PMID- 36772693
OWN - NLM
STAT- MEDLINE
DCOM- 20230214
LR  - 20230214
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 3
DP  - 2023 Feb 2
TI  - Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight 
      Measurement for Type-2 Diabetic Mellitus Patients' Nutrition Control.
LID - 10.3390/s23031656 [doi]
LID - 1656
AB  - The management of type 2 diabetes mellitus (T2DM) is generally not only focused 
      on pharmacological therapy. Medical nutrition therapy is often forgotten by 
      patients for several reasons, such as difficulty determining the right 
      nutritional pattern for themselves, regulating their daily nutritional patterns, 
      or even not heeding nutritional diet recommendations given by doctors. Management 
      of nutritional therapy is one of the important efforts that can be made by 
      diabetic patients to prevent an increase in the complexity of the disease. 
      Setting a diet with proper nutrition will help patients manage a healthy diet. 
      The development of Smart Plate Health to Eat is a technological innovation that 
      helps patients and users know the type of food, weight, and nutrients contained 
      in certain foods. This study involved 50 types of food with a total of 30,800 
      foods using the YOLOv5s algorithm, where the identification, measurement of 
      weight, and nutrition of food were investigated using a Chenbo load cell weight 
      sensor (1 kg), an HX711 weight weighing A/D module pressure sensor, and an 
      IMX219-160 camera module (waveshare). The results of this study showed good 
      identification accuracy in the analysis of four types of food: rice (58%), 
      braised quail eggs in soy sauce (60%), spicy beef soup (62%), and dried radish 
      (31%), with accuracy for weight and nutrition (100%).
FAU - Joshua, Salaki Reynaldo
AU  - Joshua SR
AUID- ORCID: 0000-0003-2163-4945
AD  - Department of Electronics, Information and Communication Engineering, Kangwon 
      National University, Samcheok-si 25913, Republic of Korea.
FAU - Shin, Seungheon
AU  - Shin S
AD  - Department of Computer Engineering, Kangwon National University, Samcheok-si 
      25913, Republic of Korea.
FAU - Lee, Je-Hoon
AU  - Lee JH
AUID- ORCID: 0000-0001-9481-2891
AD  - Department of Electronics, Information and Communication Engineering, Kangwon 
      National University, Samcheok-si 25913, Republic of Korea.
FAU - Kim, Seong Kun
AU  - Kim SK
AD  - Department of Liberal Studies, Kangwon National University, Samcheok-si 25913, 
      Republic of Korea.
LA  - eng
GR  - 2022RIS-005/National Research Foundation of Korea/
PT  - Journal Article
DEP - 20230202
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Animals
MH  - Cattle
MH  - Humans
MH  - *Diabetes Mellitus, Type 2
MH  - Diet
MH  - Nutritional Status
MH  - Nutrients
MH  - Eggs
PMC - PMC9920985
OTO - NOTNLM
OT  - artificial intelligence
OT  - diabetes
OT  - image recognition
OT  - nutrition
OT  - smart plate
OT  - weight
COIS- The authors declare no conflict of interest.
EDAT- 2023/02/12 06:00
MHDA- 2023/02/15 06:00
PMCR- 2023/02/02
CRDT- 2023/02/11 01:45
PHST- 2022/12/08 00:00 [received]
PHST- 2023/01/30 00:00 [revised]
PHST- 2023/01/31 00:00 [accepted]
PHST- 2023/02/11 01:45 [entrez]
PHST- 2023/02/12 06:00 [pubmed]
PHST- 2023/02/15 06:00 [medline]
PHST- 2023/02/02 00:00 [pmc-release]
AID - s23031656 [pii]
AID - sensors-23-01656 [pii]
AID - 10.3390/s23031656 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Feb 2;23(3):1656. doi: 10.3390/s23031656.