Layered feedback in user-system interaction

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Design Methods for Interactive TV : two empirical studies

The central question for this paper is how to improve the production process by closing the gap between industrial designers and software engineers of television(TV)-based User Interfaces (UI) in an industrial environment. Software engineers are highly interested whether one UI design can be converted into several fully functional UIs for TV products with different screen properties. The aim of the software engineers is to apply automatic layout and scaling in order to speed up and improve the production process. However, the question is whether a UI design lends itself for such automatic layout and scaling. This is investigated by analysing a prototype UI design done by industrial designers. In a first requirements study, industrial designers had created meta annotations on top of their UI design in order to disclose their design rationale for discussions with software engineers. In a second study, industrial designers assessed the potential of four different meta annotation approaches. The question was which annotation method industrial designers would prefer and whether it could satisfy the technical requirements of the software engineering process. One main result is that the industrial designers preferred the method they were already familiar with, which therefore seems to be the most effective one although the main objective of automatic layout and scaling could still not be achieved

On generation of time-based label refinements

Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing). Refinements of sensor level event labels suggested by domain experts have shown to enable discovery of more precise and insightful process models. However, there exist no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for automated generation of label refinements based on the time attribute of events. We show on a case study with real life smart home event data that behaviorally more specific, and therefore more insightful, process models can be found by using automatically generated refined labels in process discovery.Comment: Accepted at CS&P workshop 2016 Overlap in preliminaries with arXiv:1606.0725

Expectation-based user interaction

Multimedia and multimodal interfaces reflect the growing technological possibilities of computer-based systems for interaction with the user. The ongoing increase in communication bandwidth and the growing variety of communication channels enable further improvement in the user interface. However, how this increased communication capacity can optimally be exploited is as yet unknown. Since the functionality of these computer-based systems also continues to grow, the increased complexity of interaction procedures and the difficulty of mastering them are prime issues in the design of "easy to use" multimodal user interfaces. In order to appreciate more fully what is involved in self-evident and at the same time efficient interaction between user and system, we will first briefly describe the layered-protocol model of computer-human dialogue as proposed by Taylor (1988a). This conceptual framework emphasizes the relevance of layered feedback for the efficiency of communication. As indicated by Engel & Haakma (1993), in particular early feedback about the system's interpretation of the message part already received (I-feedback) as well as on machine expectations about message elements still to be received (E-feedback) are of relevance for the system's ease of use. Thereafter, as an interesting example of improved human-computer interaction through layered multimodal I- and E-feedback, an experimental trackball device will be described. It provides the user, in addition to the standard visual I-feedback about the current cursor position, with tactile E-feedback about the expected cursor target position. Lastly, our running experimental exploration of the possibilities for automatic cursor-endpoint prediction will be described, this research being of relevance for the further improvement of interaction with the mentioned trackball device with expectation-based force-feedback

On the reactivity of sleep monitoring with diaries

The declining costs of wearable sensors have made self-monitoring of sleep related behavior easier for personal use but also for sleep studies. Several monitor devices come with apps that make use of diary entries to provide people with an overview of their sleeping habits and give remotely advice. However, it could be that filling in a sleep diary impacts people's perception of their sleep or the very behavior that is being measured. A small-scale field study about the effects of sleep monitoring (keeping a sleep diary) on a cognitive and a behavioral level is discussed. The method was designed to be as open as possible in order to focus on the effects of sleep monitoring where participants are not given a goal, motivation or feedback. Some behavioral modifications were observed, for example, differences in total sleep time and bedtimes were found (compared to a non-monitoring week and a monitoring week). Nevertheless, what the causes are of these changes remains unclear, as it turned out that the two actigraph devices used in this study differed greatly. In addition, some participants became more aware of their sleeping routine, but changing a sleeping habit was found challenging because of other priorities. It is important to know what the effects may be of sleep monitoring as the outcomes may already have an effect on the participant behavior which could cause researchers to work with data that do not represent a real life situation. In addition, the self-monitoring may serve as an intervention for facilitating healthier sleeping habits.</p

Using dynamic time warping for sleep and wake discrimination

In previous work, a Linear Discriminant (LD) classifier was used to classify sleep and wake states during single-night polysomnography recordings (PSG) of actigraphy, respiratory effort and electrocardiogram (ECG). In order to improve the sleep-wake discrimination performance and to reduce the number of modalities needed for class discrimination, this study incorporated Dynamic Time Warping (DTW) to help discriminate between sleep and wake states based on actigraphy and respiratory effort signal. DTW quantifies signal similarities manifested in the features extracted from the respiratory effort signal. Experiments were conducted on a dataset acquired from nine healthy subjects, using an LD-based classifier. Leave-one- out cross-validation shows that adding this DTW-based feature to the original actigraphy- and respiratory-based feature set results in an epoch-by-epoch Cohen’s Kappa agreement coefficient of ¿ = 0.69 (at an overall accuracy of 95.4%), which represents a significant improvement when compared with the performance obtained without using this feature. Furthermore it is comparable to the result obtained in the previous work which used additional ECG features (¿ = 0.70)

Analyzing respiratory effort amplitude for automated sleep stage classification

Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory amplitude, it has been found that the respiratory depth is more irregular and the tidal volume is smaller during rapid-eye-movement (REM) sleep than during non-REM (NREM) sleep. However, these physiological properties have not been explicitly elaborated for sleep stage classification. By analyzing the respiratory effort amplitude, we propose a set of 12 novel features that should reflect respiratory depth and volume, respectively. They are expected to help classify sleep stages. Experiments were conducted with a data set of 48 sleepers using a linear discriminant (LD) classifier and classification performance was evaluated by overall accuracy and Cohen's Kappa coefficient of agreement. Cross validations (10-fold) show that adding the new features into the existing feature set achieved significantly improved results in classifying wake, REM sleep, light sleep and deep sleep (Kappa of 0.38 and accuracy of 63.8%) and in classifying wake, REM sleep and NREM sleep (Kappa of 0.45 and accuracy of 76.2%). In particular, the incorporation of these new features can help improve deep sleep detection to more extent (with a Kappa coefficient increasing from 0.33 to 0.43). We also revealed that calibrating the respiratory effort signals by means of body movements and performing subject-specific feature normalization can ultimately yield enhanced classification performance. Keywords Respiratory effort amplitude; Signal calibration; Feature extraction; Sleep stage classificatio