Browsing by Author "Muñoz, J. E."
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- The biocybernetic loop engine: an integrated tool for creating physiologically adaptive videogamesPublication . Bermúdez i Badia, Sergi; Cameirão, M. S.; Gouveia, E. R.; Muñoz, J. E.Biocybernetic loops (BLs) are physiological adaptation mechanisms created to augment human-computer interaction by interpreting human behaviour via physiological responses. Because of its inherent complexity, the development of BLs has been mainly utilized within the academic environment, with limited use of physiologically adaptive systems in promising fields such as assistive and gaming technologies. The Biocybernetic Loop Engine (BL Engine) is an integrated software tool designed for an easy creation of physiologically modulated videogames by means of wearable sensors. The BL Engine includes a signal acquisition panel, which facilitates the connectivity of multiple physiological sensors and the processing of their signals, a biocybernetic console to rapidly create and iterate adaptive rules using a visual scripting module, and a game connector tool that ties physiological modulations to game variables. In this paper, we present the BL Engine software architecture, its design and implementation process, as well as a proof-of-concept of the system applied to an exergaming experience aiming to improve cardiorespiratory fitness training in older adults. By developing integrated tools that aid the design and implementation of BLs in videogames, we aim to contribute to the dissemination and widespread use of this approach in the gaming industry and serious gaming applications.
- Visualization of multivariate physiological data for cardiorespiratory fitness assessment through ECG (R-peak) analysisPublication . Muñoz, J. E.; Bermúdez i Badia, Sergi; Rubio, E.; Cameirão, M. S.The recent rise and popularization of wearable and ubiquitous fitness sensors has increased our ability to generate large amounts of multivariate data for cardiorespiratory fitness (CRF) assessment. Consequently, there is a need to find new methods to visualize and interpret CRF data without overwhelming users. Current visualizations of CRF data are mainly tabular or in the form of stacked univariate plots. Moreover, normative data differs significantly between gender, age and activity, making data interpretation yet more challenging. Here we present a CRF assessment tool based on radar plots that provides a way to represent multivariate cardiorespiratory data from electrocardiographic (ECG) signals within its normative context. To that end, 5 parameters are extracted from raw ECG data using R-peak information: mean HR, SDNN, RMSSD, HRVI and the maximal oxygen uptake, VO2max. Our tool processes ECG data and produces a visualization of the data in a way that it is easy to compare between the performance of the user and normative data. This type of representation can assist both health professionals and non-expert users in the interpretation of CRF data.