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
In: 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.
2019
Kyritsis, Konstantinos; Diou, Christos; Delopoulos, Anastasios
Modeling wrist micromovements to measure in-meal eating behavior from inertial sensor data Journal Article
In: IEEE journal of biomedical and health informatics, 23 (6), pp. 2325–2334, 2019.
Abstract | Links | BibTeX | Tags: Acceleration, Feature extraction, Informatics, Monitoring, Mouth, Obesity, Wrist
@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}
}
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/.
2018
Kyritsis, Konstantinos; Diou, Christos; Delopoulos, Anastasios
End-to-end Learning for Measuring in-meal Eating Behavior from a Smartwatch Inproceedings
In: 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
In: 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
In: 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/.