Application and validation of capacitive proximity sensing systems in smart environments

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

Evaluation Strategies for HCI Toolkit Research

Toolkit research plays an important role in the field of HCI, as it can heavily influence both the design and implementation of interactive systems. For publication, the HCI community typically expects toolkit research to include an evaluation component. The problem is that toolkit evaluation is challenging, as it is often unclear what ‘evaluating’ a toolkit means and what methods are appropriate. To address this problem, we analyzed 68 published toolkit papers. From our analysis, we provide an overview of, reflection on, and discussion of evaluation methods for toolkit contributions. We identify and discuss the value of four toolkit evaluation strategies, including the associated techniques that each employs. We offer a categorization of evaluation strategies for toolkit researchers, along with a discussion of the value, potential limitations, and trade-offs associated with each strategy

Physical sketching tools and techniques for customized sensate surfaces

Sensate surfaces are a promising avenue for enhancing human interaction with digital systems due to their inherent intuitiveness and natural user interface. Recent technological advancements have enabled sensate surfaces to surpass the constraints of conventional touchscreens by integrating them into everyday objects, creating interactive interfaces that can detect various inputs such as touch, pressure, and gestures. This allows for more natural and intuitive control of digital systems. However, prototyping interactive surfaces that are customized to users' requirements using conventional techniques remains technically challenging due to limitations in accommodating complex geometric shapes and varying sizes. Furthermore, it is crucial to consider the context in which customized surfaces are utilized, as relocating them to fabrication labs may lead to the loss of their original design context. Additionally, prototyping high-resolution sensate surfaces presents challenges due to the complex signal processing requirements involved. This thesis investigates the design and fabrication of customized sensate surfaces that meet the diverse requirements of different users and contexts. The research aims to develop novel tools and techniques that overcome the technical limitations of current methods and enable the creation of sensate surfaces that enhance human interaction with digital systems.Sensorische Oberflächen sind aufgrund ihrer inhärenten Intuitivität und natürlichen Benutzeroberfläche ein vielversprechender Ansatz, um die menschliche Interaktionmit digitalen Systemen zu verbessern. Die jüngsten technologischen Fortschritte haben es ermöglicht, dass sensorische Oberflächen die Beschränkungen herkömmlicher Touchscreens überwinden, indem sie in Alltagsgegenstände integriert werden und interaktive Schnittstellen schaffen, die diverse Eingaben wie Berührung, Druck, oder Gesten erkennen können. Dies ermöglicht eine natürlichere und intuitivere Steuerung von digitalen Systemen. Das Prototyping interaktiver Oberflächen, die mit herkömmlichen Techniken an die Bedürfnisse der Nutzer angepasst werden, bleibt jedoch eine technische Herausforderung, da komplexe geometrische Formen und variierende Größen nur begrenzt berücksichtigt werden können. Darüber hinaus ist es von entscheidender Bedeutung, den Kontext, in dem diese individuell angepassten Oberflächen verwendet werden, zu berücksichtigen, da eine Verlagerung in Fabrikations-Laboratorien zum Verlust ihres ursprünglichen Designkontextes führen kann. Zudem stellt das Prototyping hochauflösender sensorischer Oberflächen aufgrund der komplexen Anforderungen an die Signalverarbeitung eine Herausforderung dar. Diese Arbeit erforscht dasDesign und die Fabrikation individuell angepasster sensorischer Oberflächen, die den diversen Anforderungen unterschiedlicher Nutzer und Kontexte gerecht werden. Die Forschung zielt darauf ab, neuartigeWerkzeuge und Techniken zu entwickeln, die die technischen Beschränkungen derzeitigerMethoden überwinden und die Erstellung von sensorischen Oberflächen ermöglichen, die die menschliche Interaktion mit digitalen Systemen verbessern

Hand gesture recognition through capacitive sensing : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Engineering at Massey University, School of Food and Advanced Technology (SF&AT), Auckland, New Zealand

Figures 1.1, 1.2, 1.3, 2.1, 2.3 & 2.4 are re-used with permission. Figure 2.2 (=Smith, 1996 Fig 1) ©1996 by International Business Machines Corporation was removed.This thesis investigated capacitive sensing-based hand gesture recognition by developing and validating through custom built hardware. We attempted to discover if massed arrays of capacitance sensors can produce a robust system capable of simple hand gesture detection and recognition. The first stage of this research was to build the hardware that performed capacitance sensing. This hardware needs to be sensitive enough to capture minor variations in capacitance values, while also reducing stray capacitance to their minimum. The hardware designed in this stage formed the basis of all the data captured and utilised for subsequent training and testing of machine learning based classifiers. The second stage of this system used mass arrays of capacitance sensor pads to capture frames of hand gestures in the form of low-resolution 2D images. The raw data was then processed to account for random variations and noise present naturally in the surrounding environment. Five different gestures were captured from several test participants and used to train, validate and test the classifiers. Different methods were explored in the recognition and classification stage: initially, simple probabilistic classifiers were used; afterwards, neural networks were used. Two types of neural networks are explored, namely Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN), which are capable of achieving upwards of 92.34 % classification accuracy

Machine Learning Techniques for Device-Free Indoor Person Tracking

L'abstract è presente nell'allegato / the abstract is in the attachmen

Prototyping capacitive sensing applications with OpenCapSense

OpenCapSense is a prototyping platform to develop innovative applications that rely on perceiving humans with electric fields. Despite today's use of capacitive sensing mostly as a method to detect touch, it offers many interesting facets that range from mid-air interaction to contactless indoor localization and identification. The platform provides active sensors to detect human interactions at distances of more than 40 cm, by generating electric fields. Passive sensors allow for measuring changes in electric fields that occur naturally in the environment, enabling detection distances up to 2 m