Literature DB >> 33519632

Assistive HCI-Serious Games Co-design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson's Disease.

Sofia Balula Dias1, José Alves Diniz1, Evdokimos Konstantinidis2, Theodore Savvidis2, Vicky Zilidou2, Panagiotis D Bamidis2, Athina Grammatikopoulou3, Kosmas Dimitropoulos3, Nikos Grammalidis3, Hagen Jaeger4, Michael Stadtschnitzer4, Hugo Silva5, Gonçalo Telo5, Ioannis Ioakeimidis6, George Ntakakis7, Fotis Karayiannis7, Estelle Huchet8, Vera Hoermann8, Konstantinos Filis9, Elina Theodoropoulou9, George Lyberopoulos9, Konstantinos Kyritsis10, Alexandros Papadopoulos10, Anastasios Depoulos10, Dhaval Trivedi11, Ray K Chaudhuri11, Lisa Klingelhoefer12, Heinz Reichmann12, Sevasti Bostantzopoulou13, Zoe Katsarou13, Dimitrios Iakovakis14, Stelios Hadjidimitriou14, Vasileios Charisis14, George Apostolidis14, Leontios J Hadjileontiadis14,15.   

Abstract

Human-Computer Interaction (HCI) and games set a new domain in understanding people's motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people's health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson's Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients' quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.
Copyright © 2021 Dias, Diniz, Konstantinidis, Savvidis, Zilidou, Bamidis, Grammatikopoulou, Dimitropoulos, Grammalidis, Jaeger, Stadtschnitzer, Silva, Telo, Ioakeimidis, Ntakakis, Karayiannis, Huchet, Hoermann, Filis, Theodoropoulou, Lyberopoulos, Kyritsis, Papadopoulos, Depoulos, Trivedi, Chaudhuri, Klingelhoefer, Reichmann, Bostantzopoulou, Katsarou, Iakovakis, Hadjidimitriou, Charisis, Apostolidis and Hadjileontiadis.

Entities:  

Keywords:  Parkinson’s disease; co-creation; game-based learning; human-computer interaction-serious games; i-PROGNOSIS

Year:  2021        PMID: 33519632      PMCID: PMC7843389          DOI: 10.3389/fpsyg.2020.612835

Source DB:  PubMed          Journal:  Front Psychol        ISSN: 1664-1078


  3 in total

1.  Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation.

Authors:  Dunia J Mahboobeh; Sofia B Dias; Ahsan H Khandoker; Leontios J Hadjileontiadis
Journal:  Front Psychol       Date:  2022-03-17

2.  Development and Co-design of NeuroOrb: A Novel "Serious Gaming" System Targeting Cognitive Impairment in Parkinson's Disease.

Authors:  Bianca Guglietti; David A Hobbs; Bradley Wesson; Benjamin Ellul; Angus McNamara; Simon Drum; Lyndsey E Collins-Praino
Journal:  Front Aging Neurosci       Date:  2022-03-29       Impact factor: 5.750

3.  Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach.

Authors:  Sofia Balula Dias; Yannis Oikonomidis; José Alves Diniz; Fátima Baptista; Filomena Carnide; Alex Bensenousi; José María Botana; Dorothea Tsatsou; Kiriakos Stefanidis; Lazaros Gymnopoulos; Kosmas Dimitropoulos; Petros Daras; Anagnostis Argiriou; Konstantinos Rouskas; Saskia Wilson-Barnes; Kathryn Hart; Neil Merry; Duncan Russell; Jelizaveta Konstantinova; Elena Lalama; Andreas Pfeiffer; Anna Kokkinopoulou; Maria Hassapidou; Ioannis Pagkalos; Elena Patra; Roselien Buys; Véronique Cornelissen; Ana Batista; Stefano Cobello; Elena Milli; Chiara Vagnozzi; Sheree Bryant; Simon Maas; Pedro Bacelar; Saverio Gravina; Jovana Vlaskalin; Boris Brkic; Gonçalo Telo; Eugenio Mantovani; Olga Gkotsopoulou; Dimitrios Iakovakis; Stelios Hadjidimitriou; Vasileios Charisis; Leontios J Hadjileontiadis
Journal:  Front Nutr       Date:  2022-07-01
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.