TY - JOUR A1 - Döllinger, Nina A1 - Wienrich, Carolin A1 - Latoschik, Marc Erich T1 - Challenges and opportunities of immersive technologies for mindfulness meditation: a systematic review JF - Frontiers in Virtual Reality N2 - Mindfulness is considered an important factor of an individual's subjective well-being. Consequently, Human-Computer Interaction (HCI) has investigated approaches that strengthen mindfulness, i.e., by inventing multimedia technologies to support mindfulness meditation. These approaches often use smartphones, tablets, or consumer-grade desktop systems to allow everyday usage in users' private lives or in the scope of organized therapies. Virtual, Augmented, and Mixed Reality (VR, AR, MR; in short: XR) significantly extend the design space for such approaches. XR covers a wide range of potential sensory stimulation, perceptive and cognitive manipulations, content presentation, interaction, and agency. These facilities are linked to typical XR-specific perceptions that are conceptually closely related to mindfulness research, such as (virtual) presence and (virtual) embodiment. However, a successful exploitation of XR that strengthens mindfulness requires a systematic analysis of the potential interrelation and influencing mechanisms between XR technology, its properties, factors, and phenomena and existing models and theories of the construct of mindfulness. This article reports such a systematic analysis of XR-related research from HCI and life sciences to determine the extent to which existing research frameworks on HCI and mindfulness can be applied to XR technologies, the potential of XR technologies to support mindfulness, and open research gaps. Fifty papers of ACM Digital Library and National Institutes of Health's National Library of Medicine (PubMed) with and without empirical efficacy evaluation were included in our analysis. The results reveal that at the current time, empirical research on XR-based mindfulness support mainly focuses on therapy and therapeutic outcomes. Furthermore, most of the currently investigated XR-supported mindfulness interactions are limited to vocally guided meditations within nature-inspired virtual environments. While an analysis of empirical research on those systems did not reveal differences in mindfulness compared to non-mediated mindfulness practices, various design proposals illustrate that XR has the potential to provide interactive and body-based innovations for mindfulness practice. We propose a structured approach for future work to specify and further explore the potential of XR as mindfulness-support. The resulting framework provides design guidelines for XR-based mindfulness support based on the elements and psychological mechanisms of XR interactions. KW - virtual reality KW - augmented reality KW - mindfulness KW - XR KW - meditation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259047 VL - 2 ER - TY - JOUR A1 - Prakash, Subash A1 - Unnikrishnan, Vishnu A1 - Pryss, Rüdiger A1 - Kraft, Robin A1 - Schobel, Johannes A1 - Hannemann, Ronny A1 - Langguth, Berthold A1 - Schlee, Winfried A1 - Spiliopoulou, Myra T1 - Interactive system for similarity-based inspection and assessment of the well-being of mHealth users JF - Entropy N2 - Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported. KW - medical analytics KW - condition prediction KW - ecological momentary assessment KW - visual analytics KW - time series Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252333 SN - 1099-4300 VL - 23 IS - 12 ER - TY - JOUR A1 - Kraft, Robin A1 - Birk, Ferdinand A1 - Reichert, Manfred A1 - Deshpande, Aniruddha A1 - Schlee, Winfried A1 - Langguth, Berthold A1 - Baumeister, Harald A1 - Probst, Thomas A1 - Spiliopoulou, Myra A1 - Pryss, Rüdiger T1 - Efficient processing of geospatial mHealth data using a scalable crowdsensing platform JF - Sensors N2 - Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case. KW - mHealth KW - crowdsensing KW - tinnitus KW - geospatial data KW - cloud-native KW - stream processing KW - scalability KW - architectural design Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-207826 SN - 1424-8220 VL - 20 IS - 12 ER - TY - JOUR A1 - Klemz, Boris A1 - Rote, Günter T1 - Linear-Time Algorithms for Maximum-Weight Induced Matchings and Minimum Chain Covers in Convex Bipartite Graphs JF - Algorithmica N2 - A bipartite graph G=(U,V,E) is convex if the vertices in V can be linearly ordered such that for each vertex u∈U, the neighbors of u are consecutive in the ordering of V. An induced matching H of G is a matching for which no edge of E connects endpoints of two different edges of H. We show that in a convex bipartite graph with n vertices and m weighted edges, an induced matching of maximum total weight can be computed in O(n+m) time. An unweighted convex bipartite graph has a representation of size O(n) that records for each vertex u∈U the first and last neighbor in the ordering of V. Given such a compact representation, we compute an induced matching of maximum cardinality in O(n) time. In convex bipartite graphs, maximum-cardinality induced matchings are dual to minimum chain covers. A chain cover is a covering of the edge set by chain subgraphs, that is, subgraphs that do not contain induced matchings of more than one edge. Given a compact representation, we compute a representation of a minimum chain cover in O(n) time. If no compact representation is given, the cover can be computed in O(n+m) time. All of our algorithms achieve optimal linear running time for the respective problem and model, and they improve and generalize the previous results in several ways: The best algorithms for the unweighted problem versions had a running time of O(n\(^{2}\)) (Brandstädt et al. in Theor. Comput. Sci. 381(1–3):260–265, 2007. https://doi.org/10.1016/j.tcs.2007.04.006). The weighted case has not been considered before. KW - dynamic programming KW - graph algorithm KW - induced matching KW - chain cover KW - convex bipartite graph KW - certifying algorithm Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267876 SN - 1432-0541 VL - 84 IS - 4 ER - TY - JOUR A1 - Davidson, Padraig A1 - Düking, Peter A1 - Zinner, Christoph A1 - Sperlich, Billy A1 - Hotho, Andreas T1 - Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study JF - Sensors N2 - The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions. KW - artificial intelligence KW - endurance KW - exercise intensity KW - precision training KW - prediction KW - wearable Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205686 SN - 1424-8220 VL - 20 IS - 9 ER - TY - RPRT ED - Hoßfeld, Tobias ED - Wunderer, Stefan T1 - White Paper on Crowdsourced Network and QoE Measurements – Definitions, Use Cases and Challenges N2 - The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic ‘crowdsourcing’. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the Würzburg seminar on “Crowdsourced Network and QoE Measurements” which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms “crowdsourced network measurements” and “crowdsourced QoE measurements”, describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is – to our knowledge – the first one covering the topic of crowdsourced network and QoE measurements. KW - Crowdsourcing KW - Network Measurements KW - Quality of Service (QoS) KW - Quality of Experience (QoE) KW - crowdsourced network measurements KW - crowdsourced QoE measurements Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-202327 ER - TY - JOUR A1 - Pawellek, Ruben A1 - Krmar, Jovana A1 - Leistner, Adrian A1 - Djajić, Nevena A1 - Otašević, Biljana A1 - Protić, Ana A1 - Holzgrabe, Ulrike T1 - Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach JF - Journal of Cheminformatics N2 - The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response independent of the analytes' chemical structure. This study was aimed at accurately predicting the CAD response of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental conditions and molecular descriptors, a mixed quantitative structure-property relationship (QSPR) modeling was performed using Gradient Boosted Trees (GBT). The ensemble of 10 decisions trees (learning rate set at 0.55, the maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99% (Q\(^2\): 0.987, RMSE: 0.051) of the observed variance in CAD responses. Validation using an external test compound confirmed the high predictive ability of the model established (R-2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy, GBT used almost all independent variables during model building. Finally, it attributed the highest importance to the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic solvent in the mobile phase and the molecular descriptors such as molecular weight (MW), Radial Distribution Function-080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful acetonitrile. KW - High-performance liquid chromatography (HPLC) KW - Charged aerosol detector (CAD) KW - Gradient boosted trees (GBT) KW - Quantitative structure-property relationship modeling (QSPR) KW - Fatty acids Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261618 VL - 13 IS - 1 ER - TY - JOUR A1 - Unruh, Fabian A1 - Landeck, Maximilian A1 - Oberdörfer, Sebastian A1 - Lugrin, Jean-Luc A1 - Latoschik, Marc Erich T1 - The Influence of Avatar Embodiment on Time Perception - Towards VR for Time-Based Therapy JF - Frontiers in Virtual Reality N2 - Psycho-pathological conditions, such as depression or schizophrenia, are often accompanied by a distorted perception of time. People suffering from this conditions often report that the passage of time slows down considerably and that they are “stuck in time.” Virtual Reality (VR) could potentially help to diagnose and maybe treat such mental conditions. However, the conditions in which a VR simulation could correctly diagnose a time perception deviation are still unknown. In this paper, we present an experiment investigating the difference in time experience with and without a virtual body in VR, also known as avatar. The process of substituting a person’s body with a virtual body is called avatar embodiment. Numerous studies demonstrated interesting perceptual, emotional, behavioral, and psychological effects caused by avatar embodiment. However, the relations between time perception and avatar embodiment are still unclear. Whether or not the presence or absence of an avatar is already influencing time perception is still open to question. Therefore, we conducted a between-subjects design with and without avatar embodiment as well as a real condition (avatar vs. no-avatar vs. real). A group of 105 healthy subjects had to wait for seven and a half minutes in a room without any distractors (e.g., no window, magazine, people, decoration) or time indicators (e.g., clocks, sunlight). The virtual environment replicates the real physical environment. Participants were unaware that they will be asked to estimate their waiting time duration as well as describing their experience of the passage of time at a later stage. Our main finding shows that the presence of an avatar is leading to a significantly faster perceived passage of time. It seems to be promising to integrate avatar embodiment in future VR time-based therapy applications as they potentially could modulate a user’s perception of the passage of time. We also found no significant difference in time perception between the real and the VR conditions (avatar, no-avatar), but further research is needed to better understand this outcome. KW - virtual reality KW - time perception KW - avatar embodiment KW - immersion KW - human computer interaction (HCI) Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259076 VL - 2 ER - TY - THES A1 - Huber, Stephan T1 - Proxemo: Documenting Observed Emotions in HCI T1 - Proxemo: Die Dokumentation Beobachteter Emotionen in der Mensch-Computer-Interaktion N2 - For formative evaluations of user experience (UX) a variety of methods have been developed over the years. However, most techniques require the users to interact with the study as a secondary task. This active involvement in the evaluation is not inclusive of all users and potentially biases the experience currently being studied. Yet there is a lack of methods for situations in which the user has no spare cognitive resources. This condition occurs when 1) users' cognitive abilities are impaired (e.g., people with dementia) or 2) users are confronted with very demanding tasks (e.g., air traffic controllers). In this work we focus on emotions as a key component of UX and propose the new structured observation method Proxemo for formative UX evaluations. Proxemo allows qualified observers to document users' emotions by proxy in real time and then directly link them to triggers. Technically this is achieved by synchronising the timestamps of emotions documented by observers with a video recording of the interaction. In order to facilitate the documentation of observed emotions in highly diverse contexts we conceptualise and implement two separate versions of a documentation aid named Proxemo App. For formative UX evaluations of technology-supported reminiscence sessions with people with dementia, we create a smartwatch app to discreetly document emotions from the categories anger, general alertness, pleasure, wistfulness and pride. For formative UX evaluations of prototypical user interfaces with air traffic controllers we create a smartphone app to efficiently document emotions from the categories anger, boredom, surprise, stress and pride. Descriptive case studies in both application domains indicate the feasibility and utility of the method Proxemo and the appropriateness of the respectively adapted design of the Proxemo App. The third part of this work is a series of meta-evaluation studies to determine quality criteria of Proxemo. We evaluate Proxemo regarding its reliability, validity, thoroughness and effectiveness, and compare Proxemo's efficiency and the observers' experience to documentation with pen and paper. Proxemo is reliable, as well as more efficient, thorough and effective than handwritten notes and provides a better UX to observers. Proxemo compares well with existing methods where benchmarks are available. With Proxemo we contribute a validated structured observation method that has shown to meet requirements formative UX evaluations in the extreme contexts of users with cognitive impairments or high task demands. Proxemo is agnostic regarding researchers' theoretical approaches and unites reductionist and holistic perspectives within one method. Future work should explore the applicability of Proxemo for further domains and extend the list of audited quality criteria to include, for instance, downstream utility. With respect to basic research we strive to better understand the sources leading observers to empathic judgments and propose reminisce and older adults as model environment for investigating mixed emotions. N2 - Für formative Evaluationen der User Experience (UX) wurden im Laufe der Jahre zahlreiche Methoden entwickelt. Die meisten Methoden erfordern jedoch, dass die Benutzer als Nebenaufgabe mit der Studie interagieren. Diese aktive Beteiligung an der Evaluation kann das untersuchte Erlebnis verfälschen und schließt Benutzer komplett aus, die keine kognitiven Ressourcen zur Verfügung haben. Dies ist der Fall, wenn 1) die kognitiven Fähigkeiten der Benutzer beeinträchtigt sind (z. B. Menschen mit Demenz) oder 2) Benutzer mit sehr anspruchsvollen Aufgaben konfrontiert sind (z. B. Fluglotsen). In dieser Arbeit konzentrieren wir uns auf Emotionen als eine Schlüsselkomponente von UX und schlagen die neue strukturierte Beobachtungsmethode Proxemo für formative UX-Evaluationen vor. Proxemo ermöglicht es qualifizierten Beobachtern, die Emotionen der Nutzer in Echtzeit zu dokumentieren und sie direkt mit Auslösern zu verknüpfen. Technisch wird dies erreicht, indem die Zeitstempel der von den Beobachtern dokumentierten Emotionen mit einer Videoaufzeichnung der Interaktion synchronisiert werden. Um die Dokumentation von beobachteten Emotionen in sehr unterschiedlichen Kontexten zu erleichtern, konzipieren und implementieren wir zwei verschiedene Versionen einer Dokumentationshilfe namens Proxemo App. Für formative UX-Evaluationen von technologiegestützten Erinnerungssitzungen mit Menschen mit Demenz erstellen wir eine Smartwatch-App zur unauffälligen Dokumentation von Emotionen aus den Kategorien Ärger, allgemeine Wachsamkeit, Freude, Wehmut und Stolz. Für formative UX-Evaluationen prototypischer Nutzerschnittstellen mit Fluglotsen erstellen wir eine Smartphone-App zur effizienten Dokumentation von Emotionen aus den Kategorien Ärger, Langeweile, Überraschung, Stress und Stolz. Deskriptive Fallstudien in beiden Anwendungsfeldern zeigen die Machbarkeit und den Nutzen der Methode Proxemo und die Angemessenheit des jeweiligen Designs der Proxemo App. Der dritte Teil dieser Arbeit besteht aus einer Reihe von Meta-Evaluationsstudien zu den Gütekriterien von Proxemo. Wir evaluieren Proxemo hinsichtlich der Reliabilität, Validität, Gründlichkeit und Effektivität, und vergleichen die Effizienz von Proxemo und die UX der Beobachter mit der Dokumentation mit Stift und Papier. Proxemo ist reliabel, sowie effizienter, gründlicher und effektiver als handschriftliche Notizen und bietet den Beobachtern eine bessere UX. Proxemo schneidet gut ab im Vergleich zu bestehenden Methoden, für die Benchmarks verfügbar sind. Mit Proxemo stellen wir eine validierte, strukturierte Beobachtungsmethode vor, die nachweislich den Anforderungen formativer UX Evaluationen in den extremen Kontexten von Benutzern mit kognitiven Beeinträchtigungen oder hohen Aufgabenanforderungen gerecht wird. Proxemo ist agnostisch bezüglich der theoretischen Ansätze von Forschenden und vereint reduktionistische und ganzheitliche Perspektiven in einer Methode. Zukünftige Arbeiten sollten die Anwendbarkeit von Proxemo für weitere Domänen erkunden und die Liste der geprüften Gütekriterien erweitern, zum Beispiel um das Kriterium Downstream Utility. In Bezug auf die Grundlagenforschung werden wir versuchen, die Quellen besser zu verstehen, auf denen die empathischen Urteile der Beobachter fußen und schlagen Erinnerungen und ältere Erwachsene als Modellumgebung für die künftige Erforschung gemischter Emotionen vor. KW - Gefühl KW - Wissenschaftliche Beobachtung KW - Methode KW - Benutzererlebnis KW - Benutzerforschung KW - Emotionserkennung KW - Emotion inference KW - Emotionsinterpretation Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-305730 ER - TY - THES A1 - Geißler, Stefan T1 - Performance Evaluation of Next-Generation Data Plane Architectures and their Components T1 - Leistungsbewertung von Data Plane Architekturen der Nächsten Generation sowie ihrer Einzelkomponenten N2 - In this doctoral thesis we cover the performance evaluation of next generation data plane architectures, comprised of complex software as well as programmable hardware components that allow fine granular configuration. In the scope of the thesis we propose mechanisms to monitor the performance of singular components and model key performance indicators of software based packet processing solutions. We present novel approaches towards network abstraction that allow the integration of heterogeneous data plane technologies into a singular network while maintaining total transparency between control and data plane. Finally, we investigate a full, complex system consisting of multiple software-based solutions and perform a detailed performance analysis. We employ simulative approaches to investigate overload control mechanisms that allow efficient operation under adversary conditions. The contributions of this work build the foundation for future research in the areas of network softwarization and network function virtualization. N2 - Diese Doktorarbeit behandelt die Leistungsbewertung von Data Plane Architekturen der nächsten Generation, die aus komplexen Softwarelösungen sowie programmierbaren Hardwarekomponenten bestehen. Hierbei werden Mechanismen entwickelt, die es ermöglichen, die Leistungsfähigkeit einzelner Komponenten zu messen und zentrale Leistungsindikatoren softwarebasierter Systeme zur Verarbeitung von Datenpaketen zu modellieren. Es werden neuartige Ansätze zur Netzabstraktion entworfen, die eine vollständig transparente Integration heterogener Technologien im selben Netz ermöglichen. Schließlich wird eine umfassende Leistungsbewertung eines komplexen Systems, das aus einer Vielzahl softwarebasierter Netzfunktionen besteht, durchgeführt. Anhand simulativer Modelle werden Überlastkontrollmechanismen entwickelt, die es dem System erlauben auch unter Überlast effizient zu arbeiten. Die Beiträge dieser Arbeit bilden die Grundlage weiterer Forschungen im Bereich der Softwarisierung von Netzen sowie der Virtualisierung von Netzfunktionen. T3 - Würzburger Beiträge zur Leistungsbewertung Verteilter Systeme - 02/21 KW - Leistungsbewertung KW - Simulation KW - Zeitdiskretes System KW - Implementierung KW - performance evaluation KW - simulation KW - discrete-time analysis KW - network softwarization KW - mobile networks Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-260157 SN - 1432-8801 ER -