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During the last decade, a number of research activities have been performed to enable user interfaces and the underlying user activities to be migrated from one device to another. We call this “Nomadic User Interfaces”. The primary goal of these research activities has been to develop the technologies to enable this. However, not much is known about the usability aspects of Nomadic User Interfaces. In this paper we present the results of three different user tests that we conducted to investigate the usefulness and the usability issues of several prototype Nomadic User Interface systems that we developed.

Breathing motion complicates many image-guided interventions working on the thorax or upper abdomen. However, prior knowledge provided by a statistical breathing model, can reduce the uncertainties of organ location. In this paper, a prediction framework for statistical motion modeling is presented and different representations of the dynamic data for motion model building of the lungs are investigated. Evaluation carried out on 4D-CT data sets of 10 patients showed that a displacement vector-based representation can reduce most of the respiratory motion with a prediction error of about 2 mm, when assuming the diaphragm motion to be known.

A fully automatic iterative training approach to generate discriminative shape models for usage in the Generalized Hough Transform (GHT) is presented. The method aims at capturing the shape variability of the target object contained in the training data as well as identifying confusable structures (anti-shapes) and to integrate this information into one model. To distinguish shape and anti-shape points and to determine their importance, an individual positive or negative weight is estimated for each model point with means of a discriminative training technique. The model is built from edge points surrounding the target point and the most confusable structure as identified by the GHT. Through an iterative approach, the performance of the model is gradually improved by extending the training dataset with images, where the current model failed to localize the target point. The proposed method is successfully tested on a set of 670 long-leg radiographs, where it achieves a localization rate of 74-97%.

In anonymous credential schemes, users obtain credentials on certain attributes from an issuer, and later show these credentials to a relying party anonymously and without fully disclosing the attributes. In this paper, we introduce the notion of (anonymous) credential schemes with encrypted attributes, in which issuers certify credentials on encrypted attributes to users. These schemes allow for the possibility that none of the involved parties, including the user, learns the values of the attributes. In fact, we will treat several variations differing in which parties see which attributes in the clear. We present effcient constructions of these new credential schemes, starting from a credential scheme by Brands, and we show that the security of Brands' original scheme is retained. Finally, we sketch several interesting applications of these novel credential schemes.

Concept development for professional domain AmI solutions involvesdifferent stakeholders than those for consumer products, and puts different requirements on experience test methods and facilities. Philips ExperienceLab facility for experience research is described, aswell as trends and lessons learned from its use in the two domains.

Recent technological advances in the field of skin electrodes and on-body sensors indicate a possibility of having an alternative to the traditionally used conductive gel electrodes for measuring electrical signals of the brain (electroencephalogram, EEG). This paper evaluates whether water-based and dry contact electrode solutions can replace the gel ones. The quality of the obtained signal by three headsets, each using 8 electrodes of a different type, is estimated onthe steady state visual evoked potential (SSVEP) brain-computer interface (BCI) use case. The stimuli frequencies in the low (12 to 21Hz) and high (28 to 40Hz) frequency domain were used. Six people, that had different hair length and type, participated in the experiment. SSVEP response in terms of power spectra across different electrodes is compared and the impact of noise on temporal characteristics ofthe response is discussed. For people with shorter hair style the performance of water-based and dry electrodes comes close to the gelones in the optimal setting. On average, the classification accuracy of 0.63 for dry and 0.88 for water-based electrodes is achieved, compared to the 0.96 obtained for gel electrodes. The theoretical maximum of the average information transfer rate across participants was 23bpm for dry, 38bpm for water-based and 67bpm for gel electrodes. Furthermore, the convenience level of all three setups was seen as comparable. These results demonstrate that, having optimized headset and electrode design for dry and water-based electrodes for people with different hair length and type, dry and water-based electrode scan replace gel ones in BCIs and Neurofeedback applications where lower communication speed is acceptable.

Electrophysiological simulations of the atria could improve diagnosis and treatment of cardiac arrhythmia, like atrial fibrillation or flutter. For this purpose, a precise segmentation of both atria is needed. However, the atrial epicardium and the electrophysiological structures needed for electrophysiological simulations are barely or not at all detectable in CT-images. Therefore, a model based segmentation of only the atrial endocardium was developed as a landmark generator to facilitate the registration of a finite wall thickness model of the right and left atrial myocardium. It further incorporates atlas information about tissue structures relevant for simulation purposes like Bachmann’s bundle, terminal crest, sinus node and the pectinate muscles. The correct model based segmentation of the atrial endocardium was achieved with a mean vertex to surface error of 0.53 mm for the left and 0.18 mm for the right atrium respectively. The atlas based myocardium segmentation yields physiologically correct results well suited for electrophysiological simulations.

Prolonged sitting is a potential health risk, not only for people with an inactive lifestyle, but also for those who do meet the recommended amount of physical activity. In this paper, we present two waysto promote the reduction of sedentary behavior. First, we report onan experiment in which office workers (n = 40) received timely persuasive messages on their smartphones, advising them to take an active break whenever 30 minutes of almost uninterrupted computer activity was recorded. The messages resulted in a significant decrease in computer activity and a peak in physical activity, indicating that participants complied to the given advice and took short breaks upon receiving a message. Second, we developed SitCoach, a mobile application to nudge office workers from their seats. SitCoach monitors physical activity and sedentary behavior to provide timely feedback by means of suggested sitting breaks. The results of a user test showedthat the general awareness of the importance of sitting breaks is low. In addition, the ability to take sitting breaks was considered to be highly dependent on external factors. This suggests that raising awareness and increasing self-efficacy are important prerequisitesfor a successful intervention to reduce sedentary behavior.

Brain computer interfaces (BCI) that use the steady-state-visual-evoked-potential (SSVEP) as neural source, offer two main advantages over other types of BCIs: shorter calibration times and higher information transfer rates. SSVEPs elicited by high frequency (larger than30 Hz) repetitive visual stimulation are less prone to cause visualfatigue, safer, and more comfortable for the user. However in the high frequency range there is a practical limitation because only fewfrequencies can elicit sufficiently strong SSVEPs for BCI purposes.We bypass this limitation by using only one stimulation frequency and different phases. To detect the phase from the recorded SSVEP, weuse spatial filtering combined to phase synchrony analysis. We developed an online BCI implementation which was tested on six subjects and resulted on an average accuracy of 95.5% and an average bit rateof 34 bits-per-minute. Our approach has the advantage of entailing only minimal visual annoyance for the user.

Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. Inthis context, we assume that user preferences can be specified by program types (documentary, sports, ...) and that an asset can be labeled by one or more program types, thus allowing an initial and coarse preselection of potentially interesting assets. As these assets may come from various sources, program type labels may not be consistent among these sources, or not even be given at all, while we assume that each asset has a possibly short textual description. In this paper, we tackle this problem by considering whether those textual descriptions can be effectively used to automatically retrieve the most related TV shows for a specific program type. More specifically, we compare a statistical approach called logistic regression with anenhanced version of the commonly used vector space model, called random indexing, where the latter is extended by means of a negation operator based on quantum logic. We also apply a new feature generation technique based on explicit semantic analysis for enriching the textual description associated to a TV show with additional features extracted from Wikipedia.

Recommender systems are becoming popular tools to aid users in finding interesting and relevant TV-shows and other digital video assets,based on implicitly learned user preferences. In this context, a common assumption is that user preferences can be specified by program types (movie, sports, ...) and that an asset can be labeled by oneor more program types, thus allowing an initial coarse pre-selection of potentially interesting assets. Furthermore each asset has a short textual description, which allows us to investigate whether it might be useful to automatically label assets with program type labels. To that purpose we compared the Vector Space Model with more recent approaches to text classification, such as Logistic Regression and Random Indexing on a large collection of TV-shows descriptions. The experimental results show that LR is the best approach, but RI outperforms VSM under particular conditions.

Without non-linear basis functions many problems can not be solved by linear algorithms. This article proposes a method to automatically construct such basis functions with slow feature analysis (SFA). Non-linear optimization of this unsupervised learning method generates an orthogonal basis on the unknown latent space for a given time series. In contrast to methods like PCA, SFA is thus well suited for techniques that make direct use of the latent space. Real-world time series can be complex, and current SFA algorithms are either not powerful enough or tend to over-fit.We make use of the kernel trick in combination with sparsification to develop a kernelized SFA algorithm which provides a powerful function class for large data sets. Sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well. For small data sets, however, the kernel SFA approach leads to over-fitting and numerical instabilities. To enforce a stable solution, we introduce regularization to the SFA objective. We hypothesize that our algorithm generates a feature space that resembles a Fourier basis in the unknown space of latent variables underlying a given real-world time series. We evaluate this hypothesis at the example of a vowel classification task in comparison to sparse kernel PCA. Our results show excellent classification accuracy and demonstrate the superiority of kernel SFA over kernel PCA in encoding latent variables.

Mood is an important aspect of music and knowledge on mood can be used as a basic ingredient in music recommender and retrieval systems.A music experiment was carried out establishing ratings for variousmoods and a number of attributes like valence and arousal. The analysis of these data is presented in this paper covering the issues ofthe number of basic dimensions in music mood, their relation to valence and arousal, the distribution of moods in the valence-arousal plane, distinctiveness of the labels, and appropriate (number of) labels for full coverage of the plane. It is also shown that subject-averaged valence and arousal ratings can be predicted from music features by a linear model.

In this work we present a method for automated orthopaedic measurements for patients that have undergone a partial or full joint replacement in the lower limbs. In contrast to previously published approaches for partially occluded objects, we deal with objects were the major part of the contour is missing, namely the epiphyses of the longbones in the lower limbs, that have been replaced in large parts byartificial joint implants of varying appearance. We present an approach based on the automatic detection and segmentation of implants and a robust adaptation of a segmentation technique based on reformable models. We evaluated our method on a set of clinical images and achieve an accuracy of 0.6° for angles and 1.3mm for lengths measurements while significantly reducing assessment time and eliminating user interaction.

We have integrated the Philips Research robot arm with the Johns Hopkins University cisst library, an open-source platform for computerassisted surgical intervention. The development of a Matlab to C++ wrapper to abstract away servo-level details facilitates the rapid development of a component-based framework with “plug and play” features. This allows the user to easily exchange the robot with an alternative manipulator while maintaining the same overall functionality.

ABSTRACT Background: Heart failure (HF) and atrial fibrillation (AF), emerging as two epidemics of the 21st century, are commonly associated with each other. Both have been mechanistically linked to changes in cardiac vagal control. The importance of peripheral chemosensors, residing in the carotid body, within this relationship betweenAF and HF has not been elucidated so far. We therefore investigatedwhether tonic activation of excitatory chemoreceptor afferents contributes to the altered vagal control in HF patients with a history ofAF. Methods and results: In 18 patients (72±9y, 7male) with sinusrhythm and a history of AF (n=9, without any evidence of structuralheart disease, AF group; n=9 with structural heart disease and clinical presentation of HF, AFHF group) we investigated the impact of chemosensory deactivation (by breathing 100% oxygen) on hemodynamics,oxygen saturation and breathing rate. 10 healthy individuals servedas a control group. In addition, we performed a deep breathing testdemonstrating an impaired heart rate variation in patients withoutand with HF as compared to controls (Expiration/Inspiration difference: 23.9±6.9 vs 6.9±6.1bpm, and 23.9±6.9 vs 7.8±4.8bpm; p<0.05). Inthe control as well as the AF group heart rate decreased during chemoreceptor deactivation (control: -4.8±3.4%; AF: -5.1±3.0%; p<0.05),whereas heart rate did not change in AFHF patients. This resulted inan impaired cardiac chemoreflex sensitivity in AFHF patients (1.9±1.6 vs 0.5±1.2 ms/mmHg; p<0.05). Conclusions: Our data suggest thattonic activation of excitatory chemoreceptor afferents contribute toa low vagal tone in heart failure patients with a history of AF. (Clinical Trials NCT01262508)

This manuscript shows that Bloch simulations provide a convenient way for predicting response of a modified Look Locker (MOLLI) T1 mapping sequence to various parameters that affect T1 accuracy of the technique. In particular, the effect of T2 on accuracy is shown. Phantom- and first in vivo experiments were conducted to verify the simulation results. Error in estimated T1 values (from gold standard T1 values) was 0.4%, -1.8% and -5.3%, respectively, while with simulatedvalues was 0.7%, -1.3% and -4.9% for the three phantoms across all experiments. For the in-vivo studies, measured T1 values were on average 7.2% lower than the true values while simulated values were lower by 7.4%. Change in T1 values due to off-resonance was minor.

Driver anger could be potentially harmful for road safety and long-term health. Because of its mood inducing properties, music is assumed to be a potential medium that could prevent anger induction duringdriving. In the current study the influence of music on anger, mood, skin conductance, and systolic blood pressure was investigated during anger inducing scenarios in a driving simulator. 100 participants were split into five groups: four listened to different types of music (high/ low energy in combination with both positive/ negative valence) or a no music control. Results showed that anger inducementwas highest during high energy negative music compared to positive music irrespective of energy level. Systolic blood pressure and skinconductance levels were higher during high energy negative music andno music compared to low energy music. Music was demonstrated to mediate the state of anger and therefore can have positive health benefits in the long run.

While studying body postures in relation to mood is not a new concept, the majority of these studies rely on actors interpretations. This project investigated the temporal aspects of naturalistic body postures while users listened to mood inducing music. Video data was collected while participants listened to eight minutes of music duringtwo sessions (happy and sad) in a within-subjects design. Subjectively reported mood scores validated that mood did differ significantly for valence and energy. Video analysis consisted of postural ratings for the head, shoulders, trunk, arms, and head &amp; hand tapping. Results showed significant differences for the majority of these dimensions by mood. This study showed that certain body postures are indicative of certain mood states in a naturalistic setting. Automatic recognition of body postures can thus lead to one of the first unobtrusive mood recognition systems, which will help in the development of emotionally smart technology.