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

Scientific Reports, 11, pp. 1-14, 2021 Jan

[Abstract][BibTex][pdf]

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.

@article{kyritsis2021assessment,
author={Konstantinos Kyritsis and Petter Fagerberg and Ioannis Ioakimidis and K Ray Chaudhuri and Heinz Reichmann and Lisa Klingelhoefer and Anastasios Delopoulos},
title={Assessment of real life eating difficulties in Parkinson’s disease patients by measuring plate to mouth movement elongation with inertial sensors},
journal={Scientific Reports},
volume={11},
pages={1-14},
year={2021},
month={01},
date={2021-01-15},
url={https://www.nature.com/articles/s41598-020-80394-y},
doi={https://doi.org/10.1038/s41598-020-80394-y},
keywords={Sensors;Computational science;Parkinson's disease;IMU;wearable;PD;Plate to mouth;PtM},
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.}
}

Athanasios Kirmizis, Konstantinos Kyritsis and Anastasios Delopoulos

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021 Dec

[Abstract][BibTex][pdf]

@conference{kirmizis2021bottom,
author={Athanasios Kirmizis and Konstantinos Kyritsis and Anastasios Delopoulos},
title={A Bottom-up method Towards the Automatic and Objective Monitoring of Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors},
booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)},
year={2021},
month={12},
date={2021-12-09},
url={https://ieeexplore.ieee.org/document/9630491},
doi={http://10.1109/EMBC46164.2021.9630491}
}

Alexandros Papadopoulos , Dimitrios Iakovakis, Lisa Klingelhoefer, Sevasti Bostantjopoulou, K. Ray Chaudhuri, Konstantinos Kyritsis, Stelios Hadjidimitriou, Vasileios Charisis , Leontios J. Hadjileontiadis and Anastasios Delopoulos

Scientific Reports, 2020 Dec

[Abstract][BibTex][pdf]

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.

@article{alpapado2020,
author={Alexandros Papadopoulos and Dimitrios Iakovakis and Lisa Klingelhoefer and Sevasti Bostantjopoulou and K. Ray Chaudhuri and Konstantinos Kyritsis and Stelios Hadjidimitriou and Vasileios Charisis and Leontios J. Hadjileontiadis and Anastasios Delopoulos},
title={Unobtrusive detection of Parkinson’s disease from multi?modal and in?the?wild sensor data using deep learning techniques},
journal={Scientific Reports},
year={2020},
month={12},
date={2020-12-07},
url={https://www.nature.com/articles/s41598-020-78418-8},
doi={https://doi.org/10.1038/s41598-020-78418-8},
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.}
}

Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos

IEEE Journal of Biomedical and Health Informatics, 2020 Apr

[Abstract][BibTex][pdf]

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.

@article{kyritsis2020data,
author={Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches},
journal={IEEE Journal of Biomedical and Health Informatics},
year={2020},
month={04},
date={2020-04-03},
url={http://mug.ee.auth.gr/wp-content/uploads/kokirits2020data.pdf},
doi={http://10.1109/JBHI.2020.2984907},
keywords={Wearable sensors;biomedical signal processing},
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.}
}

Alexandros Papadopoulos, Konstantinos Kyritsis, Lisa Klingelhoefer, Sevasti Bostanjopoulou, K. Ray Chaudhuri and Anastasios Delopoulos

IEEE Journal of Biomedical and Health Informatics, 2019 Dec

[Abstract][BibTex][pdf]

Parkinson's Disease (PD) is a slowly evolving neuro-logical 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 algo- rithm 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.

@article{alpapado2019detecting,
author={Alexandros Papadopoulos and Konstantinos Kyritsis and Lisa Klingelhoefer and Sevasti Bostanjopoulou and K. Ray Chaudhuri and Anastasios Delopoulos},
title={Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning},
journal={IEEE Journal of Biomedical and Health Informatics},
year={2019},
month={12},
date={2019-12-24},
url={http://mug.ee.auth.gr/wp-content/uploads/alpapado2019detecting.pdf},
doi={http://10.1109/JBHI.2019.2961748},
abstract={Parkinson\'s Disease (PD) is a slowly evolving neuro-logical 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 algo- rithm 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.}
}

Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos

IEEE Journal of Biomedical and Health Informatics (JBHI), 2019 Jan

[Abstract][BibTex][pdf]

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 towards 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 5 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 (CNN). In the second step we use a Long-Short Term Memory (LSTM) 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-the-art 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/.

@article{kyritsis2019modeling,
author={Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
title={Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data},
journal={IEEE Journal of Biomedical and Health Informatics (JBHI)},
year={2019},
month={01},
date={2019-01-09},
url={http://mug.ee.auth.gr/wp-content/uploads/kyritsis2019modeling.pdf},
doi={http://10.1109/JBHI.2019.2892011},
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 towards 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 5 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 (CNN). In the second step we use a Long-Short Term Memory (LSTM) 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-the-art 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/.}
}

A. Papadopoulos, K. Kyritsis, S. Bostanjopoulou, L. Klingelhoefer, R. K. Chaudhuri and A. Delopoulos

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 Jul

[Abstract][BibTex][pdf]

Parkinson’s Disease (PD) is a neurodegenerative disorder that manifests through slowly progressing symptoms, such as tremor, voice degradation and bradykinesia. Automated detection of such symptoms has recently received much attention by the research community, owing to the clinical benefits associated with the early diagnosis of the disease. Unfortunately, most of the approaches proposed so far, operate under a strictly laboratory setting, thus limiting their potential applicability in real world conditions. In this work, we present a method for automatically detecting tremorous episodes related to PD, based on acceleration signals. We propose to address the problem at hand, as a case of Multiple-Instance Learning, wherein a subject is represented as an unordered bag of signal segments and a single, expert-provided, ground-truth. We employ a deep learning approach that combines feature learning and a learnable pooling stage and is trainable end-to-end. Results on a newly introduced dataset of accelerometer signals collected in-the-wild confirm the validity of the proposed approach.

@conference{alpapado2019embc,
author={A. Papadopoulos and K. Kyritsis and S. Bostanjopoulou and L. Klingelhoefer and R. K. Chaudhuri and A. Delopoulos},
title={Multiple-Instance Learning for In-The-Wild Parkinsonian Tremor Detection},
booktitle={2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
year={2019},
month={07},
date={2019-07-23},
url={http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8856314&isnumber=8856280},
doi={https://doi.org/10.1109/EMBC.2019.8856314},
abstract={Parkinson’s Disease (PD) is a neurodegenerative disorder that manifests through slowly progressing symptoms, such as tremor, voice degradation and bradykinesia. Automated detection of such symptoms has recently received much attention by the research community, owing to the clinical benefits associated with the early diagnosis of the disease. Unfortunately, most of the approaches proposed so far, operate under a strictly laboratory setting, thus limiting their potential applicability in real world conditions. In this work, we present a method for automatically detecting tremorous episodes related to PD, based on acceleration signals. We propose to address the problem at hand, as a case of Multiple-Instance Learning, wherein a subject is represented as an unordered bag of signal segments and a single, expert-provided, ground-truth. We employ a deep learning approach that combines feature learning and a learnable pooling stage and is trainable end-to-end. Results on a newly introduced dataset of accelerometer signals collected in-the-wild confirm the validity of the proposed approach.}
}

Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos

41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Berlin, Germany, 2019 Jul

[Abstract][BibTex][pdf]

Automated and objective monitoring of eating behavior has received the attention of both the research community and the industry over the past few years. In this paper we present a method for automatically detecting meals in free living conditions, using the inertial data (acceleration and orientation velocity) from commercially available smartwatches. The proposed method operates in two steps. In the first step we process the raw inertial signals using an End-to- End Neural Network with the purpose of detecting the bite events throughout the recording. During the next step, we process the resulting bite detections using signal processing algorithms to obtain the final meal start and end timestamp estimates. Evaluation results obtained from our Leave One Subject Out experiments using our publicly available FIC and FreeFIC datasets, exhibit encouraging results by achieving an F1/Average Jaccard Index of 0.894/0.804.

@conference{kyritsis2019detecting,
author={Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch},
booktitle={41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
publisher={IEEE},
address={Berlin, Germany},
year={2019},
month={07},
date={2019-07-01},
url={http://mug.ee.auth.gr/wp-content/uploads/kyritsis2019detecting.pdf},
abstract={Automated and objective monitoring of eating behavior has received the attention of both the research community and the industry over the past few years. In this paper we present a method for automatically detecting meals in free living conditions, using the inertial data (acceleration and orientation velocity) from commercially available smartwatches. The proposed method operates in two steps. In the first step we process the raw inertial signals using an End-to- End Neural Network with the purpose of detecting the bite events throughout the recording. During the next step, we process the resulting bite detections using signal processing algorithms to obtain the final meal start and end timestamp estimates. Evaluation results obtained from our Leave One Subject Out experiments using our publicly available FIC and FreeFIC datasets, exhibit encouraging results by achieving an F1/Average Jaccard Index of 0.894/0.804.}
}

Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Honolulu, HI, USA, 2018 Oct

[Abstract][BibTex][pdf]

In this paper, we propose an end-to-end neural network (NN) architecture for detecting in-meal eating events (i.e., bites), using only a commercially available smartwatch. Our method combines convolutional and recurrent networks and is able to simultaneously learn intermediate data representations related to hand movements, as well as sequences of these movements that appear during eating. A promising F-score of 0.884 is achieved for detecting bites on a publicly available dataset with 10 subjects.

@conference{Kiritsis2018,
author={Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
title={End-to-end Learning for Measuring in-meal Eating Behavior from a Smartwatch},
booktitle={40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
publisher={IEEE},
address={Honolulu, HI, USA},
year={2018},
month={10},
date={2018-10-29},
url={http://mug.ee.auth.gr/wp-content/uploads/kyritsis2018end.pdf},
doi={http://%2010.1109/EMBC.2018.8513627},
issn={1558-4615},
isbn={978-1-5386-3647-3},
abstract={In this paper, we propose an end-to-end neural network (NN) architecture for detecting in-meal eating events (i.e., bites), using only a commercially available smartwatch. Our method combines convolutional and recurrent networks and is able to simultaneously learn intermediate data representations related to hand movements, as well as sequences of these movements that appear during eating. A promising F-score of 0.884 is achieved for detecting bites on a publicly available dataset with 10 subjects.}
}

Alexandros Papadopoulos, Konstantinos Kyritsis, Ioannis Sarafis and Anastasios Delopoulos

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Honolulu, HI, USA, 2018 Oct

[Abstract][BibTex][pdf]

Automated monitoring and analysis of eating behaviour patterns, i.e., “how one eats”, has recently received much attention by the research community, owing to the association of eating patterns with health-related problems and especially obesity and its comorbidities. In this work, we introduce an improved method for meal micro-structure analysis. Stepping on a previous methodology of ours that combines feature extraction, SVM micro-movement classification and LSTM sequence modelling, we propose a method to adapt a pretrained IMU-based food intake cycle detection model to a new subject, with the purpose of improving model performance for that subject. We split model training into two stages. First, the model is trained using standard supervised learning techniques. Then, an adaptation step is performed, where the model is fine-tuned on unlabeled samples of the target subject via semisupervised learning. Evaluation is performed on a publicly available dataset that was originally created and used in [1] and has been extended here to demonstrate the effect of the semisupervised approach, where the proposed method improves over the baseline method.

@conference{papadopoulos2018personalised,
author={Alexandros Papadopoulos and Konstantinos Kyritsis and Ioannis Sarafis and Anastasios Delopoulos},
title={Personalised meal eating behaviour analysis via semi-supervised learning},
booktitle={40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
publisher={IEEE},
address={Honolulu, HI, USA},
year={2018},
month={10},
date={2018-10-29},
url={http://mug.ee.auth.gr/wp-content/uploads/papadopoulos2018personalised.pdf},
doi={http://10.1109/EMBC.2018.8513174},
abstract={Automated monitoring and analysis of eating behaviour patterns, i.e., “how one eats”, has recently received much attention by the research community, owing to the association of eating patterns with health-related problems and especially obesity and its comorbidities. In this work, we introduce an improved method for meal micro-structure analysis. Stepping on a previous methodology of ours that combines feature extraction, SVM micro-movement classification and LSTM sequence modelling, we propose a method to adapt a pretrained IMU-based food intake cycle detection model to a new subject, with the purpose of improving model performance for that subject. We split model training into two stages. First, the model is trained using standard supervised learning techniques. Then, an adaptation step is performed, where the model is fine-tuned on unlabeled samples of the target subject via semisupervised learning. Evaluation is performed on a publicly available dataset that was originally created and used in [1] and has been extended here to demonstrate the effect of the semisupervised approach, where the proposed method improves over the baseline method.}
}

Konstantinos Kyritsis, Christina L. Tatli, Christos Diou and Aanastasios Delopoulos

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2843-2846, IEEE, Seogwipo, South Korea, 2017 Jul

[Abstract][BibTex][pdf]

Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls. Evaluation is carried out on an annotated dataset of 8 subjects where the proposed method achieves 0:78 precision and 0:77 recall. The evaluation dataset is publicly available at http://mug.ee.auth.gr/intake-cycle-detection/.

@inproceedings{8037449,
author={Konstantinos Kyritsis and Christina L. Tatli and Christos Diou and Aanastasios Delopoulos},
title={Automated analysis of in meal eating behavior using a commercial wristband IMU sensor},
booktitle={2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages={2843-2846},
publisher={IEEE},
address={Seogwipo, South Korea},
year={2017},
month={07},
date={2017-07-01},
url={http://mug.ee.auth.gr/wp-content/uploads/kyritsis2017automated.pdf},
doi={http://10.1109/EMBC.2017.8037449},
abstract={Automatic objective monitoring of eating behavior using inertial sensors is a research problem that has received a lot of attention recently, mainly due to the mass availability of IMUs and the evidence on the importance of quantifying and monitoring eating patterns. In this paper we propose a method for detecting food intake cycles during the course of a meal using a commercially available wristband. We first model micro-movements that are part of the intake cycle and then use HMMs to model the sequences of micro-movements leading to mouthfuls. Evaluation is carried out on an annotated dataset of 8 subjects where the proposed method achieves 0:78 precision and 0:77 recall. The evaluation dataset is publicly available at http://mug.ee.auth.gr/intake-cycle-detection/.}
}

Konstantinos Kyritsis, Christos Diou and Anastasios Delopoulos

New Trends in Image Analysis and Processing -- ICIAP 2017: ICIAP International Workshops, pp. 411-418, Springer International Publishing, Catania, Italy, 2017 Sep

[Abstract][BibTex][pdf]

Unobtrusive analysis of eating behavior based on Inertial Measurement Unit (IMU) sensors (e.g. accelerometer) is a topic that has attracted the interest of both the industry and the research community over the past years. This work presents a method for detecting food intake moments that occur during a meal session using the accelerometer and gyroscope signals of an off-the-shelf smartwatch. We propose a two step approach. First, we model the hand micro-movements that take place while eating using an array of binary Support Vector Machines (SVMs); then the detection of intake moments is achieved by processing the sequence of SVM score vectors by a Long Short Term Memory (LSTM) network. Evaluation is performed on a publicly available dataset with 10 subjects, where the proposed method outperforms similar approaches by achieving an F1 score of 0.892.

@inproceedings{Kyritsis2017ICIAP,
author={Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
title={Food Intake Detection from Inertial Sensors Using LSTM Networks},
booktitle={New Trends in Image Analysis and Processing -- ICIAP 2017: ICIAP International Workshops},
pages={411-418},
publisher={Springer International Publishing},
editor={Battiato, Sebastiano and Farinella, Giovanni Maria and Leo, Marco and Gallo, Giovanni},
address={Catania, Italy},
year={2017},
month={09},
date={2017-09-11},
url={https://mug.ee.auth.gr/wp-content/uploads/madima2017.pdf},
doi={http://10.1007/978-3-319-70742-6_39},
keywords={Food intake;Eating monitoring;Wearable sensors;LSTM},
abstract={Unobtrusive analysis of eating behavior based on Inertial Measurement Unit (IMU) sensors (e.g. accelerometer) is a topic that has attracted the interest of both the industry and the research community over the past years. This work presents a method for detecting food intake moments that occur during a meal session using the accelerometer and gyroscope signals of an off-the-shelf smartwatch. We propose a two step approach. First, we model the hand micro-movements that take place while eating using an array of binary Support Vector Machines (SVMs); then the detection of intake moments is achieved by processing the sequence of SVM score vectors by a Long Short Term Memory (LSTM) network. Evaluation is performed on a publicly available dataset with 10 subjects, where the proposed method outperforms similar approaches by achieving an F1 score of 0.892.}
}