Search

Radar sensing can penetrate non-conducting materials, such as glass, wood, and plastic, which makes it appropriate for recognizing gestures in environments with poor visibility, limited accessibility, and privacy sensitivity. While the performance of radar-based gesture recognition in these environments has been extensively researched, the preferences that users express for these gestures are less known. To analyze such gestures simultaneously according to their user preference and their system recognition performance, we conducted three gesture elicitation studies each with $n_{1}{=}30$ participants to identify user-defined, radar-based gestures sensed through three distinct materials: the glass of a shop window, the wood of an office door, and polyvinyl chloride in an emergency. On this basis, we created a new dataset of nine selected gesture classes for $n_{2}{=}20$ participants repeating twice the same gesture captured by radar through three materials, i.e., glass, wood, and polyvinyl chloride. To uniformly compare recognition rates in these conditions with sensing variations, a specifically tailored procedure was defined and conducted with one-shot radar calibration to train and evaluate a gesture recognizer. ‘Wood’ achieved the best recognition rate (96.44%), followed by ‘Polyvinyl chloride’ and ‘Glass’. We perform a preference-performance analysis of the gestures by combining the agreement rate from the elicitation studies and the recognition rate from the evaluation.

Abstract 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.

Cloud computing is being adopted by commercial and governmental organizations driven by the need to reduce the operational cost of their information technology resources and search for a scalable and flexible way to provide and release their software services. In this computing model, the Quality of Services (QoS) is agreed between service providers and their customers through Service Level Agreements (SLA). There is thus a need for systematic approaches with which to assess the quality of cloud services and their compliance with the SLA. In previous work, we introduced a generic method for Monitoring cloud Services using models at RunTime (MoS@RT), which allows the monitoring requirements or the metric operationalizations of these requirements to be changed at runtime without the modification of the underlying infrastructure. In this paper, we present the design of a monitoring infrastructure that supports the proposed method with its instantiation to a specific platform and reports the results of an experiment carried out to evaluate the perceived efficacy of 58 undergraduate students when using the infrastructure to configure the monitoring of cloud services deployed on the Microsoft Azure platform. The results show that the participants perceived MoS@RT to be easy to use, useful, and they also expressed their intention to use the method in the future. Although further experiments must be carried out to strengthen these results, MoS@RT has proved to be a promising monitoring method for cloud services.

Radar sensing technologies now offer new opportunities for gesturally interacting with a smart environment by capturing microgestures via a chip that is embedded in a wearable device, such as a smartwatch, a finger or a ring. Such microgestures are issued at a very small distance from the device, regardless of whether they are contact-based, such as on the skin, or contactless. As this category of microgestures remains largely unexplored, this paper reports the results of a gesture elicitation study that was conducted with twenty-five participants who expressed their preferred user-defined gestures for interacting with a radar-based sensor on nineteen referents that represented frequent Internet-of-things tasks. This study clustered the $25 \times 19=475$ initially elicited gestures into four categories of microgestures, namely, micro, motion, combined, and hybrid, and thirty-one classes of distinct gesture types and produced a consensus set of the nineteen most preferred microgestures. In a confirmatory study, twenty new participants selected gestures from this classification for thirty referents that represented tasks of various orders; they reached a high rate of agreement and did not identify any new gestures. This classification of radar-based gestures provides researchers and practitioners with a larger basis for exploring gestural interactions with radar-based sensors, such as for hand gesture recognition.

In recent years, the software applications for medical assistance, including the tele-rehabilitation, have known a high and a continuous presence in the medical area. The ePHoRt is a Web-based platform for the remote home monitoring rehabilitation exercises in patients after hip replacement surgery. It involves a learning phase and a serious game scheme for the execution and evaluation of the exercises as part of a therapeutic program. Modular software architecture is proposed, under the patient perspective, to be used as a reference model for researchers or professionals who wish to carry out tele-rehabilitation platforms, and to guarantee security, flexibility, and scalability. The architecture incorporates two main components. The first one manages the patient' therapeutic programs taking into account two principles: 1) maintain loose coupling between the layers of the framework and 2) Don't Repeat Yourself (DRY). The second one evaluates the performed exercises in real time considering an independent acquisition mechanism for the patient movements and two artificial algorithms. The first algorithm allows evaluating the quality of the movements, while the second one allows assessing the levels of pain intensity by recognizing the patient' emotions when performing the movements. Details of the components and the meta-model of the architecture are presented and discussed considering their advantages and disadvantages.

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.

Recentemente, o tema acesso universal tem atraído a atenção da comunidade, principalmente do ponto de vista dos métodos de desenvolvimento de software. Tal interesse se deve ao fato de que o projeto de interfaces usuário-computador universal se deve considerar questões de projeto para diversos contextos de uso, onde diversos usuários interagem reaÌizando várias atividades, possivelmente de diversos domínios. Os métodos de desenvolvimento de software existentes não necessariamente suportam o projeto destes tipos de interfaces. Um novo método de desenvolvimento é apresentado neste trabalho com este propósito, o qual permite o projetista derivar múltiplas interfaces a partir de modelos representados por uma ontologia de conceitos, relações e atributos do domínio em questão.

In recenty ears, universal access has attracted considerable attention by the research communities, mainly from the perspective of system development. It comes from the fact that the design of user interfaces for all must cover design issues in multiple contexts fact that the design of user interfaces for all must cover design issues in multiple contexts domains of interest. E xistingd esign methods do not necessarily support designing such user interfaces. A new design method is presented for this purpose which allows a developer derives multiple user interfaces from models represented by an ontology of concepts, relationships, and attributes of the domain of discourse.