This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (888) mHealth for Wellness, Behavior Change and Prevention (2871) Eating Disorders (78) Instruments and Questionnaires for Nutrition and Food Intake (166)

Published on in Vol 6, No 9 (2018): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9781, first published .
The SPLENDID Eating Detection Sensor: Development and Feasibility Study

The SPLENDID Eating Detection Sensor: Development and Feasibility Study

The SPLENDID Eating Detection Sensor: Development and Feasibility Study

Authors of this article:

Janet van den Boer1 Author Orcid Image ;   Annemiek van der Lee1 Author Orcid Image ;   Lingchuan Zhou2 Author Orcid Image ;   Vasileios Papapanagiotou3 Author Orcid Image ;   Christos Diou3 Author Orcid Image ;   Anastasios Delopoulos3 Author Orcid Image ;   Monica Mars1 Author Orcid Image
Article Authors Cited by (13) Tweetations (4) Metrics

Journals

  1. Selamat N, Ali S. Automatic Food Intake Monitoring Based on Chewing Activity: A Survey. IEEE Access 2020;8:48846 View
  2. Liu L, Qiu S, Wang Z, Li J, Wang J. Canoeing Motion Tracking and Analysis via Multi-Sensors Fusion. Sensors 2020;20(7):2110 View
  3. Langlet B, Maramis C, Diou C, Maglaveras N, Fagerberg P, Heimeier R, Lekka I, Delopoulos A, Ioakimidis I. Formative Evaluation of a Smartphone App for Monitoring Daily Meal Distribution and Food Selection in Adolescents: Acceptability and Usability Study. JMIR mHealth and uHealth 2020;8(7):e14778 View
  4. Bin Morshed M, Kulkarni S, Li R, Saha K, Roper L, Nachman L, Lu H, Mirabella L, Srivastava S, De Choudhury M, de Barbaro K, Ploetz T, Abowd G. A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study. JMIR mHealth and uHealth 2020;8(12):e20625 View
  5. Bahador N, Ferreira D, Tamminen S, Kortelainen J. Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors. JMIR mHealth and uHealth 2021;9(1):e21926 View
  6. Choi J, Jeon S, Kim H, Ha J, Jeon G, Lee J, Cho S. Health-Related Indicators Measured Using Earable Devices: Systematic Review. JMIR mHealth and uHealth 2022;10(11):e36696 View
  7. Tufford A, Brennan L, van Trijp H, D'Auria S, Feskens E, Finglas P, Kok F, Kolesárová A, Poppe K, Zimmermann K, van ’t Veer P. A scientific transition to support the 21st century dietary transition. Trends in Food Science & Technology 2023;131:139 View
  8. He Y, Wang X, Chen J. Current Perspectives on Food Oral Processing. Annual Review of Food Science and Technology 2022;13(1):167 View
  9. Rantala E, Balatsas-Lekkas A, Sozer N, Pennanen K. Overview of objective measurement technologies for nutrition research, food-related consumer and marketing research. Trends in Food Science & Technology 2022;125:100 View
  10. Kyritsis K, Diou C, Delopoulos A. Modeling Wrist Micromovements to Measure In-Meal Eating Behavior From Inertial Sensor Data. IEEE Journal of Biomedical and Health Informatics 2019;23(6):2325 View

Books/Policy Documents

  1. Papapanagiotou V, Diou C, van den Boer J, Mars M, Delopoulos A. Pattern Recognition. ICPR International Workshops and Challenges. View