Main Article Content
Dementia has been an increasing trend with an increase in the population of elderly people, and WHO estimates that the number of individuals with dementia doubles every 20 years. There is no curative solution for dementia, but non-drug approaches can improve patient quality of life.
In nursing homes (NH) almost 86% of patients with advanced dementia have problems eating and they need eating assistance. Patients with cognition impairment sometimes confuse food and they do not know when and how much they should eat and drink, so it leads to dehydration and malnutrition which cause weight loss, infection, decreased quality of life and increased risk of death. So, 24-hours caregivers are needed in this situation, and it is so hard for caregivers.
Patients with dementia can live easier in the familiar environment, so ATs (Assistive Technologies) can help patients and caregivers to live in their own homes if possible. One of the approaches for monitoring eating activity of people with dementia is calculating calorie of food, the aim of this research is working on it.
There are different approaches for measuring calorie of food but most of them depend on the user or they do not consider human value and ethical considerations in their design. Patients with dementia lose their autonomy, so they need an automatic system for calculating calorie of food. The objective of this research is to provide the state of art of the energy food estimation in elderly people with dementia. This study will be a good start for defining our own approaches in the domain.
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
2. Hanson, L.C., et al., Outcomes of feeding problems in advanced dementia in a nursing home population. Journal of the American Geriatrics Society, 2013. 61(10): p. 1692-1697.
3. Timlin, G. and N. RySENbRy, Design for dementia. London, UK: Helen Hamlyn Center, Royal College of Art, 2010.
4. Prince, M., et al., Nutrition and dementia: a review of available research. 2014.
5. Organisation, W.H. Nutrition. Available from: https://www.who.int/health-topics/nutrition.
6. Fostinelli, S., et al., Eating behavior in aging and dementia: The need for a comprehensive assessment. Frontiers in Nutrition, 2020. 7.
7. Buckinx, F., et al., Influence of environmental factors on food intake among nursing home residents: a survey combined with a video approach. Clinical interventions in aging, 2017. 12: p. 1055.
8. Liu, W., Y.L. Jao, and K. Williams, Factors influencing the pace of food intake for nursing home residents with dementia: Resident characteristics, staff mealtime assistance and environmental stimulation. Nursing open, 2019. 6(3): p. 772-782.
9. Fontana, J.M. and E. Sazonov, Detection and characterization of food intake by wearable sensors, in Wearable Sensors. 2014, Elsevier. p. 591-616.
10. J. Loewy, M.m.d.a.c., https://itunes.apple.com/ and a.A. us/app/my-macros-diet-calories/id475249619/, 2019.
11. Y. GmbH, Y.d.a.f.t., https://itunes.apple.com/us/ and a.A. app/calorie-counter-yazio/id946099227/, 2019.
12. “Feel great about what you ate, h.y.c., 2019.
13. “Your personal food and symptom diary, h.c.-a.c. and branch match id=651011669040444429.
14. app, A.; Available from: https://youate.com/.
15. Siek, K.A., et al. When do we eat? An evaluation of food items input into an electronic food monitoring application. in 2006 pervasive health conference and workshops. 2006. IEEE.
16. Huang, Q., Z. Yang, and Q. Zhang. Smart-u: Smart utensils know what you eat. in IEEE INFOCOM 2018-IEEE Conference on Computer Communications. 2018. IEEE.
17. Zhou, B., et al. Smart table surface: A novel approach to pervasive dining monitoring. in 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). 2015. IEEE.
18. Pouladzadeh, P., et al. Food calorie measurement using deep learning neural network. in 2016 IEEE international instrumentation and measurement technology conference proceedings. 2016. IEEE.
19. Pouladzadeh, P., A. Yassine, and S. Shirmohammadi. Foodd: food detection dataset for calorie measurement using food images. in International Conference on Image Analysis and Processing. 2015. Springer.
20. Doulah, A., et al., “Automatic Ingestion Monitor Version 2”—A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images. IEEE Journal of Biomedical and Health Informatics, 2020.
21. Li, J., et al., A personalized voice-based diet assistant for caregivers of Alzheimer Disease and related dementias: System development and validation. Journal of Medical Internet Research, 2020. 22(9): p. e19897.