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Human long duration exploration missions (LDEMs) raise a number of technological challenges. This paper addresses the question of the crew autonomy: as the distances increase, the communication delays and constraints tend to prevent the astronauts from being monitored and supported by a real time ground control. Eventually, future planetary missions will necessarily require a form of astronaut self-scheduling. We study the usage of a computer decision-support tool by a crew of analog astronauts, during a Mars simulation mission conducted at the Mars Desert Research Station (MDRS, Mars Society) in Utah. The proposed tool, called Romie , belongs to the new category of Robust Advanced Modelling and Scheduling (RAMS) systems. It allows the crew members (i) to visually model their scientific objectives and constraints, (ii) to compute near-optimal operational schedules while taking uncertainty into account, (iii) to monitor the execution of past and current activities, and (iv) to modify scientific objectives/constraints w.r.t. unforeseen events and opportunistic science. In this study, we empirically measure how the astronauts, who are novice planners, perform at using such a tool when self-scheduling under the realistic assumptions of a simulated Martian planetary habitat. [ABSTRACT FROM AUTHOR]

We introduce "4E," a new design approach of reconfigurable displays that can change their form factors by capitalizing on four quality properties inspired by applied material: extensibility, extendability, expandability, and extractability. This approach is applicable to both fixed and portable displays. We define and exemplify each property, highlighting the key differences in how reconfigurable displays can change their form factors to accommodate more screen real estate for more users, applications, and functionality. To demonstrate the 4E approach, we conduct a targeted literature review and introduce E3Screen, a prototype that enhances any flat screen (e.g., of a tablet, laptop, monitor) with two slidable, rotatable, and foldable lateral displays. We report results from a controlled experiment with N = 103 participants, conducted to collect, analyze, and understand end users' preferences for display configurations permitted by the extendability, expandability, and extractability of E3Screen. Our results structure future research and development in reconfigurable displays and multi-display collaborative workspaces. [ABSTRACT FROM AUTHOR]

Adapting the user interface (UI) to the changing context of use is intended to support the interaction effectiveness and sustain UI usability. However, designing and/or processing UIs adaptation at design time does not encompass real situation requirements. Adaptation should have a cross-cutting and low-cost impact on software patterning and appearance with regard to the situation and the ambient-context. To meet this requirement, we present TADAP proposal for run-time adaptive and adaptable UI based user feedbacks and machine learning. It allows a task-driven adaptation of the user interface (UI) at runtime by mixed-initiative. The particularity of TADAP is the utilization of Machine Learning potential to support context-aware runtime adaptation within model-driven UI. Further, TADAP allows the UI adaptation by mixed-initiative (User and System) considering the user preferences (implicit and explicit) during an interaction. Such a mixed-initiative runtime UI-adaptation tool provides recommendations on how to personalize the UI. Further, it has the ability to track real-time users' interventions and learn their preferences. Diverse tests were performed and showed TADAP as a promising initiative for intelligent model-driven UI adaptation. [ABSTRACT FROM AUTHOR]

Browsing multimedia objects, such as photos, videos, documents, and maps represents a frequent activity in a context of use where an end-user interacts on a large vertical display close to bystanders, such as a meeting in a corporate environment or a family display at home. In these contexts, mid-air gesture interaction is suitable for a large variety of end-users, provided that gestures are consistently mapped to similar functions across media types. We present Lui (Large User Interface), a ready-to-deploy and to-use application for browsing multimedia objects by consistent mid-air gesture interaction on a large display that is customizable by mapping new gesture classes to functions in real-time. The method followed to design the gesture interaction and to develop the application consists of four stages: (1) a contextual gesture elicitation study (23 participants × 18 referents = 414 proposed gestures) is conducted with the various media types to determine a consensus set satisfying consistency, (2) the continuous integration of this consensus set with gesture recognizers into a pipeline software architecture, (3) a comparative testing of these recognizers on the consensus set to configure the pipeline with the most efficient ones, and (4) an evaluation of the interface regarding its global quality and specific to the implemented gestures. [ABSTRACT FROM AUTHOR]

This article aims to investigate user preferences for extra-user interfaces (extra-UI), formerly known as meta-user interfaces. These are special user interfaces that allow the user to control or personalize the application's user interface. They are named “extra-user interfaces” because the application does not need it to work. Their main goal is the support of systems with several contexts, displays, devices, or platforms. The purposes and features offered by all the Extra-UI vary from one application to another. To create coherence between them, we defined a catalogue of 14 distribution primitives that are typically provided and classified into 4 categories: simple primitives, basic primitives, advanced primitives, and management operations. Based on this catalogue, a comparative analysis of the state of the art was conducted to identify which interaction styles have been properly used and to discuss the rationale behind these usages. From this analysis, we set up and conducted a comparative evaluation of user preferences by 14 participants testing 6 selected distribution primitives in 4 different interaction styles. The research outcomes exemplified that there were significant differences on user preferences between interaction styles with regards to experience level and primitive type. [ABSTRACT FROM AUTHOR]

Abstract: This work aims at evaluating a graphical notation for modelling software (and other kinds of) development methodologies, thus demonstrating how useful the graphical aspects can be for sharing knowledge between the people responsible for documenting information and those responsible for understanding and putting it into practice. We acknowledge the importance of having a common set of symbols that can be used to create, use and disseminate information for a larger audience than is possible today with a variety of alternatives and lack of a common ground. Using a cognitive dimensions framework, we make a standard evaluation of the elements and diagrams of the notation proposed to support the ISO/IEC 24744 methodology metamodel standard, considering the trade-offs between different dimensions. We suggest improvements to this existing notation based on this analysis, in the context of improving communication between creators and users of methodologies. [Copyright &y& Elsevier]

The expansion of touch-sensitive technologies, ranging from smartwatches to wall screens, triggered a wider use of gesture-based user interfaces and encouraged researchers to invent recognizers that are fast and accurate for end-users while being simple enough for practitioners. Since the pioneeringwork on two-dimensional (2D) stroke gesture recognition based on feature extraction and classification, numerous approaches and techniques have been introduced to classify uni- and multi-stroke gestures, satisfying various properties of articulation-, rotation-, scale-, and translation-invariance. As the domain abounds in different recognizers, it becomes difficult for the practitioner to choose the right recognizer, depending on the application and for the researcher to understand the state-of-the-art. To address these needs, a targeted literature review identified 16 significant 2D stroke gesture recognizers that were submitted to a descriptive analysis discussing their algorithm, performance, and properties, and a comparative analysis discussing their similarities and differences. Finally, some opportunities for expanding 2D stroke gesture recognition are drawn from these analyses. [ABSTRACT FROM AUTHOR]

Although many methods currently exist to evaluate a user interface, they have been mainly developed and applied for graphical and vocal user interfaces, leaving aside other modalities such as gesture interaction. As a consequence, the evaluation of the global quality of a gesture user interface most often resorts to these methods, which take little or no explicit and specific account of the gesture modality or which adapt existing methods in a way that jeopardizes their validity. To remedy this situation, this paper introduces, defines, and justifies a method for evaluating explicitly and specifically a gesture user interface based on a conceptual model that consists of: (1) six quantitative measures covering user and system aspects, such as gesture thinking time and recognition rate; (2) three new qualitative scales, i.e., discoverability, learnability, and social acceptability, combined with seven scales in a gesture evaluation scheme, which is formally based on four measures, i.e., subscale score, subscale importance, scale mean score and scale mean importance; (3) a debriefing interview to cross-analyze the results of the quantitative measures and qualitative scales. This method also includes the use of multiple sessions to take into account short and long-term memory, as well as discoverability, and integrates tests specific to each gesture, as well as concrete use cases. For validation and illustration purposes, this method is applied to a gesture user interface serving as a case study. The results are then analyzed, starting with classic methods and gradually adding the particularities of our method to highlight its added value. Based on this experiment, we suggest some implications for evaluating gesture interfaces and for incorporating these scales into UEQ+, a modular user interface evaluation method. • A new evaluation method specific to gesture-based user interfaces. • The combination of quantitative measures, qualitative scales, and interviews. • The use of a multi-session method to take into account short and long-term memory. • The use of tests specific to each gesture and concrete use cases. • The introduction of new scales (discoverability, learnability, social acceptability). • An application of this evaluation method to a case study. • Insights gained from this experience for evaluating gesture user interface. [ABSTRACT FROM AUTHOR]

Long bone fractures are a concern in long-duration exploration missions (LDEM) where crew autonomy will exceed the current Low Earth Orbit paradigm. Current crew selection assumptions require extensive complete training and competency testing prior to flight for off-nominal situations. Analogue astronauts (n = 6) can be quickly trained to address a single fracture pattern and then competently perform the repair procedure. An easy-to-use external fixation (EZExFix) was employed to repair artificial tibial shaft fractures during an inhabited mission at the Mars Desert Research Station (Utah, USA). Bone repair safety zones were respected (23/24), participants achieved 79.2% repair success, and median completion time was 50.04 min. Just-in-time training in-mission was sufficient to become autonomous without pre-mission medical/surgical/mechanical education, regardless of learning conditions (p > 0.05). Similar techniques could be used in LDEM to increase astronauts' autonomy in traumatic injury treatment and lower skill competency requirements used in crew selection. [ABSTRACT FROM AUTHOR]

Long bone fractures in hostile environments pose unique challenges due to limited resources, restricted access to healthcare facilities, and absence of surgical expertise. While external fixation has shown promise, the availability of trained surgeons is limited, and the procedure may frighten unexperienced personnel. Therefore, an easy-to-use external fixator (EZExFix) that can be performed by nonsurgeon individuals could provide timely and life-saving treatment in hostile environments; however, its efficacy and accuracy remain to be demonstrated. This study tested the learning curve and surgical performance of nonsurgeon analog astronauts (n = 6) in managing tibial shaft fractures by the EZExFix during a simulated Mars inhabited mission, at the Mars Desert Research Station (Hanksville, UT, USA). The reduction was achievable in the different 3D axis, although rotational reductions were more challenging. Astronauts reached similar bone-to-bone contact compared to the surgical control, indicating potential for successful fracture healing. The learning curve was not significant within the limited timeframe of the study (N = 4 surgeries lasting <1 h), but the performance was similar to surgical control. The results of this study could have important implications for fracture treatment in challenging or hostile conditions on Earth, such as war or natural disaster zones, developing countries, or settings with limited resources. [ABSTRACT FROM AUTHOR]

Arm-and-hand tracking by technological means allows gathering data that can be elaborated for determining gesture meaning. To this aim, machine learning (ML) algorithms have been mostly investigated looking for a balance between the highest recognition rate and the lowest recognition time. However, this balance comes mainly from statistical models, which are challenging to interpret. In contrast, we present $\mu C^{1}$ and $\mu C^{2}$ , two geometric model-based approaches to gesture recognition which support the visualization and geometrical interpretation of the recognition process. We compare $\mu C^{1}$ and $\mu C^{2}$ with respect to two classical ML algorithms, k-nearest neighbor (k-NN) and support vector machine (SVM), and two state-of-the-art (SotA) deep learning (DL) models, bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU), on an experimental dataset of ten gesture classes from the Italian Sign Language (LIS), each repeated 100 times by five inexperienced non-native signers, and gathered with wearable technology (a sensory glove and inertial measurement units). As a result, we achieve a compromise between high recognition rates ($>90\%$) and low recognition times ($ < 0.1 {\mathrm{ s}}$) that is adequate for human–computer interaction. Moreover, we elaborate on the algorithms’ geometric interpretation based on geometric algebra, which supports some understanding of the recognition process. [ABSTRACT FROM AUTHOR]

Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed. [ABSTRACT FROM AUTHOR]

Abstract: Diagrams are often used to model complex systems: in many cases several different types of diagrams are used to model different aspects of the system. These diagrams, perhaps from multiple stakeholders of different specialties, must be combined to achieve a full abstract representation of the system. Many CAD tools offer multi-diagram integration; however, sketch-based diagramming tools are yet to tackle this difficult integration problem. We extend the diagram sketching tool InkKit to combine software engineering sketches of different types. Our extensions support software design processes by providing a sketch-based approach that allows the iterative creation of multiple outputs interacting with one another from the inter-linked models. We demonstrate that InkKit can generate a functional system consisting of a user interface with processes to submit and retrieve data from a database from sketched user interfaces designs and sketched entity relationship diagrams. [Copyright &y& Elsevier]

Presently, miniaturized sensors can be embedded in any small-size wearable to recognize movements on some parts of the human body. For example, an electrooculography-based sensor in smart glasses recognizes finger movements on the nose. To explore the interaction capabilities, this paper conducts a gesture elicitation study as a between-subjects experiment involving one group of 12 females and one group of 12 males, expressing their preferred nose-based gestures on 19 Internet-of-Things tasks. Based on classification criteria, the 912 elicited gestures are clustered into 53 unique gestures resulting in 23 categories, to form a taxonomy and a consensus set of 38 final gestures, providing researchers and practitioners with a larger base with six design guidelines. To test whether the measurement method impacts these results, the agreement scores and rates, computed for determining the most agreed gestures upon participants, are compared with the Condorcet and the de Borda count methods to observe that the results remain consistent, sometimes with a slightly different order. To test whether the results are sensitive to gender, inferential statistics suggest that no significant difference exists between males and females for agreement scores and rates. [ABSTRACT FROM AUTHOR]

A gesture elicitation study consists of a popular method for eliciting a sample of end end users to propose gestures for executing functions in a certain context of use, specified by its users and their functions, the device or the platform used, and the physical environment in which they are working. Gestures proposed in such a study needs to be classified and, perhaps, extended in order to feed a gesture recognizer. To support this process, we conducted a full-body gesture elicitation study for executing functions in a smart home environment by domestic end users in front of a camera. Instead of defining functions opportunistically, we define them based on a taxonomy of abstract tasks. From these elicited gestures, a XML-compliant grammar for specifying resulting gestures is defined, created, and implemented to graphically represent, label, characterize, and formally present such full-body gestures. The formal notation for specifying such gestures is also useful to generate variations of elicited gestures to be applied on-the-fly on gestures in order to allow one-shot learning. [ABSTRACT FROM AUTHOR]

Abstract: A methodology is provided here to assist in the design of secure interactive applications. In particular, this methodology helps design an adequate security information feedback based on User Interface Patterns, the resulting feedback is then evaluated against a set of design/evaluation criteria called Human–Computer Interaction for Security (HCI-S). In case of a security issue the security information feedback is generally presented using the visual and auditory channels required to achieve an effective notifications, and it is explicitly specified in the design of user interfaces for secure web system. [Copyright &y& Elsevier]

This paper describes a framework that serves as a reference for classifying user interfaces supporting multiple targets, or multiple contexts of use in the field of context-aware computing. In this framework, a context of use is decomposed into three facets: the end users of the interactive system, the hardware and software computing platform with which the users have to carry out their interactive tasks and the physical environment where they are working. Therefore, a context-sensitive user interface is a user interface that exhibits some capability to be aware of the context (context awareness) and to react to changes of this context. This paper attempts to provide a unified understanding of context-sensitive user interfaces rather than a prescription of various ways or methods of tackling different steps of development. Rather, the framework structures the development life cycle into four levels of abstraction: task and concepts, abstract user interface, concrete user interface and final user interface. These levels are structured with a relationship of reification going from an abstract level to a concrete one and a relationship of abstraction going from a concrete level to an abstract one. Most methods and tools can be more clearly understood and compared relative to each other against the levels of this framework. In addition, the framework expresses when, where and how a change of context is considered and supported in the context-sensitive user interface thanks to a relationship of translation. In the field of multi-target user interfaces is also introduced, defined, and exemplified the notion of plastic user interfaces. These user interfaces support some adaptation to changes of the context of use while preserving a predefined set of usability properties. [Copyright &y& Elsevier]