Hall KD. Challenges of human nutrition research. Science. 2020;367:1298–300.
Shim J-S, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiol Health. 2014;36. https://doi.org/10.4178/epih/e2014009.
Free Calorie Counter, Diet & Exercise Journal | MyFitnessPal.com. https://www.myfitnesspal.com/ (accessed 25 Jun 2018).
Lose It! – Weight loss that fits. https://www.loseit.com/ (accessed 22 Nov 2017).
MyNetDiary – Free Calorie Counter and Diet Assistant. https://www.mynetdiary.com/ (accessed 8 Dec 2020).
Cordeiro F, Epstein DA, Thomaz E, Bales E, Jagannathan AK, Abowd GD, et al. Barriers and negative nudges: exploring challenges in food journaling. Proc SIGCHI Conf Hum Factors Comput Syst CHI Conf. 2015;2015:1159–62.
Gill S, Panda S. A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits. Cell Metab. 2015;22:789–98.
Höchsmann C, Martin CK. Review of the validity and feasibility of image-assisted methods for dietary assessment. Int J Obes. 2020;44:2358–71.
Fontana JM, Higgins JA, Schuckers SC, Bellisle F, Pan Z, Melanson EL, et al. Energy intake estimation from counts of chews and swallows. Appetite. 2015;85:14–21.
Bell BM, Alam R, Alshurafa N, Thomaz E, Mondol AS, Haye Kdela, et al. Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review. Npj Digit Med. 2020;3:1–14.
Hong W, Lee WG. Wearable sensors for continuous oral cavity and dietary monitoring toward personalized healthcare and digital medicine. Analyst. 2020. https://doi.org/10.1039/D0AN01484B.
Doulah A, Mccrory MA, Higgins JA, Sazonov E. A systematic review of technology-driven methodologies for estimation of energy intake. IEEE Access. 2019;7:49653–68.
Doulah A, Farooq M, Yang X, Parton J, McCrory MA, Higgins JA, et al. Meal microstructure characterization from sensor-based food intake detection. Front Nutr 2017;4. https://doi.org/10.3389/fnut.2017.00031.
Amft O, Kusserow M, Troster G. Bite weight prediction from acoustic recognition of chewing. IEEE Trans Biomed Eng. 2009;56:1663–72.
Päßler S, Fischer W-J. Food intake monitoring: automated chew event detection in chewing sounds. IEEE J Biomed Health Inform. 2014;18:278–89.
Alshurafa N, Kalantarian H, Pourhomayoun M, Liu JJ, Sarin S, Shahbazi B, et al. Recognition of nutrition intake using time-frequency decomposition in a wearable necklace using a piezoelectric sensor. IEEE Sens J. 2015;15:3909–16.
Dong Y, Hoover A, Scisco J, Muth E. A new method for measuring meal intake in humans via automated wrist motion tracking. Appl Psychophysiol Biofeedback. 2012;37:205–15.
Bi Y, Lv M, Song C, Xu W, Guan N, Yi W. AutoDietary: A wearable acoustic sensor system for food intake recognition in daily life. IEEE Sens J. 2016;16:806–16.
Bi S, Wang T, Tobias N, Nordrum J, Wang S, Halvorsen G, et al. Auracle: Detecting eating episodes with an ear-mounted sensor. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2018;2:92:1–92:27.
Amft O, Tröster G. Recognition of dietary activity events using on-body sensors. Artif Intell Med. 2008;42:121–36.
Zhang R, Bernhart S, Amft O. Diet eyeglasses: Recognising food chewing using EMG and smart eyeglasses. In: 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 2016, pp 7–12.
Mirtchouk M, Merck C, Kleinberg S. Automated estimation of food type and amount consumed from body-worn audio and motion sensors. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM: New York, NY, USA, 2016, pp 451–62.
Doulah A, Ghosh T, Hossain D, Imtiaz MH, Sazonov E. “Automatic Ingestion Monitor Version 2”—A novel wearable device for automatic food intake detection and passive capture of food images. IEEE J Biomed Health Inform. 2021;25:567–76. https://doi.org/10.1109/JBHI.2020.2995473.
Automated Self-Administered 24-Hour (ASA24®) Dietary Assessment Tool. https://epi.grants.cancer.gov/asa24/ (accessed 22 Nov 2017).
Random permutation of integers – MATLAB randperm. https://www.mathworks.com/help/matlab/ref/randperm.html (accessed 22 Nov 2017).
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol. 1996;58:267–88.
Sammut C, Webb GI (eds.). Leave-one-out cross-validation. In: Encyclopedia of Machine Learning. Springer US: Boston, MA, 2010, pp 600–1.
Wang S, Zhou G, Ma Y, Hu L, Chen Z, Chen Y, et al. Eating detection and chews counting through sensing mastication muscle contraction. Smart Health. 2018;9–10:179–91.
FNDDS: USDA ARS. https://www.ars.usda.gov/northeast-area/beltsville-md/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/ (accessed 22 Nov 2017).
USDA National Nutrient Database for Standard Reference. 2013. www.ars.usda.gov/Services/docs.htm?docid=8964.
Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8:135–60.
Yang X, Doulah A, Farooq M, Parton J, McCrory MA, Higgins JA, et al. Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor. Sci Rep. 2019;9:45.
Fontana JM, Farooq M, Sazonov E. Automatic Ingestion Monitor: A novel wearable device for monitoring of ingestive behavior. IEEE Trans Biomed Eng. 2014;61:1772–9.
Lorenzoni G, Bottigliengo D, Azzolina D, Gregori D. Food composition impacts theaccuracy of wearable devices when estimating energy intake from energy-dense food. Nutrients. 2019;11. https://doi.org/10.3390/nu11051170.
Nicklas T, Islam NG, Saab R, Schulin R, Liu Y, Butte NF, et al. Validity of a digital diet estimation method for use with preschool children. J Acad Nutr Diet. 2018;118:252–60.
Pan Z, Forjan D, Marden T, Padia J, Ghosh T, Hossain D. et al. Improvement of methodology for manual energy intake estimation from passive capture devices. Front Nutr. 2022;9:877775. https://doi.org/10.3389/fnut.2022.877775.