2021 |
Kyritsis, Konstantinos; Fagerberg, Petter; Ioakimidis, Ioannis; Chaudhuri, Ray K; Reichmann, Heinz; Klingelhoefer, Lisa; Delopoulos, Anastasios Scientific Reports, 11 (1), pp. 1–14, 2021. Abstract | Links | BibTeX | Tags: LSTM, Parkinson’s disease, Support vector machines @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} } 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. |
2020 |
Kyritsis, Konstantinos; Fagerberg, Petter; Ioakimidis, Ioannis; Klingelhoefer, Lisa; Reichmann, Heinz; Delopoulos, Anastasios Using IMU sensors to assess motor degradation of PD patients by modeling in-meal plate-to-mouth movement elongation Inproceedings 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 494–497, IEEE 2020. Abstract | Links | BibTeX | Tags: Accelerometers, control, Degradation, Feature extraction, Mouth, PD, Sensors, Support vector machines @inproceedings{kyritsis2020using, title = {Using IMU sensors to assess motor degradation of PD patients by modeling in-meal plate-to-mouth movement elongation}, author = {Konstantinos Kyritsis and Petter Fagerberg and Ioannis Ioakimidis and Lisa Klingelhoefer and Heinz Reichmann and Anastasios Delopoulos}, doi = {10.1109/EMBC44109.2020.9175615}, year = {2020}, date = {2020-01-01}, booktitle = {2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}, pages = {494--497}, organization = {IEEE}, abstract = {Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder after Alzheimer's disease, associated, among others, with motor symptoms such as resting tremor, rigidity and bradykinesia. At the same time, early diagnosis of PD is hindered by a high misdiagnosis rate and the subjective nature of the diagnosis process itself. Recent developments in mobile and wearable devices, such as smartphones and smartwatches, have allowed the automated detection and objective measurement of PD symptoms. In this paper we investigate the hypothesis that PD motor symptom degradation can be assessed by studying the in-meal behavior and modeling the food intake process. To achieve this, we use the inertial data from a commercial smartwatch to investigate the in-meal eating behavior of healthy controls and PD patients. In addition, we define and provide a methodology for calculating Plate-to-Mouth (PtM), an indicator that relates with the average time that the hand spends transferring food from the plate towards the mouth during the course of a meal. The presented experimental results, using our collected dataset of 28 participants (7 healthy controls and 21 PD patients), support our hypothesis. Results initially point out that PD patients have a higher PtM value than the healthy controls. Finally, using PtM we achieve a precision/recall/F1 of 0.882/0.714/0.789 towards classifying the meals from the PD patients and healthy controls.}, keywords = {Accelerometers, control, Degradation, Feature extraction, Mouth, PD, Sensors, Support vector machines}, pubstate = {published}, tppubtype = {inproceedings} } Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder after Alzheimer's disease, associated, among others, with motor symptoms such as resting tremor, rigidity and bradykinesia. At the same time, early diagnosis of PD is hindered by a high misdiagnosis rate and the subjective nature of the diagnosis process itself. Recent developments in mobile and wearable devices, such as smartphones and smartwatches, have allowed the automated detection and objective measurement of PD symptoms. In this paper we investigate the hypothesis that PD motor symptom degradation can be assessed by studying the in-meal behavior and modeling the food intake process. To achieve this, we use the inertial data from a commercial smartwatch to investigate the in-meal eating behavior of healthy controls and PD patients. In addition, we define and provide a methodology for calculating Plate-to-Mouth (PtM), an indicator that relates with the average time that the hand spends transferring food from the plate towards the mouth during the course of a meal. The presented experimental results, using our collected dataset of 28 participants (7 healthy controls and 21 PD patients), support our hypothesis. Results initially point out that PD patients have a higher PtM value than the healthy controls. Finally, using PtM we achieve a precision/recall/F1 of 0.882/0.714/0.789 towards classifying the meals from the PD patients and healthy controls. |
2018 |
Kyritsis, Konstantinos; Diou, Christos; Delopoulos, Anastasios End-to-end Learning for Measuring in-meal Eating Behavior from a Smartwatch Inproceedings 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5511–5514, IEEE 2018. Abstract | Links | BibTeX | Tags: Accelerometers, Gyroscopes, Hidden Markov models, Mouth, Sensors, Support vector machines, Training @inproceedings{kyritsis2018end, title = {End-to-end Learning for Measuring in-meal Eating Behavior from a Smartwatch}, author = {Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos}, doi = {10.1109/EMBC.2018.8513627}, year = {2018}, date = {2018-01-01}, booktitle = {2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, pages = {5511--5514}, organization = {IEEE}, 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.}, keywords = {Accelerometers, Gyroscopes, Hidden Markov models, Mouth, Sensors, Support vector machines, Training}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
Papadopoulos, Alexandros; Kyritsis, Konstantinos; Sarafis, Ioannis; Delopoulos, Anastasios Personalised meal eating behaviour analysis via semi-supervised learning Inproceedings 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4768–4771, IEEE 2018. Abstract | Links | BibTeX | Tags: Adaptation models, Entropy, Feature extraction, Hidden Markov models, Mouth, Support vector machines, Training @inproceedings{papadopoulos2018personalised, title = {Personalised meal eating behaviour analysis via semi-supervised learning}, author = {Alexandros Papadopoulos and Konstantinos Kyritsis and Ioannis Sarafis and Anastasios Delopoulos}, doi = {10.1109/EMBC.2018.8513174}, year = {2018}, date = {2018-01-01}, booktitle = {2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, pages = {4768--4771}, organization = {IEEE}, 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.}, keywords = {Adaptation models, Entropy, Feature extraction, Hidden Markov models, Mouth, Support vector machines, Training}, pubstate = {published}, tppubtype = {inproceedings} } 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. |
2017 |
Kyritsis, Konstantinos; Tatli, Christina Lefkothea; Diou, Christos; Delopoulos, Anastasios Automated analysis of in meal eating behavior using a commercial wristband IMU sensor Inproceedings 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2843–2846, IEEE 2017. Abstract | Links | BibTeX | Tags: Acceleration, Accelerometers, Feature extraction, Gyroscopes, Hidden Markov models, Mouth, Support vector machines @inproceedings{kyritsis2017automated, title = {Automated analysis of in meal eating behavior using a commercial wristband IMU sensor}, author = {Konstantinos Kyritsis and Christina Lefkothea Tatli and Christos Diou and Anastasios Delopoulos}, doi = {10.1109/EMBC.2017.8037449}, year = {2017}, date = {2017-01-01}, booktitle = {2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, pages = {2843--2846}, organization = {IEEE}, 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/.}, keywords = {Acceleration, Accelerometers, Feature extraction, Gyroscopes, Hidden Markov models, Mouth, Support vector machines}, pubstate = {published}, tppubtype = {inproceedings} } 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/. |
Research
2021 |
Scientific Reports, 11 (1), pp. 1–14, 2021. |
2020 |
Using IMU sensors to assess motor degradation of PD patients by modeling in-meal plate-to-mouth movement elongation Inproceedings 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 494–497, IEEE 2020. |
2018 |
End-to-end Learning for Measuring in-meal Eating Behavior from a Smartwatch Inproceedings 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5511–5514, IEEE 2018. |
Personalised meal eating behaviour analysis via semi-supervised learning Inproceedings 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4768–4771, IEEE 2018. |
2017 |
Automated analysis of in meal eating behavior using a commercial wristband IMU sensor Inproceedings 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2843–2846, IEEE 2017. |