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{kyritsis2020data,
title = {A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches},
author = {Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
doi = {10.1109/JBHI.2020.2984907},
year = {2020},
date = {2020-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
publisher = {IEEE},
abstract = {The increased worldwide prevalence of obesity has sparked the interest of the scientific community towards tools that objectively and automatically monitor eating behavior. Despite the study of obesity being in the spotlight, such tools can also be used to study eating disorders (e.g. anorexia nervosa) or provide a personalized monitoring platform for patients or athletes. This paper presents a complete framework towards the automated i) modeling of in-meal eating behavior and ii) temporal localization of meals, from raw inertial data collected in-the-wild using commercially available smartwatches. Initially, we present an end-to-end Neural Network which detects food intake events (i.e. bites). The proposed network uses both convolutional and recurrent layers that are trained simultaneously. Subsequently, we show how the distribution of the detected bites throughout the day can be used to estimate the start and end points of meals, using signal processing algorithms. We perform extensive evaluation on each framework part individually. Leave-one-subject-out (LOSO) evaluation shows that our bite detection approach outperforms four state-of-the-art algorithms towards the detection of bites during the course of a meal (0.923 F1 score). Furthermore, LOSO and held-out set experiments regarding the estimation of meal start/end points reveal that the proposed approach outperforms a relevant approach found in the literature (Jaccard Index of 0.820 and 0.821 for the LOSO and held-out experiments, respectively). Experiments are performed using our publicly available FIC and the newly introduced FreeFIC datasets.},
keywords = {Feature extraction, Informatics, Monitoring, Obesity, Sensors, Tools, Wrist},
pubstate = {published},
tppubtype = {article}
}

@article{papadopoulos2020unobtrusive,
title = {Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques},
author = {Alexandros Papadopoulos and Dimitrios Iakovakis and Lisa Klingelhoefer and Sevasti Bostantjopoulou and Ray K Chaudhuri and Konstantinos Kyritsis and Stelios Hadjidimitriou and Vasileios Charisis and Leontios J Hadjileontiadis and Anastasios Delopoulos},
doi = {https://doi.org/10.1038/s41598-020-78418-8},
year = {2020},
date = {2020-01-01},
journal = {Scientific Reports},
volume = {10},
number = {1},
pages = {1--13},
publisher = {Nature Publishing Group},
abstract = {Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, affecting more than 1% of the population above 60 years old with both motor and non-motor symptoms of escalating severity as it progresses. Since it cannot be cured, treatment options focus on the improvement of PD symptoms. In fact, evidence suggests that early PD intervention has the potential to slow down symptom progression and improve the general quality of life in the long term. However, the initial motor symptoms are usually very subtle and, as a result, patients seek medical assistance only when their condition has substantially deteriorated; thus, missing the opportunity for an improved clinical outcome. This situation highlights the need for accessible tools that can screen for early motor PD symptoms and alert individuals to act accordingly. Here we show that PD and its motor symptoms can unobtrusively be detected from the combination of accelerometer and touchscreen typing data that are passively captured during natural user-smartphone interaction. To this end, we introduce a deep learning framework that analyses such data to simultaneously predict tremor, fine-motor impairment and PD. In a validation dataset from 22 clinically-assessed subjects (8 Healthy Controls (HC)/14 PD patients with a total data contribution of 18.305 accelerometer and 2.922 typing sessions), the proposed approach achieved 0.86/0.93 sensitivity/specificity for the binary classification task of HC versus PD. Additional validation on data from 157 subjects (131 HC/26 PD with a total contribution of 76.528 accelerometer and 18.069 typing sessions) with self-reported health status (HC or PD), resulted in area under curve of 0.87, with sensitivity/specificity of 0.92/0.69 and 0.60/0.92 at the operating points of highest sensitivity or specificity, respectively. Our findings suggest that the proposed method can be used as a stepping stone towards the development of an accessible PD screening tool that will passively monitor the subject-smartphone interaction for signs of PD and which could be used to reduce the critical gap between disease onset and start of treatment.},
keywords = {Information technology, Parkinson's disease, Statistics},
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}
}

@article{10.3389/fcomp.2020.00020,
title = {Innovative Parkinson's Disease Patients' Motor Skills Assessment: The i-PROGNOSIS Paradigm},
author = {Sofia Balula Dias and Athina Grammatikopoulou and José Alves Diniz and Kosmas Dimitropoulos and Nikos Grammalidis and Vicky Zilidou and Theodore Savvidis and Evdokimos Konstantinidis and Panagiotis D Bamidis 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 Delopoulos 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/fcomp.2020.00020},
doi = {10.3389/fcomp.2020.00020},
issn = {2624-9898},
year = {2020},
date = {2020-01-01},
journal = {Frontiers in Computer Science},
volume = {2},
pages = {20},
abstract = {Being the second most common neurodegenerative disease, Parkinson's disease (PD) can be symptomatically treated, although, unfortunately, it cannot be cured yet. Moreover, diagnosing and assessing PD patients is a complex process, requiring continuous monitoring. In this vein, the design, development, and validation of innovative assessment tools may be helpful in the management of patients with PD, in particular. Based on intelligent ICT interventions, the i-PROGNOSIS project intends to mitigate PD's specific symptoms, such as neurological movement disorders of gait, balance, coordination, and posture, already characterized in the early phase of the disease. From this perspective, an innovative iPrognosis motor assessment tool is presented here, taking into consideration the Unified Parkinson Disease Rating Scale (UPDRS) Part III motor skills testing items, for evaluating the motor skills status. The efficiency of the proposed Assessment Tests to reflect the motor skills status, similarly to the UPDRS Part III items, was validated via 27 participants (18 males; mean age = 62 years},
keywords = {i-PROGNOSIS, motor assessment tests, motor skills decline, parkinson's disease (PD), unified parkinson disease rating scale (UPDRS) part III},
pubstate = {published},
tppubtype = {article}
}

@article{kyritsis2019modeling,
title = {Modeling wrist micromovements to measure in-meal eating behavior from inertial sensor data},
author = {Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
doi = {10.1109/JBHI.2019.2892011},
year = {2019},
date = {2019-01-01},
journal = {IEEE journal of biomedical and health informatics},
volume = {23},
number = {6},
pages = {2325--2334},
publisher = {IEEE},
abstract = {Overweight and obesity are both associated with in-meal eating parameters such as eating speed. Recently, the plethora of available wearable devices in the market ignited the interest of both the scientific community and the industry toward unobtrusive solutions for eating behavior monitoring. In this paper, we present an algorithm for automatically detecting the in-meal food intake cycles using the inertial signals (acceleration and orientation velocity) from an off-the-shelf smartwatch. We use five specific wrist micromovements to model the series of actions leading to and following an intake event (i.e., bite). Food intake detection is performed in two steps. In the first step, we process windows of raw sensor streams and estimate their micromovement probability distributions by means of a convolutional neural network. In the second step, we use a long short-term memory network to capture the temporal evolution and classify sequences of windows as food intake cycles. Evaluation is performed using a challenging dataset of 21 meals from 12 subjects. In our experiments, we compare the performance of our algorithm against three state-of-theart approaches, where our approach achieves the highest F1 detection score (0.913 in the leave-one-subject-out experiment). The dataset used in the experiments is available at https://mug.ee.auth.gr/intake-cycle-detection/.},
keywords = {Acceleration, Feature extraction, Informatics, Monitoring, Mouth, Obesity, Wrist},
pubstate = {published},
tppubtype = {article}
}

@article{papadopoulos2019detecting,
title = {Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning},
author = {Alexandros Papadopoulos and Konstantinos Kyritsis and Lisa Klingelhoefer and Sevasti Bostanjopoulou and Ray K Chaudhuri and Anastasios Delopoulos},
doi = {10.1109/JBHI.2019.2961748},
year = {2019},
date = {2019-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
publisher = {IEEE},
abstract = {Parkinson's Disease (PD) is a slowly evolving neurological disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings.},
keywords = {Accelerometers, Annotations, Diseases, Feature extraction, Rigidity, Sensors, Standards},
pubstate = {published},
tppubtype = {article}
}