Reinder Haakma

Improved efficiency through I- and E-feedback

It has been argued by Engel and Haakma (1993, Expectations and feedback in user-system communication, International Journal of Man-Machine Studies, 39, 427-452) that for user-system communication to become more efficient, machine interfaces should present both early layered I-feedback on the current partial message interpretation as well as layered expectations (E-feedback) concerning the message components still to be communicated. As a clear example of our claim, this paper describes an experimental trackball device that provides the user with the less common E-feedback in addition to the conventional layered I-feedback in the form of the momentary cursor position on the screen and the kinetic forces from the ball. In particular, the machine expresses its expectation concerning the goal position of the cursor by exerting an extra force to the trackball.Two optical sensors and two servo motors are used in the described trackball device with contextual force feedback. One combination of position sensor and servo motor handles the cursor position and tactile feedback along the x -axis, the other combination controls that along the y-axis. By supplying supportive force feedback as a function of the current display contents and the momentary cursor position, the users movements are guided towards the cursor target position expected by the machine. The force feedback diminishes the visual processing load of the user and combines increased ease of use with robustness of manipulation.Experiments with a laboratory version of this new device have shown that the force feedback significantly enhances speed and accuracy of pointing and dragging, while the effort needed to master the trackball is minimal compared with that for the conventional trackball without force feedback.

Sleep and Wake Classification With Actigraphy and Respiratory Effort Using Dynamic Warping

This paper proposes the use of dynamic warping (DW) methods for improving automatic sleep and wake classification using actigraphy and respiratory effort. DW is an algorithm that finds an optimal nonlinear alignment between two series allowing scaling and shifting. It is widely used to quantify (dis)similarity between two series. To compare the respiratory effort between sleep and wake states by means of (dis)similarity, we constructed two novel features based on DW. For a given epoch of a respiratory effort recording, the features search for the optimally aligned epoch within the same recording in time and frequency domain. This is expected to yield a high (or low) similarity score when this epoch is sleep (or wake). Since the comparison occurs throughout the entire-night recording of a subject, it may reduce the effects of within- and between-subject variations of the respiratory effort, and thus help discriminate between sleep and wake states. The DW-based features were evaluated using a linear discriminant classifier on a dataset of 15 healthy subjects. Results show that the DW-based features can provide a Cohens Kappa coefficient of agreement κ = 0.59 which is significantly higher than the existing respiratory-based features and is comparable to actigraphy. After combining the actigraphy and the DW-based features, the classifier achieved a κ of 0.66 and an overall accuracy of 95.7%, outperforming an earlier actigraphy- and respiratory-based feature set ( κ = 0.62). The results are also comparable with those obtained using an actigraphy- and cardiorespiratory-based feature set but have the important advantage that they do not require an ECG signal to be recorded.

Analyzing respiratory effort amplitude for automated sleep stage classification

Abstract 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 Cohens 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.

Sleep stage classification with ECG and respiratory effort

Automatic sleep stage classification with cardiorespiratory signals has attracted increasing attention. In contrast to the traditional manual scoring based on polysomnography, these signals can be measured using advanced unobtrusive techniques that are currently available, promising the application for personal and continuous home sleep monitoring. This paper describes a methodology for classifying wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) light and deep sleep on a 30 s epoch basis. A total of 142 features were extracted from electrocardiogram and thoracic respiratory effort measured with respiratory inductance plethysmography. To improve the quality of these features, subject-specific Z-score normalization and spline smoothing were used to reduce between-subject and within-subject variability. A modified sequential forward selection feature selector procedure was applied, yielding 80 features while preventing the introduction of bias in the estimation of cross-validation performance. PSG data from 48 healthy adults were used to validate our methods. Using a linear discriminant classifier and a ten-fold cross-validation, we achieved a Cohens kappa coefficient of 0.49 and an accuracy of 69% in the classification of wake, REM, light, and deep sleep. These values increased to kappa = 0.56 and accuracy = 80% when the classification problem was reduced to three classes, wake, REM sleep, and NREM sleep.

Measuring dissimilarity between respiratory effort signals based on uniform scaling for sleep staging

Polysomnography (PSG) has been extensively studied for sleep staging, where sleep stages are usually classified as wake, rapid-eye-movement (REM) sleep, or non-REM (NREM) sleep (including light and deep sleep). Respiratory information has been proven to correlate with autonomic nervous activity that is related to sleep stages. For example, it is known that the breathing rate and amplitude during NREM sleep, in particular during deep sleep, are steadier and more regular compared to periods of wakefulness that can be influenced by body movements, conscious control, or other external factors. However, the respiratory morphology has not been well investigated across sleep stages. We thus explore the dissimilarity of respiratory effort with respect to its signal waveform or morphology. The dissimilarity measure is computed between two respiratory effort signal segments with the same number of consecutive breaths using a uniform scaling distance. To capture the property of signal morphological dissimilarity, we propose a novel window-based feature in a framework of sleep staging. Experiments were conducted with a data set of 48 healthy subjects using a linear discriminant classifier and a ten-fold cross validation. It is revealed that this feature can help discriminate between sleep stages, but with an exception of separating wake and REM sleep. When combining the new feature with 26 existing respiratory features, we achieved a Cohens Kappa coefficient of 0.48 for 3-stage classification (wake, REM sleep and NREM sleep) and of 0.41 for 4-stage classification (wake, REM sleep, light sleep and deep sleep), which outperform the results obtained without using this new feature.

The theoretical foundation and validity of a component-based usability questionnaire

Although software engineers extensively use a component-based software engineering (CBSE) approach, existing usability questionnaires only support a holistic evaluation approach, which focuses on the usability of the system as a whole. Therefore, this paper discusses a component-specific questionnaire for measuring the perceived ease-of-use of individual interaction components. A theoretical framework is presented for this compositional evaluation approach, which builds on Taylors layered protocol theory. The application and validity of the component-specific measure is evaluated by re-examining the results of four experiments. Here, participants were asked to use the questionnaire to evaluate a total of nine interaction components used in a mobile phone, a room thermostat, a web-enabled TV set and a calculator. The applicability of the questionnaire is discussed in the setting of a new usability study of an MP3 player. The findings suggest that at least part of the perceived usability of a product can be evaluated on a component-based level.

Spectral Boundary Adaptation on Heart Rate Variability for Sleep and Wake Classification

A method of adapting the boundaries when extracting the spectral features from heart rate variability (HRV) for sleep and wake classification is described. HRV series can be derived from electrocardiogram (ECG) signals obtained from single-night polysomnography (PSG) recordings. Conventionally, the HRV spectral features are extracted from the spectrum of an HRV series with fixed boundaries specifying bands of very low frequency (VLF), low frequency (LF), and high frequency (HF). However, because they are fixed, they may fail to accurately reflect certain aspects of autonomic nervous activity which in turn may limit their discriminative power, e.g. in sleep and wake classification. This is in part related to the fact that the sympathetic tone (partially reflected in the LF band) and the respiratory activity (modulated in the HF band) vary over time. In order to minimize the impact of these variations, we adapt the HRV spectral boundaries using time-frequency analysis. Experiments were conducted on a data set acquired from two groups with 15 healthy and 15 insomnia subjects each. Results show that adapting the HRV spectral features significantly increased their discriminative power when classifying sleep and wake. Additionally, this method also provided a significant improvement of the overall classification performance when used in combination with other HRV non-spectral features. Furthermore, compared with the use of actigraphy, the classification performed better when combining it with the HRV features.

Time-frequency analysis of heart rate variability for sleep and wake classification

This paper describes a method to adapt the spectral features extracted from heart rate variability (HRV) for sleep and wake classification. HRV series can be derived from electrocardiogram (ECG) signals obtained from single-night polysomnography (PSG) recordings. Traditionally, the HRV spectral features are extracted from the spectrum of an HRV series with fixed boundaries specifying bands of very low frequency (VLF), low frequency (LF), and high frequency (HF). However, because they are fixed, they may fail to accurately reflect certain aspects of autonomic nervous activity, which in turn may limit their discriminative power when using HRV spectral features, e.g., in sleep and wake classification. This is in part related to the fact that the sympathetic tone (partially reflected in the LF band) and the respiratory activity (modulated in the HF band) will vary over time. In order to minimize the impact of these differences, we adapt the HRV spectral boundaries using time-frequency analysis. Experiments conducted on a dataset acquired from 15 healthy subjects show that the discriminative power of the adapted HRV spectral features are significantly increased when classifying sleep and wake. Additionally, this method also provides a significant improvement of the overall classification performance when used in combination with some other (non-spectral) HRV features.

Layered protocols: hands-on experience

An assessment is presented of the benefits and limitations of the Layered Protocols (LP) model for the analysis and design of user interfaces in the field of consumer electronics. In the assessment a user interface of an existing digital audio recorder which is only partly in line with the LP model is compared with an interface designed according to the model. The observed differences in usability between the two interfaces are mainly caused by deviations from the LP model. It turned out that especially the learnability of an interface is positively influenced by a layered organization of user-system interaction in combination with high-quality E- and I-feedback and optimum similarity between interaction protocols.

Heuristic approaches for generating Local Process Models through log projections

Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. subsets of possible events are taken into account to create socalled local process models. Often such smaller models provide valuable insights into the behavior of the process, especially when no adequate and comprehensible single overall process model exists that is able to describe the traces of the process from start to end. The practical application of LPM discovery is however hindered by computational issues in the case of logs with many activities (problems may already occur when there are more than 17 unique activities). In this paper, we explore three heuristics to discover subsets of activities that lead to useful log projections with the goal of speeding up LPM discovery considerably while still finding high-quality LPMs. We found that a Markov clustering approach to create projection sets results in the largest improvement of execution time, with discovered LPMs still being better than with the use of randomly generated activity sets of the same size. Another heuristic, based on log entropy, yields a more moderate speedup, but enables the discovery of higher quality LPMs. The third heuristic, based on the relative information gain, shows unstable performance: for some data sets the speedup and LPM quality are higher than with the log entropy based method, while for other data sets there is no speedup at all.