Mobile phones are becoming increasingly important in monitoring and delivery of healthcare interventions. They are often considered as pocket computers, due to their advanced computing features, enhanced preferences and diverse capabilities. Their sophisticated sensors and complex software applications make the mobile healthcare (m-health) based applications more feasible and innovative. In a number of scenarios user-friendliness, convenience and effectiveness of these systems have been acknowledged by both patients as well as healthcare providers. M-health technology employs advanced concepts and techniques from multidisciplinary fields of electrical engineering, computer science, biomedical engineering and medicine which benefit the innovations of these fields towards healthcare systems. This paper deals with two important aspects of current mobile phone based sensor applications in healthcare. Firstly, critical review of advanced applications such as; vital sign monitoring, blood glucose monitoring and in-built camera based smartphone sensor applications. Secondly, investigating challenges and critical issues related to the use of smartphones in healthcare including; reliability, efficiency, mobile phone platform variability, cost effectiveness, energy usage, user interface, quality of medical data, and security and privacy. It was found that the mobile based applications have been widely developed in recent years with fast growing deployment by healthcare professionals and patients. However, despite the advantages of smartphones in patient monitoring, education, and management there are some critical issues and challenges related to security and privacy of data, acceptability, reliability and cost that need to be addressed.

Mobile healthcare applications: system design review, critical issues and challenges

mHealth (healthcare using mobile wireless technologies) has the potential to improve healthcare and the quality of life for elderly and chronic patients. Many studies from all over the world have addressed this issue in view of the aging population in many countries. However, there has been a lack of any consolidated evidence-based study to classify mHealth from the dual perspectives of healthcare and technology. This paper reports the results of an evidence-based study of mHealth solutions for chronic care amongst the elderly population and proposes a taxonomy of a broad range of mHealth solutions from the perspective of technological complexity. A systematic literature review was conducted over 10 online databases and the findings were classified into four categories of predominant mHealth solutions, that is, self-healthcare, assisted healthcare, supervised healthcare and continuous monitoring. The findings of the study have major implications for information management and policy development in the context of the Millennium Development Goals (MDGs) related to healthcare in the world.

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mHealth Technologies for Chronic Diseases and Elders: A Systematic Review

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

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From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Mobile, pervasive computing environments respond to users’ requirements by providing access to and composition of various services over networked devices. In such an environment, service composition needs to satisfy a request’s goal, and be mobile-aware even throughout service discovery and service execution. A composite service also needs to be adaptable to cope with the environment’s dynamic network topology. Existing composition solutions employ goal-oriented planning to provide flexible composition, and assign service providers at runtime, to avoid composition failure. However, these solutions have limited support for complex service flows and composite service adaptation. This paper proposes a self-organizing, goal-driven service model for task resolution and execution in mobile pervasive environments. In particular, it proposes a decentralized heuristic planning algorithm based on backward-chaining to support flexible service discovery. Further, we introduce an adaptation architecture that allows execution paths to dynamically adapt, which reduces failures, and lessens re-execution effort for failure recovery. Simulation results show the suitability of the proposed mechanism in pervasive computing environments where providers are mobile, and it is uncertain what services are available. Our evaluation additionally reveals the model’s limits with regard to network dynamism and resource constraints.

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Goal-Driven Service Composition in Mobile and Pervasive Computing

There has been tremendous increase in both the different types of Mobile Health (mHealth) applications and the number of applications being created for both the clinical and consumer healthcare space. The rapid proliferation of mHealth applications is creating confusion in the domain among both consumers and healthcare professionals due to uncertainty about reliability, security, regulation, and integration concerns. New applications are being developed faster than researchers, patients, and healthcare professionals can grasp the multiplicity of the mHealth applications and the various ways they can be used. This paper proposes a taxonomy of existing and emerging mHealth applications to help all users understand this domain.

mHealth taxonomy: a literature survey of mobile health applications

Measurements of vital signs and behavioral patterns can be translated into accurate predictors of health risk, even at an early stage, and can be combined with alarm-triggering systems in order to initiate the appropriate actions. The paper presents the design and implementation of a mobile TeleCare system based on a smart wrist-worn device with a non-obtrusive sensing module for cardiac, respiratory and motor activity, a microcontroller platform for primary processing of the data from the sensors and wireless communication using Bluetooth protocol. Advanced data processing, data management, human computing interfacing and data communication are implemented using a smartphone running Android operating system (OS). A Web based health TeleCare information system was implemented being characterized by the following functionalities: data synchronization with the smartphone, advanced data processing and data presentation assuring a comprehensive data analysis and evidence based health management as well as for remote assistance of the patients by doctors and nurses. Experimental results associated with vital signs sensing and the software implementation are included in the paper.

Fernando Santiago

Papers

Enabling telecare assessment with pervasive sensing and Android OS smartphone