Designing Self-Aware Textiles
Darmstadt, TU, Master Thesis, 2018
We are surrounded by textiles in our everyday live. Making them capable of local monitoring and computing is already a growing field of research in the area of Smart Home and Ambient Assisted Living. Equipping usual furniture with sensors and simple computational elements can provide useful information about the user and help with identifying emergency events, for instance fall recognition. This thesis investigates an approach to apply those ideas to textile materials worn by users by embedding inertial measurement unit sensors in a non-intrusive manner. In our approach, a simulation framework is used to ensure the highest possible accuracy while keeping the amount of sensors needed as low as possible. For this, a simulated sensor grid across the whole jacket was evaluated. Later, a prototype which uses the deformation of the jacket to provide valuable information about the current state of the jacket will be introduced. The presented use case to help find the jacket is just one idea on how to use the information gained by the sensor network.
The Emotive Couch - Learning Emotions by Capacitively Sensed Movements
Procedia Computer Science [online]
International Conference on Ambient Systems, Networks and Technologies (ANT) <9, 2018, Porto, Portugal>
Affective computing allows machines to simulate and detect emotional states. The most common method is the observation of the face by camera. However, in our increasingly observed society, more privacy-aware methods are worth exploring that do not require facial images, but instead look at other physiological indicators of emotion. In this work we present the Emotive Couch, a sensor-augmented piece of smart furniture that detects proximity and motion of the human body. We present the design rationale and use standard machine learning techniques to detect the three basic emotions Anxiety, Interest, and Relaxation. We evaluate the performance of our approach with 15 participants in a study that includes various affect elicitation methods, achieving an accuracy of 77.7 %.
Assistive Apps for Activities of Daily Living Supporting Persons with Down's Syndrome
Journal of Ambient Intelligence and Smart Environments
Supporting persons with Down's Syndrome in their daily activities using ICT is a key element in further advancing their independence and integration into society. The POSEIDON project embraces this goals and develops technology which creates adjustable and personalizable assistive systems. We present a system for Money-Handling Training and assistance for shopping. In this paper we present results of evaluating the Money-Handling Training App in different pilot studies and work-shops, with a larger group of persons with Down's Syndrome, comparing different interaction devices like tablet, personal computer and interactive table. Furthermore, we present evaluation results for the Shopping App.
E-Textile Couch: Towards Smart Garments Integrated Furniture
European Conference on Ambient Intelligence (AmI) <13, 2017, Malaga, Spain>
Application areas like health-care and smart environments have greatly benefited from embedding sensors into every-day-objects, enabling for example sleep apnea detection. We propose to further integrate parts of sensors into the very own materials of the objects. Thus, in this work we explore integrating smart garments into furniture using a couch as our use-case. Equipped with textile capacitive sensing electrodes, we show that our prototype outperforms existing systems achieving an F-measure of 94.1%. Furthermore, we discuss implications and limitation of the integration process.
Emotion Detection By Evaluating Activities For Smart Home Appliances
Darmstadt, TU, Master Thesis, 2017
Human computer interactions can be made easier if we make computers understand person's emotions. Over the years, research in emotion recognition has mainly focused on facial expressions, voice analysis and hand-writing. Apart from these conventional methods, body movements, body postures and gestures or quality of movements can also be used to differentiate basic or fundamental emotions like happiness, anger, fear, sadness, surprise etc. For instance in case of fear, body of a person contracts, muscles tighten while as in case of happiness, muscles are more relaxed and body tends to occupy more area. Recognizing emotions of a person solely on his movements will enable efficient communication between human and machine. This master thesis is based on this idea of the machine being able to recognize the emotions from postures and movements of a human. A couch as a smart furniture has been used for the prediction of postures which are further used to predict the fundamental emotions including anxiety, happiness, sadness, relaxation, being focused/interested by using capacitive proximity sensors integrated into the couch. Android application was developed to predict the real-time postures of a person using machine learning classification algorithms. A relation between postures and movements with emotions has been established. This relation was considered as a baseline for the prediction of emotions. For the recognition of mentioned emotions, the detected movements and postures were analyzed and evaluated using various classification algorithms in machine learning. Furthermore the comparison of these classification algorithms with respect to performance was done and the better accuracy classification algorithm was chosen. This thesis also discusses in depth various methods that have been used to evoke the emotions of a human being during evaluation experiments. After successful evoking and prediction of the emotions, the results can then be used in various smart home applications.
Evaluating the Recognition of Bed Postures Using Mutual Capacitance Sensing
Journal of Ambient Intelligence and Smart Environments
Capacitive sensing is increasingly used to gather contextual information about humans. They can be used to create stationary or mobile systems for non-contact activity recognition. They are able to sense any conductive objects at distances up to 50 cm. This paper investigates an approach to classify bed postures using mutual capacitance sensing. The goal is to develop a system that prevents decubitus ulcers, which is a condition caused by prolonged pressure on the skin that can result in injuries to the skin and underlying tissues. The posture recognition is used to detect prolonged lying in a single pose and can notify care personnel. A low-cost grid of crossed wires is proposed that is placed between the mattress and the bed sheet that creates 48 measurement points. The experiments analyze a set of five bedding positions with 14 users. Using self-defined features, we achieved an accuracy of 80.8% for all users and an accuracy of 93.8% for individuals of similar body size. Refining the classification approach by directly classifying the raw data an overall accuracy of 90.5% was reached. By introducing an uncertainty threshold the classification is correct in 97.6% of cases.
Invisible Human Sensing in Smart Living Environments Using Capacitive Sensors
Ambient Assisted Living
Ambient Assisted Living (AAL) <9, 2016, Frankfurt, Germany>
Smart living environments aim at supporting their inhabitants in daily tasks by detecting their needs and dynamically reacting accordingly. This generally requires several sensor devices, whose acquired data is combined to assess the current situation. Capturing the full range of situations necessitates many sensors. Often cameras and motion detectors are used, which are rather large and difficult to hide in the environment. Capacitive sensors measure changes in the electric field and can be operated through any non-conductive material. They gained popularity in research in the last few years, with some systems becoming available on the market. In this work we will introduce how those sensors can be used to sense humans in smart living environments, providing applications in situation recognition and human-computer interaction. We will discuss opportunities and challenges of capacitive sensing and give an outlook on future scenarios.
Simulation and Validation of Capacitive Sensing on Flexible and Curved Surfaces Applied on Sleeping Breathing Rate Detection
Bremen, Hochschule, Master Thesis, 2017
In recent years the capacitive sensing is the main technology, act as input in smart devices. There will be limitations for the flat surface, and nowadays people wants to use flexible surface input devices such as foldable mobiles, flexible input devices, and curved input devices. For these input devices, the surface wants to be flexible and curved; limited research has been carried out till now on curved and flexible surface capacitive sensing technology. The main objective of this master thesis is to investigate the capacitive sensing for different materials, different flexibility, and curvature. Simulation and validation of capacitive sensing on flexible and curved surfaces for various materials are carried out. Moreover, designing an own application (Breathing rate detection while sleeping) based on the characteristics obtained by capacitive sensing for various material on the flexible and curved surface.
CapTap - Combining Capacitive Gesture Recognition and Acoustic Touch Detection
International Workshop on Sensor-based Activity Recognition (iWOAR) <3, 2016, Rostock, Germany>
Capacitive sensing is a common technology for finger-controlled touch screens. The variety of proximity sensors extends the range, thus supporting mid-air gesture interaction and application below any non-conductive materials. However, this comes at the cost of limited resolution for touch detection. In this paper, we present CapTap, which uses capacitive proximity and acoustic sensing to create an interactive surface that combines mid-air and touch gestures, while being invisibly integrated into living room furniture. We introduce capacitive imaging, investigating the use of computer vision methods to track hand and arm positions and present several use cases for CapTap. In a user study we found that the system has average localization errors of 1.5cm at touch distance and 5cm at an elevation of 20cm above the table. The users found the system intuitive and interesting to use.
Unsichtbare Erkennung menschlicher Aktivitäten in Smart Living Umgebungen mit Kapazitiven Sensoren
Zukunft Lebensräume 2016
Zukunft Lebensräume <2016, Frankfurt/Main, Germany>
Smart Living Umgebungen versuchen ihre Bewohner bei der Bewältigung alltäglicher Aufgaben zu unterstützen. Wünsche und Notwendigkeiten werden dynamisch erkannt und eine angemessene Reaktion erzeugt. Dies benötigt mehrere Sensoren, deren Daten intelligent kombiniert werden, um eine Vielzahl von Situationen zu erkennen. Häufig greift man hierbei auf Kameras und Bewegungsmelder zurück, die sich nur schwer unsichtbar in der Umgebung anbringen lassen. Kapazitive Sensoren messen Änderungen in elektrischen Feldern und können durch nicht-leitende Materialien hindurch Messungen vornehmen. In den letzten Jahren stieg ihre Popularität in Forschung und am Markt; insbesondere der fingerkontrollierte Touchscreen ist ein populäres Beispiel. In dieser Arbeit führen wir diese Art von Sensorik ein und stellen vor, inwiefern mit diesen menschliche Aktivitäten in Smart Living Umgebungen gemessen werden können. Wir stellen verschiedene Anwendungen in den Bereichen der Aktivitätserkennung und Mensch-Maschine-Interaktion vor, diskutieren Möglichkeiten und Herausforderungen der kapazitiven Sensorik und stellen zukünftige Forschungsrichtungen vor.
Assessing Real World Imagery in Virtual Environments for People with Cognitive Disabilities
The 11th International Conference on Intelligent Environments
International Conference on Intelligent Environments (IE) <11, 2015, Prague, Czech Republic>
People with cognitive disabilities are often socially excluded. We propose a system based on Virtual and Augmented Reality that has the potential to act as an educational and support tool in everyday tasks for people with cognitive disabilities. Our solution consists of two components: the first that enables users to train for several essential quotidian activities and the second that is meant to offer real time guidance feedback for immediate support. In order to illustrate the functionality of our proposed system, we chose to train and support navigation skills. Thus, we conducted a preliminary study on people with Down Syndrome (DS) based on a navigation task. Our experiment was aimed at evaluating the visual and spatial perception of people with DS when interacting with different elements of our system. We provide a preliminary evaluation that illustrates how people with DS perceive different landmarks and types of visual feedback, in static images and videos. Although we focused our study on people with DS, people with different cognitive disabilities could also benefit from the features of our solution. This analysis is mandatory in the design of a virtual intelligent system with several functionalities that aims at helping disabled people in developing basic knowledge in every day tasks.
Design Factors for Flexible Capacitive Sensors in Ambient Intelligence
European Conference on Ambient Intelligence (AmI) <12, 2015, Athens, Greece>
Capacitive sensors in both touch and proximity varieties are becoming more common in many industrial and research applications. Each sensor requires one or more electrodes to create an electric field and measure changes thereof. The design and layout of those electrodes is crucial when designing applications and systems. It can influence range, detectable objects, or refresh rate. In the last years, new measurement systems and materials, as well as advances in rapid prototyping technologies have vastly increased the potential range of applications using flexible capacitive sensors. This paper contributes an extensive set of capacitive sensing measurements with different electrode materials and layouts for two measurement modes - self-capacitance and mutual capacitance. The evaluation of the measurement results reveals how well-suited certain materials are for different applications. We evaluate the characteristics of those materials for capacitive sensing and enable application designers to choose the appropriate material for their application.
Physical Simulation- and Reconstruction-framework for Shape Sensing Fabrics
Darmstadt, TU, Master Thesis, 2015
Over the last decade a large number of prototypes for several research areas in the field of shape sensing have been based on optical tracking devices like the Microsoft Kinect. To overcome the disadvantages of such devices, namely immobility and occlusion of tracked objects, another approach, to which only little attention has been given to so far, is the usage of embedded sensors in fabrics. One of the reasons might be the high effort to manufacture such prototypes with uncertain outcome in terms of matching the requirements of certain use cases. To help developing and planning such devices as well as the used software, a simulation- and reconstruction-framework is introduced in this thesis. Furthermore both parts together enable creating software for a use case even before the hardware is ready. An exemplary workflow, demonstrating how the implemented software can support the development of new applications for shape sensing fabrics, is presented with the Sleeping Posture Recognizer. It uses a blanket with embedded acceleration sensors to determine the sleeping posture of the covered person.
Klassifizierung von Liegepositionen mittels kapazitiver Näherungssensorik
Darmstadt, TU, Bachelor Thesis, 2014
In den vergangenen Jahren hat die Verwendung von kapazitiver Sensorik in kleinen elektronischen Endgeräten stark zugenommen, zudem spielt sie auch bei Ubiquitous Computing immer mehr eine bedeutende Rolle. Im Bezug auf die Reichweite der Sensorik ist es mittlerweile möglich, auf Objekte in einer Entfernung von bis zu 50 cm zu reagieren. Diese Bachelorarbeit behandelt die Klassifizierung von Liegepositionen mittels kapazitiver Sensorik. Das Hauptaugenmerk liegt dabei auf Anwendungsfällen aus der Medizin und der Analyse, wie zuverlässig ein solches System die Liegepositionen von Patienten erkennt. Zum Erlangen der Rohdaten befindet sich auf der Matratze Bettlaken mit einem groben Gitter aus Elektroden, wodurch an jedem Schnittpunkt des Gitters ein kapazitiver Wert gemessen werden kann. Die Rohdaten aus diesem Versuchsaufbau werden zur Klassifizierung der Liegeposition verwendet.
Recognition of Bed Postures Using Mutual Capacitance Sensing
European Conference on Ambient Intelligence (AmI) <11, 2014, Eindhoven, The Netherlands>
In recent years, mutual capacitive sensing made significant advances in the field of gathering implicit contextual data. These systems find broad usage in pervasive activity-recognition systems, installed stationary or made portable. In the domain of context recognition new ways of interaction with the environment opened up since conductive objects can be detected under certain conditions at distances up to 50 cm. This paper investigates an approach to recognize bed postures using mutual capacitance sensing. The overall goal is to develop a technological concept that can be applied to recognize bed postures of patients in elderly homes. The use of this contextual data may lead to many desired benefits in elderly care e.g. the better prevention of decubitus ulcer, a condition caused by prolonged pressure on the skin resulting in injuries to skin and underlying tissues. For this, we propose a low-cost grid of crossed wires of 48 measurement points placed between the mattress and the bed sheet. The experimental results analyze a set of five lying positions. We achieved for all tested individuals an accuracy of 80.76% and for several individuals of the same bodysize an accuracy of 93.8%.
Recognition of Lying Postures Using Capacitive Proximity Sensing
Darmstadt, TU, Master Thesis, 2013
In recent years, capacitive proximity sensing made significant advances in the field of gathering implicit contextual data. These systems find broad usage in pervasive activity-recognition systems, installed stationary or made portable. In the domain of context recognition new ways of interaction with the environment opened up since conductive objects can be detected under certain conditions at distances up to 50 cm. This master's thesis investigates an approach to recognize lying positions using capacitive proximity sensing. The overall goal is to develop a technological concept that can be applied to recognize lying postures of patients in elderly homes. Using this contextual data may lead to many desired benefits in elderly care. For example, the occurrence of Decubitus, a condition caused by prolonged pressure on the skin resulting in injuries to skin and underlying tissues, can be avoided better by knowing how the patient is bedded. In order to achieve a desired high resolution, the multiple access schemes CDMA and FDMA are investigated as a basis for capacitive proximity measurements. For this purpose CDMA has been implemented using Gold Codes while the correlation serves the purpose of a measure. In a test setup measurements for CDMA and FDMA have been conducted. They lead to the conclusion that at close distances FDMA has a slightly better spatial resolution than CDMA but as the distance increases the spatial resolution of FDMA will be twice as high as the one of CDMA. The real-world setup achieves a resolution of 48 measurement points, by applying a low-cost grid of crossed wires under the slatted frame of a bed. The experimental results show that the presented approach and the decisions derived from the evaluation of multiple access schemes lead to a well-suitable system for recognizing lying postures.