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DI Michael Haslgrübler

DI Michael Haslgrübler

Researcher

DI Michael Haslgrübler absolviert an der Johannes Kepler Universität zu Linz das Bachalorstudium der Informatik und das Masterstudium Pervasive Computing.

Des weiteren hat er mehrjährige Erfahrung in der Softwareentwicklung in der Immobilien- sowie auch der Automobilbranche.

Der fachliche Schwerpunkt liegt im Bereich Activity Recognition, Embedded System mit Linux und Sensor-Netzwerke.

 

Aktuelle Projekte:

360Light, Bridge Projekt, FFG

Entwicklung einer intuitiven Gesteninteraktion für panoramische Inhalte in Multi-Display Installationen

http://pervasive.researchstudio.at/360-light

 

Publikationen:

DarSens: A Framework for Distributed Activity Recognition from Body-Worn Sensors

Proceedings of the Fifth International Conference on Body Area Networks (BodyNets'10), Corfu Island, Greece, September 2010.

Abstract: With the increasing amount of sensors in our environment, the desire to reuse existing sensors for different applications grows.However, most appliances do not provide access to their sensors in a cross-application manner, but rather use them for a specific purpose only. In this paper, we describe a framework which provides a way to access not only the data, but also the processing capabilities of a sensor system in a reusable way, without the need for a-priori knowledge about the availability of sensors in the environment. In particular, the presented framework is able to run on an embedded system platform, and is used for the recognition of human activities in a body sensor network. Notably, both the feature extraction and classification are performed within the network. Hence, we can use the processing power of sensor nodes, and do not have to revert to the processing capabilities of a client device which is using the sensor network. Two experiments have been conducted to show the feasibility and performance of our approach in typical activity recognition scenarios.

C. Holzmann, M. Haslgrübler

A Self-Organizing Approach to Activity Recognition with Wireless Sensors Proceedings of the 4th International Workshop on Self-Organizing Systems

(IWSOS 2009), Springer LNCS, ETH Zurich, Switzerland, December 2009.

Abstract: In this paper, we describe an approach to activity recognition, which is based on a self-organizing, ad hoc network of body-worn sensors. It makes best use of the available sensors, and autonomously adapts to dynamically varying sensor setups in terms of changing sensor availabilities, characteristics and on-body locations.

For a widespread use of activity recognition systems, such an opportunistic approach is better suited than a fixed and application-specific deployment of sensor systems, as it unburdens the user from placing specific sensors at pre-defined locations on his body.

The main contribution of this paper is the presentation of an interaction model for the self-organization of sensor nodes, which enables a cooperative recognition of activities according to the demands of a user's mobile device. We implemented it with an embedded system platform, and conducted an evaluation showing the feasibility and performance of our approach.

Projekte (in Kooperation mit RSA):

Linux Kernel Display Driver

Es wurde ein Framebuffer Treiben für den Linux Kernel entwickelt, für das eyescreenTM ME3204 P-OLED Microdisplay von MicroEmissive Displays, als Hardwareplatform diente ein Blackfin CM-527

Activity Tracking with Zigbee

Basierend auf der IEEE 802.15.4 ZigBee compliant MeshNetics MeahBean Platform und der dazugehörigen MAC-Layer Implementierung in TinyOS und nesC, wurde ein Software Prototyp implementiert, der die Daten eines 3-Achsen Beschleunigungssensors auswertet und Aufschluß über die aktuelle Aktivität des Benutzers liefert.

 

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Telefon: +43(0) 662 834 602 - 0