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Dieses Dokument präsentiert eine Zusammenfassung der Dissertation der Autorin. In dieser Dissertation [Ha20] wurde ein neuartiger und hybrider Ansatz für die Scha ̈tzung der Intensität von Gesichtsmuskelbewegungen (Action Unit (AU)) vorgeschlagen und validiert. Dieser Ansatz basiert auf einer Gauß’schen Zustandsschätzung und kombiniert ein verformbares, AU-basiertes Gesichtsformmodell, ein viskoelastisches Modell der Gesichtsmuskelbewegung, mehrere erscheinungsbasierten AU-Klassifikatoren und eine Methode zur Erkennung von Gesichtspunkten. Es wurden mehrere Erweiterungen vorgeschlagen und in das Zustandsschätzungs-Framework integriert, um mit den personenspezifischen Eigenschaften sowie technischen und praktischen Herausforderungen umzugehen.Die mit der vorgeschlagenen Methode erzeugten AU-Intensitätsschätzungen wurden für die automatische Erkennung von Schmerzen und für die Analyse von Fahrerablenkung eingesetzt.
This dissertation presents a probabilistic state estimation framework for integrating data-driven machine learning models and a deformable facial shape model in order to estimate continuous-valued intensities of 22 different facial muscle movements, known as Action Units (AU), defined in the Facial Action Coding System (FACS). A practical approach is proposed and validated for integrating class-wise probability scores from machine learning models within a Gaussian state estimation framework. Furthermore, driven mass-spring-damper models are applied for modelling the dynamics of facial muscle movements. Both facial shape and appearance information are used for estimating AU intensities, making it a hybrid approach. Several features are designed and explored to help the probabilistic framework to deal with multiple challenges involved in automatic AU detection. The proposed AU intensity estimation method and its features are evaluated quantitatively and qualitatively using three different datasets containing either spontaneous or acted facial expressions with AU annotations. The proposed method produced temporally smoother estimates that facilitate a fine-grained analysis of facial expressions. It also performed reasonably well, even though it simultaneously estimates intensities of 22 AUs, some of which are subtle in expression or resemble each other closely. The estimated AU intensities tended to the lower range of values, and were often accompanied by a small delay in onset. This shows that the proposed method is conservative. In order to further improve performance, state-of-the-art machine learning approaches for AU detection could be integrated within the proposed probabilistic AU intensity estimation framework.
It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the drive's ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video cameras. The types of driving distractions that can be sensed with various sensors is an open research question that this study attempts to answer. This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation and facial action units (AUs)) for the detection of four types of distractions. The dataset was collected in a previous driving simulation study. The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising. The experimental comparison of seven classical machine learning (ML) and seven end-to-end deep learning (DL) methods, which were evaluated on a separate test set of 10 subjects, showed that when classifying windows into distracted or not distracted, the highest F1-score of 79%; was realized by the extreme gradient boosting (XGB) classifier using 60-second windows of AUs as input. When classifying complete driving sessions, XGB's F1-score was 94%. The best-performing DL model was a spectro-temporal ResNet, which realized an F1-score of 75%; when classifying segments and an F1-score of 87%; when classifying complete driving sessions. Finally, this study identified and discussed problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.
Towards an Interaction-Centered and Dynamically Constructed Episodic Memory for Social Robots
(2020)
Towards self-explaining social robots. Verbal explanation strategies for a needs-based architecture
(2019)
In order to establish long-term relationships with users, social companion robots and their behaviors need to be comprehensible. Purely reactive behavior such as answering questions or following commands can be readily interpreted by users. However, the robot's proactive behaviors, included in order to increase liveliness and improve the user experience, often raise a need for explanation. In this paper, we provide a concept to produce accessible “why-explanations” for the goal-directed behavior an autonomous, lively robot might produce. To this end we present an architecture that provides reasons for behaviors in terms of comprehensible needs and strategies of the robot, and we propose a model for generating different kinds of explanations.
Towards explaining deep learning networks to distinguish facial expressions of pain and emotions
(2018)
Deep learning networks are successfully used for object and face recognition in images and videos. In order to be able to apply such networks in practice, for example in hospitals as a pain recognition tool, the current procedures are only suitable to a limited extent. The advantage of deep learning methods is that they can learn complex non-linear relationships between raw data and target classes without limiting themselves to a set of hand-crafted features provided by humans. However, the disadvantage is that due to the complexity of these networks, it is not possible to interpret the knowledge that is stored inside the network. It is a black-box learning procedure. Explainable Artificial Intelligence (AI) approaches mitigate this problem by extracting explanations for decisions and representing them in a human-interpretable form. The aim of this paper is to investigate the explainable AI method Layer-wise Relevance Propagation (LRP) and apply it to explain how a deep learning network distinguishes facial expressions of pain from facial expressions of emotions such as happiness and disgust.
This paper describes a dynamic, model-based approach for estimating intensities of 22 out of 44 different basic facial muscle movements. These movements are defined as Action Units (AU) in the Facial Action Coding System (FACS) [1]. The maximum facial shape deformations that can be caused by the 22 AUs are represented as vectors in an anatomically based, deformable, point-based face model. The amount of deformation along these vectors represent the AU intensities, and its valid range is [0, 1]. An Extended Kalman Filter (EKF) with state constraints is used to estimate the AU intensities. The focus of this paper is on the modeling of constraints in order to impose the anatomically valid AU intensity range of [0, 1]. Two process models are considered, namely constant velocity and driven mass-spring-damper. The results show the temporal smoothing and disambiguation effect of the constrained EKF approach, when compared to the frame-by-frame model fitting approach ‘Regularized Landmark Mean-Shift (RLMS)’ [2]. This effect led to more than 35% increase in performance on a database of posed facial expressions.
A device includes an input to sequential data associated to a face; a predictor configured to predict facial parameters; and a corrector configured to correct the predicted facial parameters on the basis of input data, the input data containing geometric measurements and other information. A related method and a related computer program are also disclosed.
BACKGROUND
Given the unreliable self-report in patients with dementia, pain assessment should also rely on the observation of pain behaviors, such as facial expressions. Ideal observers should be well trained and should observe the patient continuously in order to pick up any pain-indicative behavior; which are requisitions beyond realistic possibilities of pain care. Therefore, the need for video-based pain detection systems has been repeatedly voiced. Such systems would allow for constant monitoring of pain behaviors and thereby allow for a timely adjustment of pain management in these fragile patients, who are often undertreated for pain.
METHODS
In this road map paper we describe an interdisciplinary approach to develop such a video-based pain detection system. The development starts with the selection of appropriate video material of people in pain as well as the development of technical methods to capture their faces. Furthermore, single facial motions are automatically extracted according to an international coding system. Computer algorithms are trained to detect the combination and timing of those motions, which are pain-indicative.
RESULTS/CONCLUSION
We hope to encourage colleagues to join forces and to inform end-users about an imminent solution of a pressing pain-care problem. For the near future, implementation of such systems can be foreseen to monitor immobile patients in intensive and postoperative care situations.
A method for minimum range extension with improved accuracy in triangulation laser range finder
(2011)
Plant sap-feeding insects are widespread, having evolved to occupy diverse environmental niches despite exclusive feeding on an impoverished diet lacking in essential amino acids and vitamins. Success depends exquisitely on their symbiotic relationships with microbial symbionts housed within specialized eukaryotic bacteriocyte cells. Each bacteriocyte is packed with symbionts that are individually surrounded by a host-derived symbiosomal membrane representing the absolute host-symbiont interface. The symbiosomal membrane must be a dynamic and selectively permeable structure to enable bidirectional and differential movement of essential nutrients, metabolites, and biosynthetic intermediates, vital for growth and survival of host and symbiont. However, despite this crucial role, the molecular basis of membrane transport across the symbiosomal membrane remains unresolved in all bacteriocyte-containing insects. A transport protein was immuno-localized to the symbiosomal membrane separating the pea aphid Acyrthosiphon pisum from its intracellular symbiont Buchnera aphidicola. The transporter, A. pisum nonessential amino acid transporter 1, or ApNEAAT1 (gene: ACYPI008971), was characterized functionally following heterologous expression in Xenopus oocytes, and mediates both inward and outward transport of small dipolar amino acids (serine, proline, cysteine, alanine, glycine). Electroneutral ApNEAAT1 transport is driven by amino acid concentration gradients and is not coupled to transmembrane ion gradients. Previous metabolite profiling of hemolymph and bacteriocyte, alongside metabolic pathway analysis in host and symbiont, enable prediction of a physiological role for ApNEAAT1 in bidirectional host-symbiont amino acid transfer, supplying both host and symbiont with indispensable nutrients and biosynthetic precursors to facilitate metabolic complementarity.
The limited sodium availability of freshwater and terrestrial environments was a major physiological challenge during vertebrate evolution. The epithelial sodium channel (ENaC) is present in the apical membrane of sodium-absorbing vertebrate epithelia and evolved as part of a machinery for efficient sodium conservation. ENaC belongs to the degenerin/ENaC protein family and is the only member that opens without an external stimulus. We hypothesized that ENaC evolved from a proton-activated sodium channel present in ionocytes of freshwater vertebrates and therefore investigated whether such ancestral traits are present in ENaC isoforms of the aquatic pipid frog Xenopus laevis. Using whole-cell and single-channel electrophysiology of Xenopus oocytes expressing ENaC isoforms assembled from alpha beta gamma- or delta beta gamma-subunit combinations, we demonstrate that Xenopus delta beta gamma-ENaC is profoundly activated by extracellular acidification within biologically relevant ranges (pH 8.0-6.0). This effect was not observed in Xenopus alpha beta gamma-ENaC or human ENaC orthologs. We show that protons interfere with allosteric ENaC inhibition by extracellular sodium ions, thereby increasing the probability of channel opening. Using homology modeling of ENaC structure and site-directed mutagenesis, we identified a cleft region within the extracellular loop of the delta-subunit that contains several acidic amino acid residues that confer proton-sensitivity and enable allosteric inhibition by extracellular sodium ions. We propose that Xenopus delta beta gamma-ENaC can serve as a model for investigating ENaC transformation from a proton-activated toward a constitutively-active ion channel. Such transformation might have occurred during the evolution of tetrapod vertebrates to enable bulk sodium absorption during the water-to-land transition.
Cholinergic polymodal chemosensory cells in the mammalian urethra (urethral brush cells = UBC) functionally express the canonical bitter and umami taste transduction signaling cascade. Here, we aimed to determine whether UBC are functionally equipped for the perception of salt through ENaC (epithelial sodium channel). Cholinergic UBC were isolated from ChAT-eGFP reporter mice (ChAT = choline acetyltransferase). RT-PCR showed mRNA expression of ENaC subunits Scnn1a, Scnn1b, and Scnn1g in urethral epithelium and isolated UBC. Scnn1a could also be detected by next generation sequencing in 4/6 (66%) single UBC, two of them also expressed the bitter receptor Tas2R108. Strong expression of Scnn1a was seen in some urothelial umbrella cells and in 65% of UBC (30/46 cells) in a Scnn1a reporter mouse strain. Intracellular [Ca2+] was recorded in isolated UBC stimulated with the bitter substance denatonium benzoate (25 mM), ATP (0.5 mM) and NaCl (50 mM, on top of 145 mM Na+ and 153 mM Cl- baseline in buffer); mannitol (150 mM) served as osmolarity control. NaCl, but not mannitol, evoked an increase in intracellular [Ca2+] in 70% of the tested UBC. The NaCl-induced effect was blocked by the ENaC inhibitor amiloride (IC50 = 0.471 mu M). When responses to both NaCl and denatonium were tested, all three possible positive response patterns occurred in a balanced distribution: 42% NaCl only, 33% denatonium only, 25% to both stimuli. A similar reaction pattern was observed with ATP and NaCl as test stimuli. About 22% of the UBC reacted to all three stimuli. Thus, NaCl evokes calcium responses in several UBC, likely involving an amiloride-sensitive channel containing alpha-ENaC. This feature does not define a new subpopulation of UBC, but rather emphasizes their polymodal character. The actual function of alpha-ENaC in cholinergic UBC-salt perception, homeostatic ion transport, mechanoreception-remains to be determined.
Evolutionary conservation of the antimicrobial function of mucus: a first defence against infection
(2018)
Mucus layers often provide a unique and multi-functional hydrogel interface between the epithelial cells of organisms and their external environment. Mucus has exceptional properties including elasticity, changeable rheology and an ability to self-repair by reannealing, and is therefore an ideal medium for trapping and immobilising pathogens and serving as a barrier to microbial infection. The ability to produce a functional surface mucosa was an important evolutionary step, which evolved first in the Cnidaria, which includes corals, and the Ctenophora. This allowed the exclusion of non-commensal microbes and the subsequent development of the mucus-lined digestive cavity seen in higher metazoans. The fundamental architecture of the constituent glycoprotein mucins is also evolutionarily conserved. Although an understanding of the biochemical interactions between bacteria and the mucus layer are important to the goal of developing new antimicrobial strategies, they remain relatively poorly understood. This review summarises the physicochemical properties and evolutionary importance of mucus, which make it so successful in the prevention of bacterial infection. In addition, the strategies developed by bacteria to counteract the mucus layer are also explored.
The epithelial sodium channel (ENaC) is a critical regulator of vertebrate electrolyte homeostasis. ENaC is the only constitutively open ion channel in the degenerin/ENaC protein family, and its expression, membrane abundance, and open probability therefore are tightly controlled. The canonical ENaC is composed of three subunits (, , and ), but a fourth -subunit may replace and form atypical -ENaCs. Using Xenopus laevis as a model, here we found that mRNAs of the - and -subunits are differentially expressed in different tissues and that -ENaC predominantly is present in the urogenital tract. Using whole-cell and single-channel electrophysiology of oocytes expressing Xenopus - or -ENaC, we demonstrate that the presence of the -subunit enhances the amount of current generated by ENaC due to an increased open probability, but also changes current into a transient form. Activity of canonical ENaCs is critically dependent on proteolytic processing of the - and -subunits, and immunoblotting with epitope-tagged ENaC subunits indicated that, unlike -ENaC, the -subunit does not undergo proteolytic maturation by the endogenous protease furin. Furthermore, currents generated by -ENaC were insensitive to activation by extracellular chymotrypsin, and presence of the -subunit prevented cleavage of -ENaC at the cell surface. Our findings suggest that subunit composition constitutes an additional level of ENaC regulation, and we propose that the Xenopus -ENaC subunit represents a functional example that demonstrates the importance of proteolytic maturation during ENaC evolution.
Recently, we discovered a cholinergic mechanism that inhibits the adenosine triphosphate (ATP)-dependent release of interleukin-1 beta (IL-1 beta) by human monocytes via nicotinic acetylcholine receptors (nAChRs) composed of alpha 7, alpha 9 and/or alpha 10 subunits. Furthermore, we identified phosphocholine (PC) and dipalmitoylphosphatidylcholine (DPPC) as novel nicotinic agonists that elicit metabotropic activity at monocytic nAChR. Interestingly, PC does not provoke ion channel responses at conventional nAChRs composed of subunits alpha 9 and alpha 10. The purpose of this study is to determine the composition of nAChRs necessary for nicotinic signaling in monocytic cells and to test the hypothesis that common metabolites of phosphatidylcholines, lysophosphatidylcholine (LPC) and glycerophosphocholine (G-PC), function as nAChR agonists. In peripheral blood mononuclear cells from nAChR gene-deficient mice, we demonstrated that inhibition of ATP-dependent release of IL-1 beta by acetylcholine (ACh), nicotine and PC depends on subunits alpha 7, alpha 9 and alpha 10. Using a panel of nAChR antagonists and siRNA technology, we confirmed the involvement of these subunits in the control of IL-1 beta release in the human monocytic cell line U937. Furthermore, we showed that LPC (C16:0) and G-PC efficiently inhibit ATP-dependent release of IL-1 beta. Of note, the inhibitory effects mediated by LPC and G-PC depend on nAChR subunits alpha 9 and alpha 10, but only to a small degree on alpha 7. In Xenopus laevis oocytes heterologously expressing different combinations of human alpha 7, alpha 9 or alpha 10 subunits, ACh induced canonical ion channel activity, whereas LPC, G-PC and PC did not. In conclusion, we demonstrate that canonical nicotinic agonists and PC elicit metabotropic nAChR activity in monocytes via interaction of nAChR subunits alpha 7, alpha 9 and alpha 10. For the metabotropic signaling of LPC and G-PC, nAChR subunits alpha 9 and alpha 10 are needed, whereas alpha 7 is virtually dispensable. Furthermore, molecules bearing a PC group in general seem to regulate immune functions without perturbing canonical ion channel functions of nAChR.
Hydrogen sulfide stimulates CFTR in Xenopus oocytes by activation of the cAMP/PKA signalling axis
(2017)
Hydrogen sulfide (H2S) has been recognized as a signalling molecule which affects the activity of ion channels and transporters in epithelial cells. The cystic fibrosis transmembrane conductance regulator (CFTR) is an epithelial anion channel and a key regulator of electrolyte and fluid homeostasis. In this study, we investigated the regulation of CFTR by H2S. Human CFTR was heterologously expressed in Xenopus oocytes and its activity was electrophysiologically measured by microelectrode recordings. The H2S-forming sulphur salt Na2S as well as the slow-releasing H2S-liberating compound GYY4137 increased transmembrane currents of CFTR-expressing oocytes. Na2S had no effect on native, noninjected oocytes. The effect of Na2S was blocked by the CFTR inhibitor CFTR_inh172, the adenylyl cyclase inhibitor MDL 12330A, and the protein kinase A antagonist cAMPS-Rp. Na2S potentiated CFTR stimulation by forskolin, but not that by IBMX. Na2S enhanced CFTR stimulation by membranepermeable 8Br-cAMP under inhibition of adenylyl cyclase-mediated cAMP production by MDL 12330A. These data indicate that H2S activates CFTR in Xenopus oocytes by inhibiting phosphodiesterase activity and subsequent stimulation of CFTR by cAMP-dependent protein kinase A. In epithelia, an increased CFTR activity may correspond to a pro-secretory response to H2S which may be endogenously produced by the epithelium or H2S-generating microflora.