Angel Ruiz-Zafra

Zappa: An Open Mobile Platform to Build Cloud-Based m-Health Systems

Cloud computing and associated services are changing the way in which we manage information and access data. E-health services are not impermeable to novel technologies, especially those that involve mobile devices. At present, many patient monitoring m-health (mobile-health) platforms consist of close, vendor-dependent solutions based on particular architectures and technologies offering a limited set of interfaces to interoperate with. This fact hinders to advance in quality attributes such as customization, adaptation, extension, interoperability and even transparency of cloud infrastructure of existing solutions according to the specific needs of their users (patients and physicians). This paper presents an extensible, scalable, highly-interoperable and customizable platform called Zappa, designed to support e-Health/m-Health systems and that is able to operate in the cloud. The platform is based on components and services architecture, as well as on open and close source hardware and open-source software that reduces its acquisition and operation costs. The platform has been used to develop several remote mobile monitoring m-health systems.

Towards a Model-Driven Approach for Sensor Management in Wireless Body Area Networks

Nowadays, new portable devices are constantly being launched with their ever greater application to an ever growing number of domains. These devices or wearables (from wearable computing) are present in many different areas ranging from healthcare to entertainment, and provide a series of features to enhance the quality of everyday life. When used in conjunction with other wearables, they give rise to wireless body area networks (WBAN) or body area networks (BAN). The large variety of devices along with the lack of standardized services, which means that each designer or engineer must customize the API design, forces developers to implement source code mostly from scratch in order to cope with the heterogeneity of wearables and support their integration on a wider system. The result of these drawbacks is that new device integration is hampered and the time spent on the software development process is increased and these problems are addressed in this paper. We propose a model-driven approach based on a meta-model which has been designed to define and specify interaction with sensors. Our main aim is to distance developers from specific implementation and to cope with heterogeneous designs. The resulting models, which are instances of the proposed meta-model, are specified in a custom language which we call the wearable markup language (WML). We also introduce the coordinator, i.e. component-based software for handling sensor models and improving the integration of new sensors.

A model-driven approach for wearable systems developments

This paper proposes a model-driven approach for developing high-level software interfaces that allow developers to interact with wearable devices easily. These components hide the heterogeneity of the devices interfaces and provide developers with a simple and homogeneous way to interoperate with these digital peripherals. The use of this approach also allows reducing risks and development efforts.

Energy Expenditure Analysis: A Comparative Research of Based on Mobile Accelerometers

Nowadays, the Metabolic Equivalent Task (MET) is the most often used indicator for energy expenditure (EE) calculation of physical activity (PA). The use of novel devices based on inertial movements (e.g. accelerometers) enable the measurement of the PA using “counts”:, where each count is an aggregated value that can be used to determine the number of METs. For some kind of users, such as elderly people or patients in AAL environments, the MET is an important indicator in order to maintain a good health. At present, there exist several types of inertial devices that enable different forms of count calculation and types of exercise monitoring. From the point of view of process analysis and infrastructure needed, they differ in several aspects, such as extra devices required and out-of-device processing. This paper presents a survey analysis about the possibilities of different types of accelerometers to measure EE in AAL contexts. To achieve this objective, we have conducted several experiments based on the performing of different exercises with different accelerometers placed in different body parts.

CloudFit: A Cloud-Based Mobile Wellness Platform Supported by Wearable Computing

Health and wellness area is an emerging social concern. The emergence of Cloud Computing and the growth of new technologies as smartphones and all kinds of wearable devices have given rise to delocalized health and wellness management systems and applications. Most of these systems, which are used by users on their own, are designed to track the exercises, monitor the physiological variables or as dietary diary with the aim to change the diary habits of users to improve their health and wellness, that could also be enhanced by the participation of expert advisors in the supervision of these activities.

A Comparative Study on the Suitability of Smartphones and IMU for Mobile, Unsupervised Energy Expenditure Calculi

The metabolic equivalent of task (MET) is currently the most used indicator for measuring the energy expenditure (EE) of a physical activity (PA) and has become an important measure for determining and supervising a person’s state of health. The use of new devices which are capable of measuring inertial movements by means of built-in accelerometers enable the PA to be measured objectively on the basis of the reckoning of “counts”. These devices are also known as inertial measurement units (IMUs) and each count is an aggregated value indicating the intensity of a movement and can be used in conjunction with other parameters to determine the MET rate of a particular physical activity and thus it’s associated EE. Various types of inertial devices currently exist that enable count calculus and physical activity to be monitored. The advent of mobile devices, such as smartphones, with empowered computation capabilities and integrated inertial sensors, has enabled EE to be measure in a distributed, ubiquitous and natural way, thereby overcoming the reluctance of users and practitioners associated with in-lab studies. From the point of view of the process analysis and infrastructure needed to manage data from inertial devices, there are also various differences in count computing: extra devices are required, out-of-device processing, etc. This paper presents a study to discover whether the estimation of energy expenditure is dependent on the accelerometer of the device used in measurements and to discover the suitability of each device for performing certain physical activities. In order to achieve this objective, we have conducted several experiments with different subjects on the basis of the performance of various daily activities with different smartphones and IMUs.

Cloud and Web Services Integration for mHealth Telerehabilitation Support

Cloud Computing and mobile technology have become an integral part of society, changing how we interact with devices and each other. In this context, users are able to connect with other users/devices anywhere and anytime, taking advantage of endless possibilities in different areas. One of these areas is healthcare, where cloud features can cover important healthcare requirements such as information exchange, security, privacy and scalability of solutions to support users’ needs. In this paper we introduce a Cloud-supported e-Rehabilitation platform for Brain-Injured patients and health professionals. The goal of the platform is the improvement of the quality of life of patients, providing asynchronous remote interaction between health professionals and patients.