Closing the loop in exergaming - Health benefits of biocybernetic adaptation in senior adults

Exergames help senior players to get physically active by promoting fun and enjoyment while exercising. However, most exergames are not designed to produce recommended levels of exercise that elicit adequate physical responses for optimal training in the aged population. In this project, we developed physiological computing technologies to overcome this issue by making real-time adaptations in a custom exergame based on recommendations for targeted heart rate (HR) levels. This biocybernetic adaptation was evaluated against conventional cardiorespiratory training in a group of active senior adults through a floor-projected exergame and a smartwatch to record HR data. Results showed that the physiologically-augmented exergame leads players to exert around 40% more time in the recommended HR levels, compared to the conventional training, avoiding over exercising and maintaining good enjoyment levels. Finally, we made available our biocybernetic adaptation software tool to enable the creation of physiological adaptive videogames, permitting the replication of our study.info:eu-repo/semantics/publishedVersio

Beyond Genre: Classifying Virtual Reality Experiences

12 pagesBecause virtual reality (VR) shares common features with video games, consumer content is usually classified according to traditional game genres and standards. However, VR offers different experiences based on the medium’s unique affordances. To account for this disparity, the paper presents a comparative analysis of titles from the Steam digital store across three platform types: VR only, VR supported, and non-VR. We analyzed data from a subset of the most popular applications within each category (N=141, 93, and 1217, respectively). The three classification types we analyzed were academic game genres, developer defined categories, and user-denoted tags. Results identify the most common content classifications (e.g., Action and Shooter within VR only applications), the relative availability of each between platforms (e.g., Casual is more common in VR only than VR supported or non-VR), general platform popularity (e.g., VR only received less positive ratings than VR supported and nonVR), and which content types are associated with higher user ratings across platforms (e.g., Action and Music/Rhythm are most positively rated in VR only). Our findings ultimately provide a foundational framework for future theoretical constructions of classification systems based on content, market, interactivity, sociality, and service dependencies, which underlay how consumer VR is currently categorized

gEYEded: Subtle and Challenging Gaze-Based Player Guidance in Exploration Games

This paper investigates the effects of gaze-based player guidance on the perceived game experience, performance, and challenge in a first-person exploration game. In contrast to existing research, the proposed approach takes the game context into account by providing players not only with guidance but also granting them an engaging game experience with a focus on exploration. This is achieved by incorporating gaze-sensitive areas that indicate the location of relevant game objects. A comparative study was carried out to validate our concept and to examine if a game supported with a gaze guidance feature triggers a more immersive game experience in comparison to a crosshair guidance version and a solution without any guidance support. In general, our study findings reveal a more positive impact of the gaze-based guidance approach on the experience and performance in comparison to the other two conditions. However, subjects had a similar impression concerning the game challenge in all conditions

How perceived toxicity of gaming communities is associated with social capital, satisfaction of relatedness, and loneliness

There are various benefits of playing multiplayer games, such as enjoyment, satisfaction of basic psychological needs, facilitation of social relationships, and coping and recovery. However, these benefits to online game players are often undermined by the presence of in-game toxicity. Toxicity can be detrimental for game developers when players leave their games. For the players, toxicity can be harmful, by causing distress; however, effects of toxicity on the wellbeing of players are not yet fully understood nor substantiated with empirical evidence. To close this gap, we conducted a study partially replicating and extending findings from prior work. We conducted two online surveys, using validated scales, to explore relationships between the perceived toxicity of gaming communities and social connectedness outcomes. We found that toxicity was associated with lower in-game social capital, need satisfaction of relatedness, and higher loneliness. Our findings provide further evidence that toxicity poses a problem for multiplayer game communities

Digital Mental Health and Social Connectedness

A detailed understanding of the mental health needs of people from refugee backgrounds is crucial for the design of inclusive mental health technologies. We present a qualitative account of the digital mental health experiences of women from refugee backgrounds. Working with community members and community workers of a charitable organisation for refugee women in the UK, we identify social and structural challenges, including loneliness and access to mental health technologies. Participants' accounts document their collective agency in addressing these challenges and supporting social connectedness and personal wellbeing in daily life: participants reported taking part in community activities as volunteers, sharing technological expertise, and using a wide range of non-mental health-focused technologies to support their mental health, from playing games to supporting religious practices. Our findings suggest that, rather than focusing only on individual self-care, research also needs to leverage community-driven approaches to foster social mental health experiences, from altruism to connectedness and belonging

Embracing first-person perspectives in soma-based design

This article belongs to the Special Issue Tangible and Embodied InteractionA set of prominent designers embarked on a research journey to explore aesthetics in movement-based design. Here we unpack one of the design sensitivities unique to our practice: A strong first person perspective-where the movements, somatics and aesthetic sensibilities of the designer, design researcher and user are at the forefront. We present an annotated portfolio of design exemplars and a brief introduction to some of the design methods and theory we use, together substantiating and explaining the first-person perspective. At the same time, we show how this felt dimension, despite its subjective nature, is what provides rigor and structure to our design research. Our aim is to assist researchers in soma-based design and designers wanting to consider the multiple facets when designing for the aesthetics of movement. The applications span a large field of designs, including slow introspective, contemplative interactions, arts, dance, health applications, games, work applications and many others

Enhancing explainability and scrutability of recommender systems

Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithm’s behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in filtering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the system’s behavior can be modified accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: • We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between users’ profiles and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. • We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the user’s prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for finding the smallest counterfactual explanations. • We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-specific item representations. We evaluate all proposed models and methods with real user studies and demonstrate their benefits at achieving explainability and scrutability in recommender systems.Unsere zunehmende Abhängigkeit von komplexen Algorithmen für maschinelle Empfehlungen erfordert Modelle und Methoden für erklärbare, nachvollziehbare und vertrauenswürdige KI. Zum Verstehen der Beziehungen zwischen Modellein- und ausgaben muss KI erklärbar sein. Möchten wir das Verhalten des Systems hingegen nach unseren Vorstellungen ändern, muss dessen Entscheidungsprozess nachvollziehbar sein. Erklärbarkeit und Nachvollziehbarkeit von KI helfen uns dabei, die Lücke zwischen dem von uns erwarteten und dem tatsächlichen Verhalten der Algorithmen zu schließen und unser Vertrauen in KI-Systeme entsprechend zu stärken. Um ein Übermaß an Informationen zu verhindern, spielen Empfehlungsdienste eine entscheidende Rolle um Inhalte (z.B. Produkten, Nachrichten, Musik und Filmen) zu filtern und deren Benutzern eine personalisierte Erfahrung zu bieten. Infolgedessen erheben immer mehr In- formationskonsumenten Anspruch auf angemessene Erklärungen für deren personalisierte Empfehlungen. Diese Erklärungen sollen den Benutzern helfen zu verstehen, warum ihnen bestimmte Dinge empfohlen wurden und wie sich ihre früheren Eingaben in das System auf die Generierung solcher Empfehlungen auswirken. Außerdem können Erklärungen für den Fall, dass unerwünschte Inhalte empfohlen werden, wertvolle Informationen darüber enthalten, wie das Verhalten des Systems entsprechend geändert werden kann. In dieser Dissertation stellen wir unsere Beiträge zu Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten vor. • Mit FAIRY stellen wir ein benutzerzentriertes Framework vor, mit dem post-hoc Erklärungen für die von Black-Box-Plattformen generierten sozialen Feeds entdeckt und bewertet werden können. Diese Erklärungen zeigen Beziehungen zwischen Benutzerprofilen und deren Feeds auf und werden aus den lokalen Interaktionsgraphen der Benutzer extrahiert. FAIRY verwendet eine LTR-Methode (Learning-to-Rank), um die Erklärungen anhand ihrer Relevanz und ihres Grads unerwarteter Empfehlungen zu bewerten. • Mit der PRINCE-Methode erleichtern wir das anbieterseitige Generieren von Erklärungen für PageRank-basierte Empfehlungsdienste. PRINCE-Erklärungen sind für Benutzer verständlich, da sie Teilmengen früherer Nutzerinteraktionen darstellen, die für die erhaltenen Empfehlungen verantwortlich sind. PRINCE-Erklärungen sind somit kausaler Natur und werden von einem Algorithmus mit polynomieller Laufzeit erzeugt , um präzise Erklärungen zu finden. • Wir präsentieren ein Human-in-the-Loop-Framework, ELIXIR, um die Nachvollziehbarkeit der Empfehlungsmodelle und die Qualität der Empfehlungen zu verbessern. Mit ELIXIR können Empfehlungsdienste Benutzerfeedback zu Empfehlungen und Erklärungen sammeln. Das Feedback wird in das Modell einbezogen, indem benutzerspezifischer Einbettungen von Objekten gelernt werden. Wir evaluieren alle Modelle und Methoden in Benutzerstudien und demonstrieren ihren Nutzen hinsichtlich Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten

Unpacking Non-Dualistic Design: The Soma Design Case

We report on a somaesthetic design workshop and the subsequent analytical work aiming to demystify what is entailed in a non-dualistic design stance on embodied interaction and why a first-person engagement is crucial to its unfoldings. However, as we will uncover through a detailed account of our process, these first-person engagements are deeply entangled with second- and third-person perspectives, sometimes even overlapping. The analysis furthermore reveals some strategies for bridging the body-mind divide by attending to our inner universe and dissolving or traversing dichotomies between inside and outside; individual and social; body and technology. By detailing the creative process, we show how soma design becomes a process of designing with and through kinesthetic experience, in turn letting us confront several dualisms that run like fault lines through HCI's engagement with embodied interaction

Merlynne: Motivating Peer-to-Peer Cognitive Behavioral Therapy with a Serious Game

Human-Computer Interaction researchers have explored how online communities can be leveraged for peer support, but general disinterest and a lack of engagement have emerged as substantial barriers to their use in practice. To address this gap, we designed Merlynne, a serious game that seeks to motivate individuals to support peers through Cognitive Behavioural Therapy (CBT). Our game explored use of the Proteus Effect — a phenomenon where players adopt characteristics of their in-game avatar — to motivate peer support through stereotyped 'helpful' and 'unhelpful' avatars. We then conducted a mixed-methods, exploratory study to investigate its design. We found that our game successfully motivated players to offer peer support, despite the substantial emotional labour required by CBT. However, we were not able to replicate the Proteus Effect, and did not find differences in that support based on a player's avatar. In reflecting on our findings, we discuss design challenges and considerations for the use of serious games to motivate participation in mental health support, including: fatigue, a player's need for self-expression and to relate to those they are supporting, and ludonarrative dissonance