Regular monitoring of vital signs guarantees a preventive treatment of common diseases ensuring better health for people. Most of the proposed solutions in e-health context are based on a set of heterogeneous wireless sensors, fitting the patient and his environment. Often, these sensors are connected to a local smart node acting as a gateway to the outside (contacts, servers). When the patient is mobile, one of the issues we may face is the guarantee of a permanent connectivity between local smart node and the outside. To overcome this problem, we need to define a robust communications architecture able to benefit from different technologies and standards. This provides equipments with the ability to dispose of free-bands to perform their transmission any-time and anywhere. Cognitive radio, although appropriate technology, requires taking into account the interdependence between the patient’s mobility and frequency band changes. Our proposal, is an anticipation model, a decision-making function that predicts the state of frequency bands occupancy. The model combines the machine learning techniques to the Grey Model system to provide low cost algorithm for spectral prediction which facilitates or guarantees permanent connectivity.

The need for high-quality on-the-spot emergency care necessitates access to reliable broadband connectivity for emergency telemedicine services used by paramedics in the field. In a significant proportion of recorded cases, these medical emergencies would tend to occur in indoor locations. However,broadband wireless connectivity may be of low quality due to poor indoor coverage of macro-cellular public mobile networks, or may be unreliable and/or inaccessible in the case of private Wi-Fi networks. To that end, relaying is emerging as one of promising radio access network techniques that provide coverage gain with improved quality of service. This paper analyzes the use of nomadic relays that could be temporarily deployed close to a building as part of the medical emergency response. The objective is to provide improved indoor coverage for paramedics located within the building for enhanced downlink performance (throughput gain, lower outage probability). For that scenario, we propose a resource sharing algorithm based on static relay link with exclusive assigned subframes at the macro base station (MBS) coupled with access link prioritization for paramedic’s terminals to achieve max-min fairness. Via comprehensive system-level simulations, incorporating standard urban propagation models, the results indicate that paramedics are always able to obtain improved performance when connected via the relay enhanced cell (REC) networks rather than the MBS only.

This paper describes the user centered development of a mobile assistance and supervision system for cardiac disease patients. Smartphones are used to collect data from wireless sensors like ECG, blood pressure or oxygen saturation sensors while a patient is exercising outdoors. All data from the wireless sensors as well as GPS information is sent to a supervision center where doctors and sport therapists analyze the data in a collaborative and interactive setting. We here present the User Centered Design process, the technical realization as well as the interaction modalities of our system.

In industrialized countries the share of elderly subjects is increasing. Hence, diseases or symptoms associated with aging are more common than they were in the past. As a consequence, more effort is invested into research analyzing the effects of aging on the motion and cognition. However, economical and flexible methods to measure motion and its cross-effects with cognition are still missing. Therefore, we developed a new approach which neither requires a specific location, large infrastructural requirements, nor does it require large investments. We base our setting on match-box sized inertial measurement units (IMUs) attached to the participants’ legs. 47 elderly subjects participated in our study where we analyzed the interplay between cognitive load and gait features. We show that it is feasible to automatically detect episodes of interest, e.g. straight path, during walking periods of a subject only using IMU data. Our approach detects the steps autonomously and calculates gait features without supervision. The results demonstrate that cognitive load induces a significant increase ( = 0.007) in step-duration variability from 16 (baseline) to 21 (load). Our findings demonstrate that IMUs are a proved alternative to static setups that usually require a non-trivial infrastructure, e.g. optical movement tracking.

The disparity between healthcare systems in developed countries and underdeveloped countries is huge, particularly due to the fact that the healthcare infrastructure of former is based on a sophisticated technological infrastructure. Efforts are being made worldwide to bridge this disparity and make healthcare services affordable even to the most remote areas of undeveloped countries. Recent growth of mobile networks in underdeveloped countries argues for building mHealth systems and applications on their basis. However, peculiarities of the area introduce difficulties into potential use cases of mobile devices, thus making the copying of mHealth services from developed countries inapplicable. In this paper, we present the functional requirements for a patient medical record system running partially on mobile devices and architectural choices for this system. Requirements are based on the circumstances of the Republic of Cameroon. In addition we discuss the country-level benefits that may appear after deployment of such a system.