The authors introduce an algorithmic framework to process real-time physiological data using nonparametric Bayesian models under the context of developing and testing personalized wellness monitors.... Show More

The authors introduce an algorithmic framework to process real-time physiological data using nonparametric Bayesian models under the context of developing and testing personalized wellness monitors. A wearable device aggregates signals from various sensors while periodically transmitting the collected data to a backend server, which builds custom user profiles based on inferred hidden Markov states. They discuss how these user profiles can be used in various contexts as proxies for fluctuating physiological states and leveraged for various longitudinal classification tasks. Using data collected in a two-week study hosted at Jaslok Hospital, the authors show how physiological changes induced by different environments with various levels of stress can be quantified by the authors' platform. To minimize the dependence on continuous connectivity with the backend server, they introduce a heuristic to enable real-time state identification using the modest processing capabilities of the wearable device.

Wireless sensor networks are subject to different types of faults and interferences after their deployment. Abnormal values reported by sensors should be separated from faulty or injected... Show More

Wireless sensor networks are subject to different types of faults and interferences after their deployment. Abnormal values reported by sensors should be separated from faulty or injected measurements to ensure reliable monitoring operation. The aim of this paper is to propose a lightweight approach for the detection and suppression of faulty measurements in medical wireless sensor networks. The proposed approach is based on the combination of statistical model and machine learning algorithm. The authors begin by collecting physiological data and then they cluster the data collected during the first few minutes using the Gaussian mixture decomposition. They use the resulted labeled data as the input for the Ant Colony algorithm to derive classification rules in the central base station. Afterward, the derived rules are transmitted and installed in each associated sensor to detect abnormal values in distributed manner, and notify anomalies to the base station. Finally, the authors exploit the spatial and temporal correlations between monitored attributes to differentiate between faulty sensor readings and clinical emergency. They evaluate their approach with real and synthetic patient datasets. The experimental results demonstrate that their proposed approach achieves a high rate of detection accuracy for clinical emergency with reduced false alarm rate when compared to robust Mahalanobis distance.

Biomedical wireless sensor networks are a key technology to enable the development of new healthcare services and/or applications, reducing costs and improving the citizen's quality of life.... Show More

Biomedical wireless sensor networks are a key technology to enable the development of new healthcare services and/or applications, reducing costs and improving the citizen's quality of life. However, since they deal with health data, such networks should implement mechanisms to enforce high levels of quality of service. In most cases, the sensor nodes that form such networks are small and battery powered, and these extra quality of service mechanisms mean significant lifetime reduction due to the extra energy consumption. The network lifetime is thus a relevant feature to ensure the necessary quality of service requirements. In order to maximise the network lifetime, and its ability to offer the required quality of service, new strategies are needed to increase the energy efficiency, and balance in the network. The focus of this work goes to the effective use of the available energy in each node, combined with information about the reliability of the wireless links, as a metric to form reliable and energy-aware routes throughout the network. This paper present and discusses two different deployment strategies using energy-aware routing and relay nodes, assessed for different logical topologies. The authors' conclusion is that the use of energy-aware routing combined with strategic placed relay nodes my increase the network lifetime as high as 45%.

This paper presents an implementation of a Low Power Listening-based (LPL) Medium Access Control (MAC) protocol on a platform for Body Area Network (BAN) applications. LPL exploits the transmission... Show More

This paper presents an implementation of a Low Power Listening-based (LPL) Medium Access Control (MAC) protocol on a platform for Body Area Network (BAN) applications. LPL exploits the transmission of a burst of short packets, called preambles, to synchronize the transmitter and the receiver. In this way, devices are able to spend most of the time in sleeping mode, providing longer lifetime and energy saving. Experiments on the field have been conducted by considering different scenarios and results, in terms of average energy consumed per packet transmitted/received, packet loss rate, average delay and network throughput, have been investigated. Conclusions regarding the proper parameters setting depending on the application requirements were derived. This work has been performed in the framework of the FP7 Integrated Project, WiserBAN.

This work further investigates paradigm of radiation awareness three-dimentionnel models for WBAN network environments. The authors incorporate the effect of dynamic topology as well as the time... Show More

This work further investigates paradigm of radiation awareness three-dimentionnel models for WBAN network environments. The authors incorporate the effect of dynamic topology as well as the time domain and environment aspects. Even, if the impact of radiation to human health remains largely unexplored and controversial. They ask two fundamental issues, (a) deployment and (b) information routing taking into account radiation awarness. The authors first propose a multi objectives flow model which allows describing a new optimal deployment model for WBAN sensor devices with dynamic topology and the relevant possible trade-offs between coverage, connectivity, network life time while maintaining at low levels the radiation cumulated by wireless transmissions. They propose oblivious deployment heuristics that are radiation aware. The authors then combine them with dynamic spectrum management is proposed based multi-commodity flow model which allows to prevent sensor node saturation and take best action against reliability and the path loss, by imposing an equilibrium use of sensors during the routing process in order to “spread” radiation in a spatio-temporal way. Experimental results show that the proposed models and algorithms balances the energy consumption of nodes effectively, maximize the network lifetime . It will meet the enhanced WBANs requirements, including better delivery ratio, less reliable routing overhead. Their proposed radiation aware deployment and routing heuristics succeed to keep radiation levels low.

To cope with the needs raised by the demographic changes in our society, several Ambient-Assisted Living (AAL) technologies have emerged in recent years, but those ‘first offers' are often... Show More

To cope with the needs raised by the demographic changes in our society, several Ambient-Assisted Living (AAL) technologies have emerged in recent years, but those ‘first offers' are often monolithic, incompatible and thus expensive and potentially not sustainable. The AAL4ALL project aims at improving that situation through the development of an open ecosystem of interoperable AAL components (products and services), tied together by an integration infrastructure, comprising a message-queue based service bus and gateways bridging the communication with devices. To that end, the project encompasses the specification of interfaces and requirements for interoperable components, against which candidates can be tested and certified before entering the ecosystem. This paper proposes a testing and certification methodology for such an ecosystem. Besides fulfilling specified pre-requisites, candidate components must pass unit tests that check their conformance with interface specifications and integration tests that check their semantic interoperability with other components in specified orchestration scenarios.

Information and communications technologies are a significant component of the healthcare domain, and electronic health records play a major role in it. Therefore, it is important that they are... Show More

Information and communications technologies are a significant component of the healthcare domain, and electronic health records play a major role in it. Therefore, it is important that they are accepted en masse by healthcare professionals. How healthcare professionals perceive the usefulness of electronic health records and their attitudes towards them have been shown to have significant effects on the overall acceptance in many healthcare systems around the world. This paper investigates the role of perceived usefulness and attitude on the intention to use electronic health records by future healthcare professionals using polynomial regression with response surface analysis. Results show that the relationships between these variables are more complex than predicted in prior research. The paper concludes that the properties of the above determinants must be further investigated to clearly understand: (i) their role in predicting the intention to use electronic health records; and (ii) in designing systems that are better adopted by healthcare professionals of the future.