneous healthcare standards with which these are compliant. The relationship between healthcare organization data and different heterogeneous standards is necessary to achieve the goal of data level interoperability. We propose a personalized-detailed clinical model (P-DCM) approach for the generation of customized mappings that creates the necessary linkage between organization-conformed healthcare standards concepts and clinical model concepts to ensure data interoperability among HIE systems. MATERIALS AND METHODS We consider electronic health record (EHR) standards, openEHR, and HL7 CDA instances transformation using P-DCM. P-DCM concepts associated with openEHR and HL7 CDA help in transformation of instances among these standards. We investigated two datasets: (1) data of 100 diabetic patients, including 50 each of type 1 and type 2, from a local hospital in Korea and (2) data of a single Alzheimers disease patient. P-DCMs were created for both scenarios, which provided the basis for deriving instances for HL7 CDA and openEHR standards. RESULTS For proof of concept, we present case studies of encounter information for type 2 diabetes mellitus patients and monitoring of daily routine activities of an Alzheimers disease patient. These reflect P-DCM-based customized mappings generation with openEHR and HL7 CDA standards. Customized mappings are generated based on the relationship of P-DCM concepts with CDA and openEHR concepts. CONCLUSIONS The objective of this work is to achieve semantic data interoperability among heterogeneous standards. This would lead to effective utilization of resources and allow timely information exchange among healthcare systems.

An innovative platform for person-centric health and wellness support

Modern digital technologies are paving the path to a revolutionary new concept of health and wellness care. Nowadays, many new solutions are being released and put at the reach of most consumers for promoting their health and wellness self-management. However, most of these applications are of very limited use, arguable accuracy and scarce interoperability with other similar systems. Accordingly, frameworks that may orchestrate, and intelligently leverage, all the data, information and knowledge generated through these systems are particularly required. This work introduces Mining Minds, an innovative framework that builds on some of the most prominent modern digital technologies, such as Big Data, Cloud Computing, and Internet of Things, to enable the provision of personalized healthcare and wellness support. This paper aims at describing the efficient and rational combination and interoperation of these technologies, as well as their integration with current and future personalized health and wellness services and business.

Human Behavior Analysis by Means of Multimodal Context Mining

There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.

SPHeRe: A Performance Initiative Towards Ontology Matching by Implementing Parallelism over Cloud Platform

The abundance of semantically related information has resulted in semantic heterogeneity. Ontology matching is among the utilized techniques implemented for semantic heterogeneity resolution; however, ontology matching being a computationally intensive problem can be a time-consuming process. Medium to large-scale ontologies can take from hours up to days of computation time depending upon the utilization of computational resources and complexity of matching algorithms. This delay in producing results, makes ontology matching unsuitable for semantic web-based interactive and semireal-time systems. This paper presents SPHeRe, a performance-based initiative that improves ontology matching performance by exploiting parallelism over multicore cloud platform. Parallelism has been overlooked by ontology matching systems. SPHeRe avails this opportunity and provides a solution by: (i) creating and caching serialized subsets of candidate ontologies with single-step parallel loading; (ii) lightweight matcher-based and redundancy-free subsets result in smaller memory footprints and faster load time; and (iii) implementing M.B. Amin · R. Batool · W.A. Khan · S. Lee (B) Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, Global Campus, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, South Korea e-mail: sylee@oslab.khu.ac.kr M.B. Amin e-mail: mbilalamin@oslab.khu.ac.kr R. Batool e-mail: rabia@oslab.khu.ac.kr W.A. Khan e-mail: wajahat.alikhan@oslab.khu.ac.kr E.-N. Huh Internet Computing and Network Security Lab, Department of Computer Engineering, Kyung Hee University, Global Campus, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, South Korea e-mail: johnhuh@khu.ac.kr

Healthcare standards based sensory data exchange for Home Healthcare Monitoring System

Interoperability is the among the key functionalities of an intelligent systems. Home Healthcare Monitoring Systems (HHMS) investigates patients activities at home, but lacks critical information exchange with Health Management Information System (HMIS). This information is vital for physicians to take necessary steps for timely and effective healthcare provisioning for patients. Physicians can only monitor and prescribe patients in time, if the data is shared with their HMIS. HMIS can be compliant to different healthcare standards. Therefore, mediation system is required to enable interoperability between HHMS and HMIS such that physicians and patients information can easily be exchanged. We propose Interoperability Mediation System (IMS) that provides interoperability services for exchange of information among HHMS and HMIS. We consider that HMIS are compliant to two heterogeneous EHR standards (HL7 CDA and openEHR). Alzheimers patient case study is described as a proof of concept. Sensory information gathered at HHMS, is communicated with HMIS compliant to EHR based healthcare standards. Sensors information in XML form is converted by interoperability service to HL7 CDA and openEHR instances and communicated to HMIS afterwards. This allows the physicians registered with HHMS to monitor the patient using their HMIS and provide timely healthcare information.