Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Records 1 - 8 / 8

  • help
  • print

    Print search results

  • export

    Export search results

  • alert
    We will mail you new results for this query: q=Delopoulos
Check title to add to marked list
The SPLENDID chewing detection challenge
Delopoulos, A.D. ; Papapanagiotou, V.P. ; Diou, C.D. ; Zhou, L.Z. ; Boer, J.H.W. van den; Mars, M. - \ 2017
This dataset contains approximately 60 hours of recordings from a prototype chewing detection system. The sensor signals include photoplethysmography (PPG) and processed audio from the ear-worn chewing sensor, and signals from a belt-mounted 3D accelerometer. The recording sessions include 14 participants and were conducted in the context of the EU funded SPLENDID project, at Wageningen University, The Netherlands, during the summer of 2015. The purpose of the dataset is to help develop effective algorithms for chewing detection based PPG, audio and accelerometer signals.
The SPLENDID chewing detection challenge (Version 2)
Delopoulos, A.D. ; Papapanagiotou, V.P. ; Diou, C.D. ; Zhou, L.Z. ; Boer, J.H.W. van den; Mars, M. - \ 2017
This dataset contains approximately 60 hours of recordings from a prototype chewing detection system. The sensor signals include photoplethysmography (PPG) and processed audio from the ear-worn chewing sensor, and signals from a belt-mounted 3D accelerometer. The recording sessions include 14 participants and were conducted in the context of the EU funded SPLENDID project, at Wageningen University, The Netherlands, during the summer of 2015. The purpose of the dataset is to help develop effective algorithms for chewing detection based PPG, audio and accelerometer signals.
The SPLENDID chewing detection challenge
Papapanagiotou, Vasileios ; Diou, Christos ; Zhou, Lingchuan ; Boer, Janet van den; Mars, Monica ; Delopoulos, Anastasios - \ 2017
In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Proceedings. - Institute of Electrical and Electronics Engineers Inc. - ISBN 9781509028092 - p. 817 - 820.

Monitoring of eating behavior using wearable technology is receiving increased attention, driven by the recent advances in wearable devices and mobile phones. One particularly interesting aspect of eating behavior is the monitoring of chewing activity and eating occurrences. There are several chewing sensor types and chewing detection algorithms proposed in the bibliography, however no datasets are publicly available to facilitate evaluation and further research. In this paper, we present a multi-modal dataset of over 60 hours of recordings from 14 participants in semi-free living conditions, collected in the context of the SPLENDID project. The dataset includes raw signals from a photoplethysmography (PPG) sensor and a 3D accelerometer, and a set of extracted features from audio recordings; detailed annotations and ground truth are also provided both at eating event level and at individual chew level. We also provide a baseline evaluation method, and introduce the 'challenge' of improving the baseline chewing detection algorithms. The dataset can be downloaded from http: //dx.doi.org/10.17026/dans-zxw-v8gy, and supplementary code can be downloaded from https://github. com/mug-auth/chewing-detection-challenge.git.

A Novel Chewing Detection System Based on PPG, Audio, and Accelerometry
Papapanagiotou, Vasileios ; Diou, Christos ; Zhou, Lingchuan ; Boer, Janet van den; Mars, Monica ; Delopoulos, Anastasios - \ 2017
IEEE Journal of Biomedical and Health Informatics 21 (2017)3. - ISSN 2168-2194 - p. 607 - 618.
Acoustic sensors - acoustic signal processing - bioinformatics - biomedical informatics - optical sensors - optical signal processing
In the context of dietary management, accurate monitoring of eating habits is receiving increased attention. Wearable sensors, combined with the connectivity and processing of modern smartphones, can be used to robustly extract objective and real-time measurements of human behavior. In particular, for the task of chewing detection, several approaches based on an in-ear microphone can be found in the literature, while other types of sensors have also been reported, such as strain sensors. In this paper, performed in the context of the SPLENDID project, we propose to combine an in-ear microphone with a photoplethysmography (PPG) sensor placed in the ear concha, in a new high accuracy and low sampling rate prototype chewing detection system. We propose a pipeline that initially processes each sensor signal separately, and then fuses both to perform the final detection. Features are extracted from each modality, and support vector machine (SVM) classifiers are used separately to perform snacking detection. Finally, we combine the SVM scores from both signals in a late-fusion scheme, which leads to increased eating detection accuracy. We evaluate the proposed eating monitoring system on a challenging, semifree living dataset of 14 subjects, which includes more than 60 h of audio and PPG signal recordings. Results show that fusing the audio and PPG signals significantly improves the effectiveness of eating event detection, achieving accuracy up to 0.938 and class-weighted accuracy up to 0.892.
A novel approach for chewing detection based on a wearable PPG sensor
Papapanagiotou, Vasileios ; Diou, Christos ; Zhou, Lingchuan ; Boer, Janet van den; Mars, Monica ; Delopoulos, Anastasios - \ 2016
In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - Institute of Electrical and Electronics Engineers Inc. - ISBN 9781457702204 - p. 6485 - 6488.

Monitoring of human eating behaviour has been attracting interest over the last few years, as a means to a healthy lifestyle, but also due to its association with serious health conditions, such as eating disorders and obesity. Use of self-reports and other non-automated means of monitoring have been found to be unreliable, compared to the use of wearable sensors. Various modalities have been reported, such as acoustic signal from ear-worn microphones, or signal from wearable strain sensors. In this work, we introduce a new sensor for the task of chewing detection, based on a novel photoplethysmography (PPG) sensor placed on the outer earlobe to perform the task. We also present a processing pipeline that includes two chewing detection algorithms from literature and one new algorithm, to process the captured PPG signal, and present their effectiveness. Experiments are performed on an annotated dataset recorded from 21 individuals, including more than 10 hours of eating and non-eating activities. Results show that the PPG sensor can be successfully used to support dietary monitoring.

SPLENDID: A new mobile tool for weight management
Boer, J.H.W. van den; Lee, Annemiek van der; Maramis, C. ; Diou, C. ; Ioakeimidis, I. ; Lekka, I. ; Zhou, Lingchuan ; Maglaveras, N. ; Bergh, C. ; Delopoulos, A. ; Mars, M. - \ 2016
Appetite 107 (2016). - ISSN 0195-6663 - p. 678 - 678.
Mobile technologies targeting both eating and physical activity behavior provide new opportunities for weight management, but for data on eating behavior these rely on self-report. This results in unreliable information and is burdensome for the user. SPLENDID, an EU project, aims to take the next step in the development of these mobile technologies and is currently developing a wearable personal coach; i.e. a system that will actively monitor eating and physical activity behavior and provides real-time feedback. For the system to be implemented successfully it is essential that it is accepted by consumers and health professionals. While still under development it was investigated how the system is perceived by them. The system was discussed in a focus group with five females (age: 19–24 years) interested in weight management. Nineteen overweight young adults completed a questionnaire on their experiences with the system after 5 hours of using and wearing the system. Furthermore, twelve health professionals were interviewed on the system. Overall the system was perceived as very promising. The objective measurements of both eating and physical activity behavior, and real-time feedback were especially appreciated. However, it was emphasized that the system should be discrete, user-friendly and comfortable for the consumer, and saves the health professional time. These results of the focus group, interviews and questionnaire on user experience are promising and are being taken into account in the further development of the system.
Fractal Nature of Chewing Sounds
Papapanagiotou, V. ; Diou, C. ; Lingchuan, Z. ; Boer, J.H.W. van den; Mars, M. ; Delopoulos, A. - \ 2015
In: New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops / Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C., Springer International Publishing (Lecture Notes in Computer Science 9281) - ISBN 9783319232218 - p. 401 - 408.
monitoring has been investigated by many researchers. For this purpose, one of the most promising modalities is the acoustic signal captured by a common microphone placed inside the outer ear canal. Various chewing detection algorithms for this type of signals exist in the literature. In this work, we perform a systematic analysis of the fractal nature of chewing sounds, and find that the Fractal Dimension is substantially different between chewing and talking. This holds even for severely down-sampled versions of the recordings. We derive chewing detectors based on the the fractal dimension of the recorded signals that can clearly discriminate chewing from non-chewing sounds. We experimentally evaluate snacking detection based on the proposed chewing detector, and we compare our approach against well known counterparts. Experimental results on a large dataset of 10 subjects and total recordings duration of more than 8 hours demonstrate the high effectiveness of our method. Furthermore, there exists indication that discrimination between different properties (such as crispness) is possible.
Preventing Obesity and Eating Disorders through Behavioural Modifications: the SPLENDID Vision
Maramis, C. ; Diou, C. ; Ioakeimidis, I. ; Lekka, I. ; Dudnik, G. ; Mars, M. ; Maglaveras, N. ; Bergh, C. ; Delopoulos, A. - \ 2014
In: Proceedings 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming healthcare through innovations in mobile and wireless technologies. - Institute of Electrical and Electronics Engineers Inc. - ISBN 9781631900143 - p. 7 - 10.
Recent intensive research in the fields of obesity and eating disorders has proved most traditional interventions inadequate: The obesity-targeting interventions have either failed or are strongly social context dependent, while the interventions for eating disorders have poor results and high levels of relapse. On the contrary, recent randomized control trials have illustrated that supervised training of patients to eat and move in a non-pathological way is effective in the prevention of both obesity and eating disorders. Applying the same kind of methodologies to the general population in real life conditions for prevention purposes comes as the logical next step. SPLENDID is a recently initiated EU-funded collaborative project that intends to develop a personalised guidance system for helping and training children and young adults to improve their eating and activity behaviour. By combining expertise in behavioural patterns with current advancements in intelligent systems and sensor technologies, SPLENDID is going to detect subjects at risk for developing obesity or eating disorders and offer them enhanced monitoring and guidance to prevent further disease progression. Both behavioural data collection and system evaluation are going to be performed via pilot studies supported by expert health professionals.
Check title to add to marked list

Show 20 50 100 records per page

 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.