Mobile solution for three-tier biofeedback data acquisition and processing

“Copyright © [2008] IEEE. Reprinted from Global Telecommunications Conference. (GLOBECOM 2008).IEEE ISBN:978-1-4244-2324-8. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.”Wireless sensor networks span from military applications into everyday life. Body sensor networks greatly benefit from wireless sensor networks to answer the biofeedback challenges in healthcare applications. In such applications, data is of fundamental importance, it must be reliable and within easy reach. However, most solutions rely on a personal computer to process and display sensor data. In this paper we propose a mobile solution that draws on three-tier body sensor networks to dramatically improve data accessibility, through the use of a Java and Bluetooth-enabled mobile phone. The mobile tool features data monitoring and presentation. This approach allows data visualization by the patient or medical staff without a portable computer or specific monitoring hardware. We hope to contribute to the adoption of biofeedback for early detection of health abnormalities and lower the budget that governments spend each year in healthcare

A Symbian-based mobile solution for intra-body temperature monitoring

Copyright © [2010] IEEE. Reprinted from 12th IEEE International Conference on e-Health Networking, Applications and Services . ISBN: 978-1-4244-6374-9. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.”Biofeedback data acquisition is an extremely important task in body sensor networks (BSNs). Data collected by sensors need to be processed in order to be shown in an easy and meaningful way for the user. The use of mobile devices may support and offer new user experiences. When connected to a BSN they can aggregate and process data collected by each sensor, providing a mobile solution for a healthcare system. This mobility offers a better patients' quality of life allowing a regular daily routine and always under monitoring. This paper proposes a Symbian-based mobile solution for intra-body temperature monitoring. Mobile device connects wirelessly to an intra-vaginal temperature sensor and interacts with sensor for temperature data collection and monitoring. This system helps women to detect their fertile and ovulation periods by the increasing of their intra-vaginal temperature. The mobile system was tested and validated with success and it is available for regular use

Brownie: A Platform for Conducting NeuroIS Experiments

In the NeuroIS field, experimental software needs to simultaneously present experimental stimuli to participants while recording, analyzing, or displaying neurophysiological measures. For example, a researcher might record a user’s heart beat (neurophysiological measure) as the user interacts with an e-commerce website (stimulus) to track changes in user arousal or show a user’s changing arousal levels during an exciting game. In this paper, we identify requirements for a NeuroIS experimental platform that we call Brownie and present its architecture and functionality. We then evaluate Brownie via a literature review and a case study that demonstrates Brownie’s capability to meet the requirements in a complex research context. We also verify Brownie’s usability via a quantitative study with prospective experimenters who implemented a test experiment in Brownie and an alternative software. We summarize the salient features of Brownie as follows: 1) it integrates neurophysiological measurements, 2) it incorporates real-time processing of neurophysiological data, 3) it facilitates research on individual and group behavior in the lab, 4) it offers a large variety of options for presenting experimental stimuli, and 5) it is open source and easily extensible with open source libraries. In summary, we conclude that Brownie is innovative in its potential to reduce barriers for IS researchers by fostering replicability and research collaboration and to support NeuroIS and interdisciplinary research in cognate areas, such as management, economics, or human-computer interaction

Mobile platform-independent solutions for body sensor network interface

Body Sensor Networks (BSN) appeared as an application of Wireless Sensor Network (WSN) to medicine and biofeedback. Such networks feature smart sensors (biosensors) that capture bio-physiological parameters from people and can offer an easy way for data collection. A new BSN platform called Sensing Health with Intelligence Modularity, Mobility and Experimental Reusability (SHIMMER) presents an excellent opportunity to put the concept into practice, with suitable size and weight, while also supporting wireless communication via Bluetooth and IEEE 802.15.4 standards. BSNs also need suitable interfaces for data processing, presentation, and storage for latter retrieval, as a result one can use Bluetooth technology to communicate with several more powerful and Graphical User Interface (GUI)-enabled devices such as mobile phones or regular computers. Taking into account that people currently use mobile and smart phones, it offers a good opportunity to propose a suitable mobile system for BSN SHIMMER-based networks. This dissertation proposes a mobile system solution with different versions created to the four major smart phone platforms: Symbian, Windows Mobile, iPhone, and Android. Taking into account that, currently, iPhone does not support Java, and Java cannot match a native solution in terms of performance in other platforms such as Android or Symbian, a native approach with similar functionality must be followed. Then, four mobile applications were created, evaluated and validated, and they are ready for use

An ambient assisted living solution for mobile environments

An Ambient Assisted Living (AAL) mobile health application solution with biofeedback based on body sensors is very useful to perform a data collection for diagnosis in patients whose clinical conditions are not favourable. This system allows comfort, mobility, and efficiency in all the process of data collection providing more confidence and operability. A physical fall may be considered something natural in the life span of a human being from birth to death. In a perfect scenario it would be possible to predict when a fall will occur in order to avoid it. Falls represent a high risk for senior people health. Those falls can cause fractures or injuries causing great dependence and debilitation to the elderly and even death in extreme cases. Falls can be detected by the accelerometer included in most of the available mobile phones or portable digital assistants (PDAs). To reverse this tendency, it can be obtained more accurate data for patients monitoring from the body sensors attached to the human body (such as, electrocardiogram (ECG), electromyography (EMG), blood volume pulse (BVP), electro dermal activity (EDA), and galvanic skin response (GSR)). Then, this dissertation reviews the related literature on this topic and introduces a mobile solution for falls prevention, detection, and biofeedback monitoring. The proposed system collects sensed data that is sent to a smartphone or tablet through Bluetooth. Mobile devices are used to process and display information graphically to users. The falls prevention system uses collected data from sensors in order to control and advice the patient or even to give instructions to treat an abnormal condition to reduce the falls risk. In cases of symptoms that last more time it can even detect a possible disease. The signal processing algorithms plays a key role in the fall prevention system. These algorithms in real time, through the capture of biofeedback data, are needed to extract relevant information from the signals detected to warn the patient. Monitoring and processing data from sensors is realized by a smartphone or tablet that will send warnings to users. All the process is performed in real time. These mobile devices are also used as a gateway to send the collected data to a Web service, which subsequently allows data storage and consultation. The proposed system is evaluated, demonstrated, and validated through a prototype and it is ready for use

Internet protocol over wireless sensor networks, from myth to reality

Internet Protocol (IP) is a standard network layer protocol of the Internet architecture, allowing communication among heterogeneous networks. For a given network to be accessible from the Internet it must have a router that complies with this protocol. Wireless sensor networks have many smart sensing nodes with computational, communication and sensing capabilities. Such smart sensors cooperate to gather relevant data and present it to the user. The connection of sensor networks and the Internet has been realized using gateway or proxy- based approaches. Historically, several routing protocols were specifically created, discarding IP. However, recent research, prototypes and even implementation tools show that it is possible to combine the advantages of IP access with sensor networks challenges, with a major contribution from the 6LoWPAN Working Group. This paper presents the advantages and challenges of IP on sensor networks, surveys the state-of-art with some implementation examples, and points further research topics in this area

Acquisition, analysis and visualization of data from physiological sensors for biofeedback applications

With the latest advances in technology and the rise of physiological sensors for everyday life, biofeedback is celebrating its revival and is a topic of great interest. The aim of this thesis is a mash-up of biofeedback techniques, modern physiological sensors and 3D technology. It investigates how to create a flexible and reusable biofeedback framework that can be used as extendable platform for future physiological sensors and research projects. It results in a fully operational biofeedback system that can be used to improve body awareness and control. The thesis explains what biofeedback is, investigates physiological sensor modalities and recording techniques, and provides a comprehensive analysis of related work in this domain. Simultaneous acquisition of data from multiple physiological sensors introduces new data management challenges on how to access stored data in an efficient way while still having enough processing power available for data visualization. Rather than just mapping a single value from a sensor like in traditional biofeedback systems, the thesis explains how to create an interactive classification graph, where customizable classifiers combine results from signal processing and map them to one or multiple feedback scores. The thesis extends the traditional biofeedback loop by a control and adjust mechanism and encapsulates analysis and classification from visualization. The two tier architecture allows the creation of state-of-the-art visualizations with any rendering engine. Several sample visualizations are created, including a virtual reality scene using the Oculus Rift in order to investigate the impact of virtual reality in biofeedback. An evaluation with 8 participants, each doing 7 tests, showed that key for successful biofeedback are (1) interaction with a human feedback controller who monitors the session, (2) interaction with a fast responding and simple visualization, and (3) customization of classification. The thesis provides guidelines on how to design useful biofeedback visualizations along with an investigation of the operational capability of physiological sensors and the effect of virtual reality. As a result of this research, a biofeedback framework with a visual and interactive graph-based classification system was created that enables feedback controllers to easily change the classification process and customize it for their users.Aufgrund neuester technologischer Fortschritte und der steigenden Verfügbarkeit von physiologischen Sensoren für das alltägliche Leben, wird das Thema Biofeedback wieder aktuell und mit großem Interesse verfolgt. Ziel dieser Diplomarbeit ist die nahtlose Verbindung aus Biofeedback-Techniken, modernen physiologischen Sensoren und 3D Technologien. Ein flexibles und wiederverwendbares System wird erstellt, das als erweiterbare Plattform für zukünftige Sensoren und Forschungsprojekte verwendet werden kann. Das Resultat ist eine funktionsfähige Biofeedback-Software, welche die eigene Körperwahrnehmung und Körperkontrolle verbessern kann. Ferner erklärt diese Arbeit was Biofeedback ist, untersucht Modalitäten und Aufnahmetechniken von physiologischen Sensoren und stellt eine umfassende Recherche und Analyse von verwandten Arbeiten und Projekten bereit. Die zeitgleiche Datenerfassung mehrerer physiologischer Sensoren erfordert eine effiziente Speichernutzung um der Daten-Visualisierung genügend Rechenleistung zur Verfügung stellen zu können. Im Gegensatz zu traditionellen Biofeedback-Systemen, welche leiglich einen Wert von einem Sensor abbilden, erklärt diese Arbeit, wie ein interaktiver Klassifizierungs-Graph verwendet werden kann, um anpassbare Klassifikatoren zu erstellen und die Ergebnisse von der Signalverarbeitung auf einen oder mehrere Feedback-Werte abzubilden. Die Arbeit erweitert die traditionelle Biofeedback-Schleife um einen Kontroll- und Veränderungsmechanismus und trennt die Analyse und Klassifizierung von der Visualisierung. Die Zwei-Schichten Architektur ermöglicht es state-of-the-art Visualisierungen mit beliebigen Render-Engines zu erstellen. Mehrere Beispiel-Visualisierungen werden entwickelt, inklusive einer Virtual Reality Szene, welche ein Oculus Rift verwendet, um die Auswirkungen von Virtual Reality auf Biofeedback zu untersuchen. Die Evaluation, bei der 8 Probanden jeweils 7 Testszenarien durchliefen, zeigt, dass mehrere Faktoren für erfolgreiches Biofeedback entscheidend sind. Dazu gehören (1) die Interaktion mit einem menschlichen Feedback-Controller, (2) die Interaktion mit einer schnell reagierenden und simplen Visualisierung sowie (3) die Anpassung der Klassifikatoren. Die Diplomarbeit liefert einen Leitfaden für die Gestaltung von Biofeedback-Visualisierungen, über den Effekt von Virtual Reality und eine Untersuchung der Funktionsfähigkeit von physiologischen Sensoren. Das Ergebnis dieser Arbeit ist ein Biofeedback-System mit einer visuellen und interaktiven Graph-basierten Klassifikation, welches es Feedback-Controller erlaubt den Klassifizierungsprozess an den jeweiligen Benutzer anzupassen

A biosensor and data presentation solution for body sensor networks

A Body Sensor Network can sense health parameters directly on the patient’s body, allowing 24/7 monitoring in an unobtrusive way. Several tiny sensors collect and route data to a special sink node. A new intra-vaginal biosensor was developed to study the relation between temperature variations and women health conditions, such as ovulation period, among others. We present a biosensor prototype and some initial results on real scenarios with a woman. One of the main issues in a body sensor network is the transformation of the sensor raw data into meaningful medical data for medical staff. Several approaches exist, from mobile device-based approaches to more powerful hardware such as a personal computer. This paper presents our current work in body sensor networks, namely a prototype for intra-vaginal temperature monitoring with initial results, and a mobile tool for data presentation of a three-tier body sensor network. The gathered results demonstrate the feasibility of the approach, contributing to the widespread application of body sensor networks

Emotions and cognitive workload in economic decision processes - A NeuroIS Approach

The influence of cognitive and emotions on decision processes have been recently highlighted. Emotions interplay with the process of cognition, and determine decision processes. In this work, the role of external and internal influences on economic decision processes are studied. A NeuroIS method is applied for measuring emotions and cognitive workload. The lack of a suitable experimental platform for performing NeuroIS studies was recognized and the platform Brownie was developed and evaluated