| dc.description.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. | en_US |
