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

Wearable Devices and Sensors (887) New Methods (943) Imaging Informatics (193) Machine Learning (1459) Instruments and Questionnaires for Nutrition and Food Intake (165) Evaluation and Research Methodology for mHealth (207) mHealth for Wellness, Behavior Change and Prevention (2867)

Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21926, first published .
Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors

Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors

Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors

Authors of this article:

Nooshin Bahador1 Author Orcid Image ;   Denzil Ferreira1 Author Orcid Image ;   Satu Tamminen1 Author Orcid Image ;   Jukka Kortelainen1 Author Orcid Image
Article Authors Cited by (18) Tweetations (4) Metrics

Journals

  1. Sempionatto J, Montiel V, Vargas E, Teymourian H, Wang J. Wearable and Mobile Sensors for Personalized Nutrition. ACS Sensors 2021;6(5):1745 View
  2. Dai L, Guan Y, Wang B, Wang L. Current role and future perspectives of chemometrics in spectroscopic and chromatographic analysis of traditional Chinese medicines. Materials Express 2022;12(2):202 View
  3. Bahador N, Kortelainen J. Deep learning-based classification of multichannel bio-signals using directedness transfer learning. Biomedical Signal Processing and Control 2022;72:103300 View
  4. Bahador N, Zhao G, Jokelainen J, Mustola S, Kortelainen J. Morphology-preserving reconstruction of times series with missing data for enhancing deep learning-based classification. Biomedical Signal Processing and Control 2021;70:103052 View
  5. Asgari S, Fabritius T. Graphene-based dual-functional chiral metamirror composed of complementary 90° rotated U-shaped resonator arrays and its equivalent circuit model. Scientific Reports 2021;11(1) View
  6. Neves P, Simões J, Costa R, Pimenta L, Gonçalves N, Albuquerque C, Cunha C, Zdravevski E, Lameski P, Garcia N, Pires I. Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection. Sensors 2022;22(17):6443 View
  7. Bahador N, Jokelainen J, Mustola S, Kortelainen J. Reconstruction of missing channel in electroencephalogram using spatiotemporal correlation-based averaging. Journal of Neural Engineering 2021;18(5):056045 View
  8. Shao W, Min W, Hou S, Luo M, Li T, Zheng Y, Jiang S. Vision-based food nutrition estimation via RGB-D fusion network. Food Chemistry 2023;424:136309 View
  9. Sükei E, de Leon-Martinez S, Olmos P, Artés A. Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation. Internet Interventions 2023;33:100657 View
  10. Lamichhane B, Zhou J, Sano A. Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model. IEEE Journal of Biomedical and Health Informatics 2023;27(7):3246 View
  11. Han Y, Cheng Q, Wu W, Huang Z. DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion. Foods 2023;12(23):4293 View
  12. Saklani A, Tiwari S, Pannu H. Ameliorating multimodal food classification using state of the art deep learning techniques. Multimedia Tools and Applications 2024;83(21):60189 View
  13. Saklani A, Tiwari S, Pannu H. Deep attentive multimodal learning for food information enhancement via early-stage heterogeneous fusion. The Visual Computer 2024 View
  14. Chakraborty K, Ebihara A. Pesticide Biosensors for Multiple Target Detection: Improvement Potential with Advanced Data-processing Methods. Reviews in Agricultural Science 2024;12(0):128 View