Computer Interaction Paradigm Research Articles

HOLMeS: eHealth in the Big Data and Deep Learning Era

Now, data collection and analysis are becoming more and more important in a variety of application domains, as long as novel technologies advance. At the same time, we are experiencing a growing need for human–machine interaction with expert systems, pushing research toward new knowledge representation models and interaction paradigms. In particular, in the last few years, eHealth—which usually indicates all the healthcare practices supported by electronic elaboration and remote communications—calls for the availability of a smart environment and big computational resources able to offer more and more advanced analytics and new human–computer interaction paradigms. The aim of this paper is to introduce the HOLMeS (health online medical suggestions) system: A particular big data platform aiming at supporting several eHealth applications. As its main novelty/functionality, HOLMeS exploits a machine learning algorithm, deployed on a cluster-computing environment, in order to provide medical suggestions via both chat-bot and web-app modules, especially for prevention aims. The chat-bot, opportunely trained by leveraging a deep learning approach, helps to overcome the limitations of a cold interaction between users and software, exhibiting a more human-like behavior. The obtained results demonstrate the effectiveness of the machine learning algorithms, showing an area under ROC (receiver operating characteristic) curve (AUC) of 74.65% when some first-level features are used to assess the occurrence of different chronic diseases within specific prevention pathways. When disease-specific features are added, HOLMeS shows an AUC of 86.78%, achieving a greater effectiveness in supporting clinical decisions.

A methodology to develop usability/user experience heuristics

Technology, software systems and human–computer interaction paradigms are evolving. Traditional usability heuristics do not cover all aspects of user–system interactions. Many sets of heuristics have been proposed, with the aim of evaluating specific application domains and their specific usability-related features. In addition, several sets of heuristics are used to evaluate aspects other than usability that are related to the user experience (UX). However, most authors use an informal process to develop usability/UX heuristics; there is no clear protocol for heuristic validation. This can result in sets of usability/UX heuristics that are difficult to understand or use; moreover, the resulting sets of heuristics may not be effective or efficient evaluation tools. This article presents a formal methodology for developing usability/user experience heuristics. The methodology was applied in practice in several case studies; it was also validated through expert opinions.

Universal Remote Delivery of Rehabilitation: Validation with Seniors' Joint Rehabilitation Therapy

Remote rehabilitation applications still have limited deployment. The path to achieve greater user acceptance and adherence lies in the provision of solutions in their real-life context. Such acceptance is gained through flexibility provided in terms of location, client device, interaction means, and content. This article presents a universal remote rehabilitation delivery approach supporting the introduced flexibility needs. Furthermore, an implementation of the approach in joint rehabilitation for the elderly is described. The approach has been evaluated in a real scenario within Donostia Hospital. The usability evaluation results show the validity of the approach and the acceptance of the developed human −computer interaction paradigm.

Interactively mediating experiences of mindfulness meditation

Sonic Cradle is a human–computer interaction paradigm designed to foster meditative attentional patterns. A user׳s body is suspended comfortably in a completely dark sound chamber while the interaction paradigm subtly encourages them to focus on their breathing to summon and progressively shape an abstract immersive sound experience. Basic interpretive qualitative methods with a purposive sample of 39 participants were used to systematically analyze interview data after a 15-min experience of the system. Results suggest that this persuasive medium can pleasantly encourage an experience comparable to mindfulness by consistently inducing a calm mental clarity and loss of intention. Surprisingly, participants also reported perceptual illusions, feelings of floating, and emotional responses. Mounting evidence implies mindfulness meditation as an effective practice for self-regulation; this study represents a first step toward realizing technology׳s potential to increase wellbeing by introducing people to this psychologically beneficial contemplative practice.

Admux: An Adaptive Multiplexer for Haptic–Audio–Visual Data Communication

Research trends in multimedia strive to incorporate multiple modalities, such as audio, video, graphics, and haptics, into multimedia applications to enhance the user's experience. Researchers have made significant progress in advanced multimedia by incorporating virtual reality environments, haptics, and scent into the human computer interaction paradigm. However, the communication of multimedia data over the Internet (particularly the haptic media) remains a real challenge since each media has varying and sometimes conflicting communication requirements. This paper proposes Admux, an adaptive application layer multiplexing framework (including a communication protocol) for multimedia applications incorporating haptic, visual, auditory, and scent data for nondedicated networks. Being an application layer framework, Admux is highly adaptable to the application requirements, the media type (haptic, audio, video, etc.), and the network conditions. To facilitate the application-Admux communication, we used haptic application metalanguage descriptions. Second, Admux enhances the network throughput by adopting statistical multiplexing. Finally, Admux enables media prioritization based on the application events and QoS requirements. By simulating an interpersonal teleconferencing system (named the HugMe system), our results showed that Admux provides dynamic bandwidth allocation based on the network conditions, media type, and application events.

Distributed, multi-sensor tracking of multiple participants within immersive environments using a 2-cohort camera setup

Vision-based human motion tracking has gained significant interest in recent years, as the need for more intuitive human–computer interaction paradigms are sought after. Immersive environments, such as large-scale displays and virtual reality systems are particularly suitable for such interaction mechanisms, as they provide a controlled basis for experimentation and evaluation. However, despite the growing interest, human-motion tracking within immersive environments is still in its infancy. The lack of visible light and the presence of multiple people within such environments pose several challenges to the problem. In this research a robust framework for real-time, multi-camera human-motion tracking within an immersive environment is presented. The static nature of the environment is exploited to utilise a novel 2-cohort camera layout. This layout proves to be highly useful for tracking, providing several benefits over existing techniques, which treat all cameras as the same.

Authentic facial expression analysis

There is a growing trend toward emotional intelligence in human–computer interaction paradigms. In order to react appropriately to a human, the computer would need to have some perception of the emotional state of the human. We assert that the most informative channel for machine perception of emotions is through facial expressions in video. One current difficulty in evaluating automatic emotion detection is that there are currently no international databases which are based on authentic emotions. The current facial expression databases contain facial expressions which are not naturally linked to the emotional state of the test subject. Our contributions in this work are twofold: first, we create the first authentic facial expression database where the test subjects are showing the natural facial expressions based upon their emotional state. Second, we evaluate the several promising machine learning algorithms for emotion detection which include techniques such as Bayesian networks, SVMs, and decision trees.