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
Papadopoulos, Alexandros; Kyritsis, Konstantinos; Klingelhoefer, Lisa; Bostanjopoulou, Sevasti; Chaudhuri, Ray K; Delopoulos, Anastasios
Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning Journal Article
In: IEEE Journal of Biomedical and Health Informatics, 2019.
Abstract | Links | BibTeX | Tags: Accelerometers, Annotations, Diseases, Feature extraction, Rigidity, Sensors, Standards
@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}
}
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.
Kyritsis, Konstantinos; Diou, Christos; Delopoulos, Anastasios
Detecting meals in the wild using the inertial data of a typical smartwatch Inproceedings
In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4229–4232, IEEE 2019.
Abstract | Links | BibTeX | Tags: Accelerometers, Artificial neural networks, Detection algorithms, Feature extraction, Gyroscopes, Lenses, Monitoring
@inproceedings{kyritsis2019detecting,
title = {Detecting meals in the wild using the inertial data of a typical smartwatch},
author = {Konstantinos Kyritsis and Christos Diou and Anastasios Delopoulos},
doi = {10.1109/EMBC.2019.8857275},
year = {2019},
date = {2019-01-01},
booktitle = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages = {4229--4232},
organization = {IEEE},
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.},
keywords = {Accelerometers, Artificial neural networks, Detection algorithms, Feature extraction, Gyroscopes, Lenses, Monitoring},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
Papadopoulos, Alexandros; Kyritsis, Konstantinos; Bostanjopoulou, Sevasti; Klingelhoefer, Lisa; Chaudhuri, Ray K; Delopoulos, Anastasios
Multiple-instance learning for in-the-wild parkinsonian tremor detection Inproceedings
In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6188–6191, IEEE 2019.
Abstract | Links | BibTeX | Tags: Accelerometers, Diseases, Mathematical model, Sensors, Smart phones, Standards, Training
@inproceedings{papadopoulos2019multiple,
title = {Multiple-instance learning for in-the-wild parkinsonian tremor detection},
author = {Alexandros Papadopoulos and Konstantinos Kyritsis and Sevasti Bostanjopoulou and Lisa Klingelhoefer and Ray K Chaudhuri and Anastasios Delopoulos},
doi = {10.1109/EMBC.2019.8856314},
year = {2019},
date = {2019-01-01},
booktitle = {2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
pages = {6188--6191},
organization = {IEEE},
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.},
keywords = {Accelerometers, Diseases, Mathematical model, Sensors, Smart phones, Standards, Training},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
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.
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/.