Research

@article{10.3389/fpsyg.2020.612835,

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

author = {Sofia Balula Dias and José Alves Diniz and Evdokimos Konstantinidis and Theodore Savvidis and Vicky Zilidou and Panagiotis D Bamidis and Athina Grammatikopoulou and Kosmas Dimitropoulos and Nikos Grammalidis and Hagen Jaeger and Michael Stadtschnitzer and Hugo Silva and Gonçalo Telo and Ioannis Ioakeimidis and George Ntakakis and Fotis Karayiannis and Estelle Huchet and Vera Hoermann and Konstantinos Filis and Elina Theodoropoulou and George Lyberopoulos and Konstantinos Kyritsis and Alexandros Papadopoulos and Anastasios Depoulos and Dhaval Trivedi and Ray K Chaudhuri and Lisa Klingelhoefer and Heinz Reichmann and Sevasti Bostantzopoulou and Zoe Katsarou and Dimitrios Iakovakis and Stelios Hadjidimitriou and Vasileios Charisis and George Apostolidis and Leontios J Hadjileontiadis},

url = {https://www.frontiersin.org/article/10.3389/fpsyg.2020.612835},

doi = {10.3389/fpsyg.2020.612835},

issn = {1664-1078},

year  = {2021},

date = {2021-01-01},

journal = {Frontiers in Psychology},

volume = {11},

pages = {4017},

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.},

keywords = {co-creation, game-based learning, human-computer interaction-serious games, i-PROGNOSIS, Parkinson’s disease},

pubstate = {published},

tppubtype = {article}

}

@article{kyritsis2021assessment,

title = {Assessment of real life eating difficulties in Parkinson’s disease patients by measuring plate to mouth movement elongation with inertial sensors},

author = {Konstantinos Kyritsis and Petter Fagerberg and Ioannis Ioakimidis and Ray K Chaudhuri and Heinz Reichmann and Lisa Klingelhoefer and Anastasios Delopoulos},

doi = {https://doi.org/10.1038/s41598-020-80394-y},

year  = {2021},

date = {2021-01-01},

journal = {Scientific Reports},

volume = {11},

number = {1},

pages = {1--14},

publisher = {Nature Publishing Group},

abstract = {Parkinson’s disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease’s natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia—factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite’s PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.},

keywords = {LSTM, Parkinson’s disease, Support vector machines},

pubstate = {published},

tppubtype = {article}

}

@article{fagerberg2020lower,

title = {Lower Energy Intake among Advanced vs. Early Parkinson’s Disease Patients and Healthy Controls in a Clinical Lunch Setting: A Cross-Sectional Study},

author = {Petter Fagerberg and Lisa Klingelhoefer and Matteo Bottai and Billy Langlet and Konstantinos Kyritsis and Eva Rotter and Heinz Reichmann and Björn Falkenburger and Anastasios Delopoulos and Ioannis Ioakimidis},

doi = {https://doi.org/10.3390/nu12072109},

year  = {2020},

date = {2020-01-01},

journal = {Nutrients},

volume = {12},

number = {7},

pages = {2109},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Unintentional weight loss has been observed among Parkinson’s disease (PD) patients. Changes in energy intake (EI) and eating behavior, potentially caused by fine motor dysfunction and eating-related symptoms, might contribute to this. The primary aim of this study was to investigate differences in objectively measured EI between groups of healthy controls (HC), early (ESPD) and advanced stage PD patients (ASPD) during a standardized lunch in a clinical setting. The secondary aim was to identify clinical features and eating behavior abnormalities that explain EI differences. All participants (n = 23 HC, n = 20 ESPD, and n = 21 ASPD) went through clinical evaluations and were eating a standardized meal (200 g sausages, 400 g potato salad, 200 g apple purée and 500 mL water) in front of two video cameras. Participants ate freely, and the food was weighed pre- and post-meal to calculate EI (kcal). Multiple linear regression was used to explain group differences in EI. ASPD had a significantly lower EI vs. HC (−162 kcal, p < 0.05) and vs. ESPD (−203 kcal, p < 0.01) when controlling for sex. The number of spoonfuls, eating problems, dysphagia and upper extremity tremor could explain most (86%) of the lower EI vs. HC, while the first three could explain ~50% vs. ESPD. Food component intake analysis revealed significantly lower potato salad and sausage intakes among ASPD vs. both HC and ESPD, while water intake was lower vs. HC. EI is an important clinical target for PD patients with an increased risk of weight loss. Our results suggest that interventions targeting upper extremity tremor, spoonfuls, dysphagia and eating problems might be clinically useful in the prevention of unintentional weight loss in PD. Since EI was lower in ASPD, EI might be a useful marker of disease progression in PD. },

keywords = {eating behavior, energy intake, food, malnutrition, Monitoring, neurodegenerative diseases, Parkinson’s disease, weight loss},

pubstate = {published},

tppubtype = {article}

}