Innovative Parkinson's Disease Patients' Motor Skills Assessment: The i-PROGNOSIS Paradigm

Dias, Sofia B.; Grammatikopoulou, Athina; Diniz, José Alves; Dimitropoulos, Kosmas; Grammalidis, Nikos; Zilidou, Vasiliki I.; Savvidis, Theodore; Konstantinidis, Evdokimos I.; Bamidis, Panagiotis D.; Jaeger, Hagen; Stadtschnitzer, Michael; Silva, Hugo; Telo, Gonçalo; Ioakimidis, Ioannis; Ntakakis, George; Karayiannis, Fotis; Huchet, Estelle; Hoermann, Vera; Filis, Konstantinos; Theodoropoulou, Elina; Lyberopoulos, George; Kyritsis, Konstantinos; Παπαδόπουλος, Αλέξανδρος; Delopoulos, Anastasios; Trivedi, Dhaval; Klingelhoefer, Lisa; Reichmann, Heinz; Bostantzopoulou, Sevasti; Katsarou, Zoe; Iakovakis, Dimitrios; Hadjidimitriou, Stelios; Charisis, Vasileios; Apostolidis, Georgios; Hadjileontiadis, Leontios J.

doi:10.3389/fcomp.2020.00020

Cited by 13 publications

(16 citation statements)

References 87 publications

(91 reference statements)

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“…For instance, the Microsoft Kinect ® sensor have been used for neurological rehabilitation ( Knippenberg et al, 2017 ) and for analyzing PD posture and lower limb tasks ( Ferraris et al, 2019 ). In the same line, the use of the MentorAge ® sensor tracking system has proven its capabilities in real life environments (e.g., Anzivino et al, 2018 ; Petsani et al, 2019 ; Dias et al, 2020 ). In this vein, gait features can be extracted by relevant ExerGames that request from the user to move a couple of steps (within the depth camera range) toward different directions.…”

Section: Resultsmentioning

confidence: 99%

“…In a similar scenario, using appropriately designed gameplay, the patients can be instructed to progressively reach out large amplitude movements (e.g., hand grasping fruits at different heights from a virtual tree); based on the tracking of the patients’ performance (e.g., maximum angle, speed, articulation), using a depth camera, a personalized set of intermediate goals can be defined just above each patient’s average performance. Clearly, this can be related to various symptoms of PD, such as difficulty in gait, balance and coordination, and be reflected to the in-game metrics and mechanisms of the relevant ExerGames ( Dias et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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“…This capability provides automated food volume estimation from just a single food image ( 82 ), facilitating the data input to the PROTEIN app and contributing toward the development of an accurate dietary assistant application. PROTEIN app acknowledges the effectiveness of health-related games to engage users in dealing with their health ( 83 , 84 ) and informing them about deterioration of symptoms in diseases ( 85 ), and thus, it also incorporates different dietary games. In this way, PROTEIN app fosters adherence to the suggested plans, making the app fun to use, and providing the means for encouragement and/or (re)education on nutritional value of the various food groups toward the adoption of a healthy and balanced diet.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions via mobile apps. However, users' perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users' perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely, PROTEIN, and the mTAM constructs toward behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of 2 months were subjected to confirmatory factor analysis (CFA) and regression analysis (RA) to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users' behavior change (BC), as expressed via the acceptance/rejection of six related hypotheses (H1–H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related p-value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1–H6 can be accepted (p < 0.001). In particular, it was found that, in all cases, FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. Moreover, the adjusted coefficient of determination (R2) was found within the range of 0.224–0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. Additionally, using a hierarchical RA, a significant change in the prediction of BC from UA when the UI is used as a mediating variable was identified. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiology experts, and marketing experts for app design/development toward behavior change.

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“…It is important to note that the motor aspects within iMAT are also reflected within the general design of the Exergames included in the i-PROGNOSIS Games. Thus, iMAT can be seen as a complement to PD patient's conventional motor status monitoring, but also it can be used as a tool for measuring the intervention's progress [7].…”

Section: Motor Assessment Toolmentioning

confidence: 99%

Innovative interventions for Parkinson's disease patients using iPrognosis games

Dias¹,

Ioakimidis

Dimitropoulos

et al. 2020

Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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Innovative Parkinson's Disease Patients' Motor Skills Assessment: The i-PROGNOSIS Paradigm

Cited by 13 publications

References 87 publications

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

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

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

Innovative interventions for Parkinson's disease patients using iPrognosis games

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