16 research outputs found
Capacitive Sensing and Communication for Ubiquitous Interaction and Environmental Perception
During the last decade, the functionalities of electronic devices within a living environment constantly increased. Besides the personal computer, now tablet PCs, smart household appliances, and smartwatches enriched the technology landscape. The trend towards an ever-growing number of computing systems has resulted in many highly heterogeneous human-machine interfaces. Users are forced to adapt to technology instead of having the technology adapt to them. Gathering context information about the user is a key factor for improving the interaction experience. Emerging wearable devices show the benefits of sophisticated sensors which make interaction more efficient, natural, and enjoyable. However, many technologies still lack of these desirable properties, motivating me to work towards new ways of sensing a user's actions and thus enriching the context. In my dissertation I follow a human-centric approach which ranges from sensing hand movements to recognizing whole-body interactions with objects.
This goal can be approached with a vast variety of novel and existing sensing approaches. I focused on perceiving the environment with quasi-electrostatic fields by making use of capacitive coupling between devices and objects. Following this approach, it is possible to implement interfaces that are able to recognize gestures, body movements and manipulations of the environment at typical distances up to 50cm. These sensors usually have a limited resolution and can be sensitive to other conductive objects or electrical devices that affect electric fields. The technique allows for designing very energy-efficient and high-speed sensors that can be deployed unobtrusively underneath any kind of non-conductive surface. Compared to other sensing techniques, exploiting capacitive coupling also has a low impact on a user's perceived privacy.
In this work, I also aim at enhancing the interaction experience with new perceptional capabilities based on capacitive coupling. I follow a bottom-up methodology and begin by presenting two low-level approaches for environmental perception. In order to perceive a user in detail, I present a rapid prototyping toolkit for capacitive proximity sensing. The prototyping toolkit shows significant advancements in terms of temporal and spatial resolution. Due to some limitations, namely the inability to determine the identity and fine-grained manipulations of objects, I contribute a generic method for communications based on capacitive coupling. The method allows for designing highly interactive systems that can exchange information through air and the human body. I furthermore show how human body parts can be recognized from capacitive proximity sensors. The method is able to extract multiple object parameters and track body parts in real-time. I conclude my thesis with contributions in the domain of context-aware devices and explicit gesture-recognition systems
Proceedings of the 4th Workshop on Interacting with Smart Objects 2015
These are the Proceedings of the 4th IUI Workshop on Interacting with
Smart Objects. Objects that we use in our everyday life are expanding
their restricted interaction capabilities and provide functionalities
that go far beyond their original functionality. They feature computing
capabilities and are thus able to capture information, process and store
it and interact with their environments, turning them into smart objects
Finding Common Ground: A Survey of Capacitive Sensing in Human-Computer Interaction
For more than two decades, capacitive sensing has played a prominent role in human-computer interaction research. Capacitive sensing has become ubiquitous on mobile, wearable, and stationary devices---enabling fundamentally new interaction techniques on, above, and around them. The research community has also enabled human position estimation and whole-body gestural interaction in instrumented environments. However, the broad field of capacitive sensing research has become fragmented by different approaches and terminology used across the various domains. This paper strives to unify the field by advocating consistent terminology and proposing a new taxonomy to classify capacitive sensing approaches. Our extensive survey provides an analysis and review of past research and identifies challenges for future work. We aim to create a common understanding within the field of human-computer interaction, for researchers and practitioners alike, and to stimulate and facilitate future research in capacitive sensing
Capacitive Sensing and Communication for Ubiquitous Interaction and Environmental Perception
During the last decade, the functionalities of electronic devices within a living environment constantly increased. Besides the personal computer, now tablet PCs, smart household appliances, and smartwatches enriched the technology landscape. The trend towards an ever-growing number of computing systems has resulted in many highly heterogeneous human-machine interfaces. Users are forced to adapt to technology instead of having the technology adapt to them. Gathering context information about the user is a key factor for improving the interaction experience. Emerging wearable devices show the benefits of sophisticated sensors which make interaction more efficient, natural, and enjoyable. However, many technologies still lack of these desirable properties, motivating me to work towards new ways of sensing a user's actions and thus enriching the context. In my dissertation I follow a human-centric approach which ranges from sensing hand movements to recognizing whole-body interactions with objects.
This goal can be approached with a vast variety of novel and existing sensing approaches. I focused on perceiving the environment with quasi-electrostatic fields by making use of capacitive coupling between devices and objects. Following this approach, it is possible to implement interfaces that are able to recognize gestures, body movements and manipulations of the environment at typical distances up to 50cm. These sensors usually have a limited resolution and can be sensitive to other conductive objects or electrical devices that affect electric fields. The technique allows for designing very energy-efficient and high-speed sensors that can be deployed unobtrusively underneath any kind of non-conductive surface. Compared to other sensing techniques, exploiting capacitive coupling also has a low impact on a user's perceived privacy.
In this work, I also aim at enhancing the interaction experience with new perceptional capabilities based on capacitive coupling. I follow a bottom-up methodology and begin by presenting two low-level approaches for environmental perception. In order to perceive a user in detail, I present a rapid prototyping toolkit for capacitive proximity sensing. The prototyping toolkit shows significant advancements in terms of temporal and spatial resolution. Due to some limitations, namely the inability to determine the identity and fine-grained manipulations of objects, I contribute a generic method for communications based on capacitive coupling. The method allows for designing highly interactive systems that can exchange information through air and the human body. I furthermore show how human body parts can be recognized from capacitive proximity sensors. The method is able to extract multiple object parameters and track body parts in real-time. I conclude my thesis with contributions in the domain of context-aware devices and explicit gesture-recognition systems
Domain-Invariant Representation Learning from EEG with Private Encoders
Deep learning based electroencephalography (EEG) signal processing methods
are known to suffer from poor test-time generalization due to the changes in
data distribution. This becomes a more challenging problem when
privacy-preserving representation learning is of interest such as in clinical
settings. To that end, we propose a multi-source learning architecture where we
extract domain-invariant representations from dataset-specific private
encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain
alignment approach to impose domain-invariance for encoded representations,
which outperforms state-of-the-art approaches in EEG-based emotion
classification. Furthermore, representations learned in our pipeline preserve
domain privacy as dataset-specific private encoding alleviates the need for
conventional, centralized EEG-based deep neural network training approaches
with shared parameters.Comment: 5 pages, 1 figur
Proceedings of the 4th Workshop on Interacting with Smart Objects 2015
These are the Proceedings of the 4th IUI Workshop on Interacting with
Smart Objects. Objects that we use in our everyday life are expanding
their restricted interaction capabilities and provide functionalities
that go far beyond their original functionality. They feature computing
capabilities and are thus able to capture information, process and store
it and interact with their environments, turning them into smart objects
Capacitive Near-field Communication for Ubiquitous Interaction and Perception
Smart objects within instrumented environments offer an always available and intuitive way of interacting with a system. Connecting these objects to other objects in range or even to smartphones and computers, enables substantially innovative interaction and sensing approaches. In this paper, we investigate the concept of Capacitive Near-Field Communication to enable ubiquitous interaction with everyday objects in a short-range spatial context. Our central contribution is a generic framework describing and evaluating this communication method in Ubiquitous Computing. We prove the relevance of our approach by an open-source implementation of a low-cost object tag and a transceiver offering a high-quality communication link at typical distances up to 15 cm. Moreover, we present three case studies considering tangible interaction for the visually impaired, natural interaction with everyday objects, and sleeping behavior analysis