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Application and validation of capacitive proximity sensing systems in smart environments

Braun, Andreas (2014)
Application and validation of capacitive proximity sensing systems in smart environments.
Technische Universität Darmstadt
Dissertation, Erstveröffentlichung

Kurzbeschreibung (Abstract)

Smart environments feature a number of computing and sensing devices that support occupants in performing their tasks. In the last decades there has been a multitude of advances in miniaturizing sensors and computers, while greatly increasing their performance. As a result new devices are introduced into our daily lives that have a plethora of functions. Gathering information about the occupants is fundamental in adapting the smart environment according to preference and situation. There is a large number of different sensing devices available that can provide information about the user. They include cameras, accelerometers, GPS, acoustic systems, or capacitive sensors. The latter use the properties of an electric field to sense presence and properties of conductive objects within range. They are commonly employed in finger-controlled touch screens that are present in billions of devices. A less common variety is the capacitive proximity sensor. It can detect the presence of the human body over a distance, providing interesting applications in smart environments. Choosing the right sensor technology is an important decision in designing a smart environment application. Apart from looking at previous use cases, this process can be supported by providing more formal methods. In this work I present a benchmarking model that is designed to support this decision process for applications in smart environments. Previous benchmarks for pervasive systems have been adapted towards sensors systems and include metrics that are specific for smart environments. Based on distinct sensor characteristics, different ratings are used as weighting factors in calculating a benchmarking score. The method is verified using popularity matching in two scientific databases. Additionally, there are extensions to cope with central tendency bias and normalization with regards to average feature rating. Four relevant application areas are identified by applying this benchmark to applications in smart environments and capacitive proximity sensors. They are indoor localization, smart appliances, physiological sensing and gesture interaction. Any application area has a set of challenges regarding the required sensor technology, layout of the systems, and processing that can be tackled using various new or improved methods. I will present a collection of existing and novel methods that support processing data generated by capacitive proximity sensors. These are in the areas of sparsely distributed sensors, model-driven fitting methods, heterogeneous sensor systems, image-based processing and physiological signal processing. To evaluate the feasibility of these methods, several prototypes have been created and tested for performance and usability. Six of them are presented in detail. Based on these evaluations and the knowledge generated in the design process, I am able to classify capacitive proximity sensing in smart environments. This classification consists of a comparison to other popular sensing technologies in smart environments, the major benefits of capacitive proximity sensors, and their limitations. In order to support parties interested in developing smart environment applications using capacitive proximity sensors, I present a set of guidelines that support the decision process from technology selection to choice of processing methods.

Typ des Eintrags: Dissertation
Erschienen: 2014
Autor(en): Braun, Andreas
Art des Eintrags: Erstveröffentlichung
Titel: Application and validation of capacitive proximity sensing systems in smart environments
Sprache: Englisch
Referenten: Fellner, Prof. Dieter W. ; Mühlhäuser, Prof. Max
Publikationsjahr: 2 Oktober 2014
Ort: Darmstadt
Datum der mündlichen Prüfung: 18 September 2014
URL / URN: http://tuprints.ulb.tu-darmstadt.de/4175
Kurzbeschreibung (Abstract):

Smart environments feature a number of computing and sensing devices that support occupants in performing their tasks. In the last decades there has been a multitude of advances in miniaturizing sensors and computers, while greatly increasing their performance. As a result new devices are introduced into our daily lives that have a plethora of functions. Gathering information about the occupants is fundamental in adapting the smart environment according to preference and situation. There is a large number of different sensing devices available that can provide information about the user. They include cameras, accelerometers, GPS, acoustic systems, or capacitive sensors. The latter use the properties of an electric field to sense presence and properties of conductive objects within range. They are commonly employed in finger-controlled touch screens that are present in billions of devices. A less common variety is the capacitive proximity sensor. It can detect the presence of the human body over a distance, providing interesting applications in smart environments. Choosing the right sensor technology is an important decision in designing a smart environment application. Apart from looking at previous use cases, this process can be supported by providing more formal methods. In this work I present a benchmarking model that is designed to support this decision process for applications in smart environments. Previous benchmarks for pervasive systems have been adapted towards sensors systems and include metrics that are specific for smart environments. Based on distinct sensor characteristics, different ratings are used as weighting factors in calculating a benchmarking score. The method is verified using popularity matching in two scientific databases. Additionally, there are extensions to cope with central tendency bias and normalization with regards to average feature rating. Four relevant application areas are identified by applying this benchmark to applications in smart environments and capacitive proximity sensors. They are indoor localization, smart appliances, physiological sensing and gesture interaction. Any application area has a set of challenges regarding the required sensor technology, layout of the systems, and processing that can be tackled using various new or improved methods. I will present a collection of existing and novel methods that support processing data generated by capacitive proximity sensors. These are in the areas of sparsely distributed sensors, model-driven fitting methods, heterogeneous sensor systems, image-based processing and physiological signal processing. To evaluate the feasibility of these methods, several prototypes have been created and tested for performance and usability. Six of them are presented in detail. Based on these evaluations and the knowledge generated in the design process, I am able to classify capacitive proximity sensing in smart environments. This classification consists of a comparison to other popular sensing technologies in smart environments, the major benefits of capacitive proximity sensors, and their limitations. In order to support parties interested in developing smart environment applications using capacitive proximity sensors, I present a set of guidelines that support the decision process from technology selection to choice of processing methods.

Alternatives oder übersetztes Abstract:
Alternatives AbstractSprache

Das Forschungsgebiet Smart Environments beschreibt Umgebungen die über eine Vielzahl von Sensoren und Computern erweitert werden. Diese unterstützen Personen, welche in dieser Umgebung agieren, in der Erfüllung verschiedener Aufgaben. Technische Entwicklungen der vergangenen Jahrzehnte führten zu einer zunehmenden Miniaturisierung von Sensoren und Computern, während die Rechenleistung beziehungsweise erreichbare Auflösung stark anstieg. In Folge nutzen wir zunehmend neue technische Geräte, welche über eine hohe Zahl von Funktionen verfügen. Eine der wichtigsten Aufgaben von Smart Environments ist die Sammlung von Informationen über Situation und Präferenz der Nutzer, welche dazu genutzt werden können die Umgebung anzupassen. Es existieren diverse Sensortechnologien, die es ermöglichen derartige Information zu gewinnen. Beispiele hierfür sind Kameras, Beschleunigungssensoren, GPS, akustische Systeme oder auch kapazitive Sensoren. Die letztgenannten messen die Eigenschaften eines elektrischen Feldes um Präsenz und Eigenschaften von leitfähigen Objekten zu bestimmen, welche in dieses Feld eintreten. Diese Technologie ist die Basis für fingerkontrollierte Touchscreens, die bereits in mehreren Milliarden Geräten verbaut wurden. Eine weniger bekannte Variante sind kapazitive Abstandssensoren. Diese sind in der Lage die Präsenz von menschlichen Körperteilen über eine gewisse Distanz zu erkennen. Dies ermöglicht interessante Anwendungen in Smart Environments. Bei der Erstellung von Anwendungen in dieser Domäne ist die Wahl passender Sensoren eine der wichtigsten Entscheidungen. Dies wird bislang primär durch eine Analyse bestehender Lösungen realisiert, welche eine gewisse Ähnlichkeit zum gewählten Ansatz haben. Ziel dieser Arbeit ist die Einordnung von kapazitiven Abstandssensoren im Bereich Smart Environments. Hierzu wird ein Benchmarking-Modell erstellt, das es ermöglicht über eine Auswahl von Sensoreigenschaften eine Eignung für bestimmte Anwendungsgebiete zu errechnen. Dies ermöglicht es verschiedene Nutzungsszenarien für kapazitive Abstandssensoren in Smart Environments zu finden, bzw. zu verifizieren. Indem eine Zahl von Prototypen für diese Szenarien entwickelt wird, ist es mir zum einen möglich neue Datenverarbeitungsmethoden zu realisieren, und zum anderen die erwähnte Klassifikation vorzunehmen. Diese besteht aus einem Vergleich von kapazitiven Abstandssensoren mit anderen populären Sensortechnologien, einer Diskussion der spezifischen Vor- und Nachteile, sowie einer Sammlung von Richtlinien, welche Entwickler dabei unterstützen, Anwendungen für Smart Environments zu realisieren.

Deutsch
URN: urn:nbn:de:tuda-tuprints-41756
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 12 Okt 2014 19:55
Letzte Änderung: 12 Okt 2014 19:55
PPN:
Referenten: Fellner, Prof. Dieter W. ; Mühlhäuser, Prof. Max
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: 18 September 2014
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