Exergaming and rehabilitation: A methodology for the design of effective and safe therapeutic exergames

Sürer, Elif
LANZİ, Pier Luca
BORGHESE, N. Alberto
We present here a comprehensive definition of therapeutic exergames from which a methodology to create safe exergames for real therapy pathways is derived. Three main steps are identified. (I) A clear identification of all the exercise requirements, not only in terms of goals of the therapy, but also in terms of additional constraints. Characteristic parameters for determining the challenge level and to assess progression are also defined in this phase. (II) The exercise is transformed into a Virtual Exercise, in which all the exercise elements are implemented inside a simple virtual environment. In this step the discussion between clinical and ICT teams allows maximizing the effectiveness of exergames implementation. (III) The final exergame is realized by introducing on top of the exercise all the game elements suggested by good game design to maximize entertainment. A clear line between exercises and games is drawn here. We illustrate the methodology with exergames designed for (1) balance and posture and (2) neglect rehabilitation, implemented and tested with post-stroke patients training autonomously at home. The methodology can have a broader impact as it can be applied also in other gaming fields in which the requirements go beyond entertainment.


Deep learning for the classification of bipolar disorder using fNIRS measurements
Evgin, Haluk Barkın; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2021-2-3)
Functional Near-Infrared Spectroscopy (fNIRS) is a neural imaging method that is proved to be prominent in the classification of psychiatric disorders, and assertive accuracy results are being obtained using fNIRS. High temporal resolution, feasibility, and partial endurance to head movements are the traits that are highlighting fNIRS among other imaging methods. fNIRS data is a one dimensional multi-channeled time series. In this thesis, bipolar disorder is classified using some state of the art deep learn...
Cognitive Learner: An Ensemble Learning Architecture for Cognitive State Classification
Moğultay, Hazal (2017-05-18)
In this study, we propose an ensemble learning architecture called "Cognitive Learner", for classification of cognitive states from functional magnetic resonance imaging (fMRI). Proposed architecture consists of a two-layer hierarchy. In the first layer, called voxel layer, we model the connectivity among the voxel time series to represent the detailed information about the experiment. In the second layer, we cluster the voxel time series by using functional similarity measure, to partition the brain volume...
Voxel-MARS: a method for early detection of Alzheimer's disease by classification of structural brain MRI
Cevik, Alper; Weber, Gerhard-Wilhelm; Eyüboğlu, Behçet Murat; Oguz, Kader Karli (2017-11-01)
Neuroscience is of emerging importance along with the contributions of Operational Research to the practices of diagnosing neurodegenerative diseases with computer-aided systems based on brain image analysis. Although multiple biomarkers derived from Magnetic Resonance Imaging (MRI) data have proven to be effective in diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI), no specific system has yet been a part of routine clinical practice. This paper aims to introduce a fully-automated vox...
Exergames Encouraging Exploration of Hemineglected Space in Stroke Patients With Visuospatial Neglect: A Feasibility Study
TOBLER-AMMANN, Bernadette C.; Sürer, Elif; DE BRUIN, Eling D.; RABUFFETTI, Marco; BORGHESE, Alberto; MAINETTI, Renato; PIROVANO, Michele; WITTWER, Lia; KNOLS, Ruud H. (2017-07-01)
Background: Use of exergames can complement conventional therapy and increase the amount and intensity of visuospatial neglect (VSN) training. A series of 9 exergames-games based on therapeutic principles-aimed at improving exploration of the neglected space for patients with VSN symptoms poststroke was developed and tested for its feasibility.
Decision support system for Warfarin therapy management using Bayesian networks
Yet, Barbaros; Raharjo, Hendry; Lifvergren, Svante; Marsh, William; Bergman, Bo (2013-05-01)
Warfarin therapy is known as a complex process because of the variation in the patients' response. Failure to deal with such variation may lead to death as a result of thrombosis or bleeding. The possible sources of variation such as concomitant illnesses and drug interactions have to be investigated by the clinician in order to deal with the variation. This paper describes a decision support system (DSS) using Bayesian networks for assisting clinicians to make better decisions in Warfarin therapy managemen...
Citation Formats
M. PIROVANO, E. Sürer, R. MAINETTI, P. L. LANZİ, and N. A. BORGHESE, “Exergaming and rehabilitation: A methodology for the design of effective and safe therapeutic exergames,” ENTERTAINMENT COMPUTING, pp. 55–65, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31268.