Assessment of Manual Dexterity in VR: Towards a Fully Automated Version of the Box and Blocks Test

Proceeding of The 27th Australian National Health Informatics Conference (HIC 2019), 12-14 August 2019, Melbourne, AustraliaIn recent years, the possibility of using serious gaming technology for the automation of clinical procedures for assessment of motor function have captured the interest of the research community. In this paper, a virtual version of the Box and Blocks Test (BBT) for manual dexterity assessment is presented. This game-like system combines the classical BBT mechanics with a play-centric approach to accomplish a fully automated test for assessing hand motor function, making it more accessible and easier to administer. Additionally, some variants of the traditional mechanics are proposed in order to fully exploit the advantages of the chosen technology. This ongoing research aims to provide the clinical practitioners with a customisable, intuitive, and reliable tool for the assessment and rehabilitation of hand motor function.Work funded by the Spanish Ministry of Economy and Competitiveness (ROBOESPAS project DPI2017-87562-C2-1-R and mobility grant EST2019-013090), and by the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (S2018/NMT-4331).Publicad

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

This manuscript explores the development of a technique for detecting the affective states of Virtual Reality (VR) users in real-time. The technique was tested with data from an experiment where 18 participants observed 16 videos with emotional content inside a VR home theater, while their electroencephalography (EEG) signals were recorded. Participants evaluated their affective response toward the videos in terms of a three-dimensional model of affect. Two variants of the technique were analyzed. The difference between both variants was the method used for feature selection. In the first variant, features extracted from the EEG signals were selected using Linear Mixed-Effects (LME) models. In the second variant, features were selected using Recursive Feature Elimination with Cross Validation (RFECV). Random forest was used in both variants to build the classification models. Accuracy, precision, recall and F1 scores were obtained by cross-validation. An ANOVA was conducted to compare the accuracy of the models built in each variant. The results indicate that the feature selection method does not have a significant effect on the accuracy of the classification models. Therefore, both variations (LME and RFECV) seem equally reliable for detecting affective states of VR users. The mean accuracy of the classification models was between 87% and 93%

Targeting social learning and engagement: What serious games and gamification can offer to participatory modeling

Serious games and gamification are useful tools for learning and sustaining long-term engagement in the activities that are not meant to be entertaining. However, the application of game design in the participatory modeling context remains fragmented and mostly limited to user-friendly interfaces, storytelling, and visualization for better representation of the simulation models. This paper suggests possible extensions of game design use for each stage of the participatory modeling process, aiming at better learning, communication among stakeholders, and overall engagement. The proposed extensions are based on the effects that different types of game-like applications bring to the aspects of social learning and the contribution of gamification to engagement, motivation, and enjoyment of some activities. We conclude that serious games and gamification have a high potential for improving the quality of the participatory modeling process, while also highlighting additional research that is needed for designing particular practical gamified applications in this context

Gamification of Discussoo: An Online AI-Based Forum for Serious Discussions

Engagement in the discussion process is one of the common challenges of asynchronous online forums. It becomes especially crucial if the discussion is organized over a serious topic about a complex problem with a group of diverse stakeholders. Gamification gives much promise in addressing this challenge. In this paper, we propose possible game design solutions to the engagement challenge for an existing online AI-based platform Discussoo and reflect on the results from the expert interviews and an experiment with students

Assessment of Manual Dexterity in VR: Towards a Fully Automated Version of the Box and Blocks Test

In recent years, the possibility of using serious gaming technology for the automation of clinical procedures for assessment of motor function have captured the interest of the research community. In this paper, a virtual version of the Box and Blocks Test (BBT) for manual dexterity assessment is presented. This game-like system combines the classical BBT mechanics with a play-centric approach to accomplish a fully automated test for assessing hand motor function, making it more accessible and easier to administer. Additionally, some variants of the traditional mechanics are proposed in order to fully exploit the advantages of the chosen technology. This ongoing research aims to provide the clinical practitioners with a customisable, intuitive, and reliable tool for the assessment and rehabilitation of hand motor function.Work funded by the Spanish Ministry of Economy and Competitiveness (ROBOESPAS project DPI2017-87562-C2-1-R and mobility grant EST2019-013090), and by the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (S2018/NMT-4331)

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