Jarek Krajewski

Rheinische Fachhochschule Köln gGmbH | rfh- Köln · Department of Psychology

Background. Relapse is a significant risk among people treated for depression, with approximately 50% of patients experiencing a recurrence after their initial depressive episode. This risk increases with subsequent episodes. This alarming statistic underscores the urgent need for innovative, reliable tools to identify individuals at high risk of r...

Background. Virtual reality (VR) has been used successfully and effectively in psychotherapy for a variety of disorders. In the field of depression, there are only a few VR interventions and approaches. Although simple social interactions have been successfully modeled in VR for several mental disorders, there has been no transfer to the field of d...

Early therapeutic intervention programs help children diagnosed with Autism Spectrum Disorder (ASD) to improve their socio-emotional and functional skills. To relieve the children’s caregivers while ensuring that the children are adequately supported in their training exercises, new technologies may offer suitable solutions. This study investigates...

This paper presents a deep learning-based analysis and classification of cold speech observed when a person is diagnosed with the common cold. The common cold is a viral infectious disease that affects the throat and the nose. Since speech is produced by the vocal tract after linear filtering of excitation source information, during a common cold,...

We study the statistical properties of facial behaviour altered by the regulation of brain arousal in the clinical domain of psychiatry. The underlying mechanism is linked to the empirical interpretation of the vigilance continuum as behavioral surrogate measurement for certain states of mind. Referring to the classical scalp-based obtrusive measur...

This article presents research on the detection of pathologies affecting speech through automatic analysis. Voice processing has indeed been used for evaluating several diseases such as Parkinson, Alzheimer, or depression. If some studies present results that seem sufficient for clinical applications, this is not the case for the detection of sleep...

This article presents research on the detection of pathologies affecting speech through automatic analysis. Voice processing has indeed been used for evaluating several diseases such as Parkinson, Alzheimer or depression. If some studies present results that seem sufficient for clinical applications, this is not the case for the detection of sleepi...

Fatigued driving is one of the main contributors to road traffic accidents. Poor sleep quality and lack of sleep negatively affect driving performance, and extreme states of fatigue can cause microsleep (i.e. short episodes of sleep with complete loss of awareness). Driver monitoring systems analyze biosignals (e.g. gaze, blinking, heart rate) and...

This paper is about automatic sleepiness state detection using speech samples. Following previous research carried out for the Interspeech 2011 challenge, we use the Sleepy Language Corpus (SLC) for our experiments. However, as we are willing to record our own subjects within a collaboration project with the Bordeaux hospital, we focus only on the...

This paper presents the idea of brute force feature extraction for Electrocardiography (ECG) signals applied to discomfort detection. To build an ECG Discomfort Corpus an experimental discomfort induction was conducted. 50 subjects underwent a 2 h (dis-)comfort condition in separate sessions in randomized order. ECG and subjective discomfort was re...

Introduction Frailty increases the risk of poor health outcomes, disability, hospitalization, and death in older adults and affects 7%–12% of the aging population. Secondary impacts of frailty on psychological health and socialization are significant negative contributors to poor outcomes for frail older adults. Method The My Active and Healthy Ag...

We study the impact of prior knowledge about invariance for the task of heart rate estimation from face videos in the wild (e.g. in presence of disturbing factors like rigid head motion, talking, facial expressions and natural illumination conditions under different scenarios). We introduce features invariant with respect to the action of a differe...

This paper introduces a novel speech-based depression score prediction paradigm, the 2-stage ranking prediction framework, and highlights the benefits it brings to depression prediction. Conventional regression approaches aim to discern a single functional relationship between speech features and depression scores, making an implicit assumption abo...

This paper presents analysis and classification of a pathological speech called cold speech, which is recorded when the person is suffering from common cold. Nose and throat are affected by the common cold. As nose and throat play an important role in speech production, the speech characteristics are altered during this pathology. In this work, var...

This study aims to provide an interdisciplinary perspective on biosignal processing for driver monitoring systems. Machine learning models were trained with ECG, Pupil Diameter and Eyelid Opening data from overnight sleep deprived subjects experiencing microsleep in a supervised test track driving environment. Model performance of 0.85 accuracy and...

This study aims to provide an interdisciplinary perspective on biosignal processing for driver monitoring systems. Machine learning models were trained with ECG, Pupil Diameter and Eyelid Opening data from overnight sleep deprived subjects experiencing micros-leep in a supervised test track driving environment. Model performance of 0.85 accuracy an...

The role of physiological signals has a large impact on driver monitoring systems, since it tells something about the human state. This work addresses the recursive probabilistic inference problem in time-varying linear dynamic systems to incorporate invariance into the task of heart rate estimation from face videos under realistic conditions. The...

This work addresses the problem of estimating heart rate from face videos under real conditions using a model based on the recursive inference problem that leverages the local invariance of the heart rate. The proposed solution is based on the canonical state space representation of an Itō process and a Wiener velocity model. Empirical results yiel...

A combination of passive, non-invasive and nonintrusive smart monitoring technologies is currently transforming healthcare. These technologies will soon be able to provide immediate health related feedback for a range of illnesses and conditions. Such tools would be game changing for serious public health concerns, such as seasonal cold and flu, fo...

This paper presents performance results, time complexities, and feature reduction aspects of three wrapper-based acoustic feature selection methods used for automatic sleepiness detection: Between-Groups Feature Selection (BGFS), Within-Groups Feature Selection (WGFS), and Individual Feature Selection (IFS) methods. Furthermore, two different metho...

This contribution addresses the question if imminent changes of the cortical state are predictable. The analysis is based on 1484 examples of microsleep (MS) and 1940 counterexamples of sustained attention (SA), both observed during overnight driving in the simulator. EEG segments (8 s in length) immediately before each respective event were includ...

The spectral and energy properties of speech have consistently been observed to change with a speaker's level of clinical depression. This has resulted in spectral and energy based features being a key component in many speech-based classification and prediction systems. However there has been no in-depth investigation into understanding how acoust...

An overview of several methods of electroencephalography (EEG) analysis in order to assess driver sleepiness is presented. All methods were applied to one single data set obtained from overnight driving simulations in our lab. 10 young adults (age 22.4 ± 4.1 years) participated and drove on rural roads; time on task was 7 x 40 min and time since sl...

In the present article we assume that people with a high degree of dysfunctional cognition do not fulfil their potential. After introductory statements regarding the concept of job-related dysfunctional cognition, we show how dysfunctional cognition reduces employees' willingness to perform. Following this, we investigate the role that these mechan...

This paper is the first review into the automatic analysis of speech for use as an objective predictor of depression and suicidality. Both conditions are major public health concerns; depression has long been recognised as a prominent cause of disability and burden worldwide, whilst suicide is a misunderstood and complex course of death that strong...

This paper presents a technique in which feature vectors are mapped onto ordinal ranges of clinical depression scores using weighted pairwise Gaussians. The position of a test vector with respect to these partitions is used to perform depression score prediction. Results found on a set of spectral and formant based speech characteristics indicate t...

Mood disorders are inherently related to emotion. In particular, the behaviour of people suffering from mood disorders such as unipolar depression shows a strong temporal correlation with the affective dimensions valence, arousal and dominance. In addition to structured self-report questionnaires, psychologists and psychiatrists use in their evalua...

Mood disorders are inherently related to emotion. In particular, the behaviour of people suffering from mood disorders such as unipolar depression shows a strong temporal correlation with the affective dimensions valence, arousal and dominance. In addition to structured self-report questionnaires, psychologists and psychiatrists use in their evalua...

Alterations in speech motor control in depressed individuals have been found to manifest as a reduction in spectral variability. In this paper we present a novel method for measuring acoustic volume - a model-based measure that is reflective of this decrease in spectral variability - and assess the ability of features resulting from this measure fo...

The INTERSPEECH 2014 Computational Paralinguistics Challenge provides for the first time a unified test-bed for the automatic recognition of speakers' cognitive and physical load in speech. In this paper, we describe these two Sub-Challenges, their conditions, baseline results and experimental procedures, as well as the COMPARE baseline features ge...

Variations in the acoustic space due to changes in speaker mental state are potentially overshadowed by variability due to speaker identity and phonetic content. Using the Audio/Visual Emotion Challenge and Workshop 2013 Depression Dataset we explore the suitability of i-vectors for reducing these latter sources of variability for distinguishing be...

The degree of sleepiness in the Sleepy Language Corpus from the Interspeech 2011 Speaker State Challenge is predicted with regression and a very large feature vector. Most notable is the great gender difference which can mainly be attributed to females showing their sleepiness less than males do.

In the emerging field of computational paralinguistics, most research efforts are devoted to either short-term speaker states such as emotions, or long-term traits such as personality, gender, or age. To bridge this gap on the time axis, and hence broaden the scope of the field, the INTERSPEECH 2011 Speaker State Challenge addressed the algorithmic...

We propose a method based on perceptual prosodic features for medium term speaker state classification, particularly sleepiness detection. Unlike existing methods, our features represent spectral characteristics of speech in perceptual bands and also track temporal content omitting any linguistic segmentation. Despite conventional methods, we aim t...

When we address speaker states like sleepiness, two partly competing interests can be observed: both within applications and engineering approaches, we aim at utmost performance in terms of classification or regression accuracy-which normally means using a very large feature vector and a brute force approach. The other interest is interpretation: w...

Depression is an affective disorder characterised by psychomotor retardation; in speech, this shows up in reduction of pitch (variation, range), loudness, and tempo, and in voice qualities different from those of typical modal speech. A similar reduction can be observed in sleepy speech (relaxation). In this paper, we employ a small group of acoust...

The third Audio-Visual Emotion Challenge and workshop AVEC 2013 will be held in conjunction ACM Multimedia'13. Like the 2012 edition of AVEC, the workshop/challenge addresses the interpretation of social signals represented in both audio and video in terms of the high-level continuous dimensions arousal and valence, but importantly this year the da...

Periodogram and other spectral power estimation methods are established in quantitative EEG analysis. Their outcome in case of drowsy subjects fulfilling a sustained attention task is difficult to interpret. Two novel kind of EEG analysis based on pattern recognition were proposed recently, namely the microsleep (MS) and the alpha burst (AB) patter...

We propose a two-class classification scheme with a small number of features for sleepiness detection. Unlike the conventional methods that rely on the linguistics content of speech, we work with prosodic features extracted by psychoacoustic masking in spectral and temporal domain. Our features also model the variations between non-sleepy and sleep...

The aim of the study was to elucidate the immediate, intermediate, and anticipatory sleepiness reducing effects of a salutogenic self-care procedure called progressive muscle relaxation (PMR), during lunch breaks. The second exploratory aim deals with determining the onset and long-term time course of sleepiness changes. In order to evaluate the in...

We introduce the automatic determination of leadership emergence by acoustic and linguistic features in on-line speeches. Full realism is provided by the varying and challenging acoustic conditions of the presented YouTube corpus of on-line available speeches labeled by ten raters and by processing that includes Long Short-Term Memory based robust...

This article focuses on consumers’ unconscious counter‐reactions to incidentally received advertising appeals. Recipients can be influenced automatically by incidental ad exposure due to evaluative conditioning processes, mere‐exposure effects, or priming events. However, it is assumed that such processes, especially priming events, can also lead t...

Objective: The purpose of the present study is to provide validation and accuracy data for the pupillographic sleepiness test (PST) on the one hand by applying self-and observer-ratings and on the other hand by fusioned self-and observer-ratings as a sleepiness reference value. Method: 30 healthy women and men took part in a partial sleep-deprivati...

Comparing different novel feature sets and classifiers for speech processing based fatigue detection is the primary aim of this study. Thus, we conducted a within-subject partial sleep deprivation design (20.00–04.00 h, N=77 participants) and recorded 372 speech samples of sustained vowel phonation. The self-report on the Karolinska Sleepiness Scal...