scholarly journals Patient Food Intake Monitoring System Using Mems Sensor

2021 ◽  
Vol 8 (2) ◽  
pp. 5-9
Author(s):  
Philip Austin M
2020 ◽  
Vol 1650 ◽  
pp. 022037
Author(s):  
Youyu Wu ◽  
Yiyao Xiao ◽  
Hua Ge

nano Online ◽  
2017 ◽  
Author(s):  
Wenbin Yang ◽  
Rebecca Lunn ◽  
Alessandro Tarantino

Author(s):  
Martha Anna Schalla ◽  
Stephanie Gladys Kühne ◽  
Tiemo Friedrich ◽  
Vivien Hanel ◽  
Peter Kobelt ◽  
...  

Author(s):  
Jindong Liu ◽  
Edward Johns ◽  
Louis Atallah ◽  
Claire Pettitt ◽  
Benny Lo ◽  
...  

2018 ◽  
Vol 18 (3) ◽  
pp. 1314-1323 ◽  
Author(s):  
Haonan Wang ◽  
Linxi Dong ◽  
Wei Wei ◽  
Wen-Sheng Zhao ◽  
Kuiwen Xu ◽  
...  

2011 ◽  
Vol 148-149 ◽  
pp. 1021-1024 ◽  
Author(s):  
Wei Dong Huang ◽  
Hong Lv ◽  
Yu Tang Sui

The requirements of environment monitoring system for solid booster of shipboard missile were analyzed, and the scheme of environment monitoring system based on microcontroller and MEMS sensor was present. The hardware units and typical hardware circuits were given. According to the circuits, the PCB board was designed, and the prototype of environment monitoring system was made and tested. It was shown by the test results the designed system can be used for solid booster environment monitoring.


2019 ◽  
Vol 8 (2) ◽  
pp. 470-476 ◽  
Author(s):  
Muhammad Fuad bin Kassim ◽  
Mohd Norzali Haji Mohd

Food intake gesture technology is one of a new strategy for obesity people managing their health care while saving their time and money. This approach involves combining face and hand joint point for monitoring food intake of a user using Kinect Xbox One camera sensor. Rather than counting calories, scientists at Brigham Young University found dieters who eager to reduce their number of daily bites by 20 to 30 percent lost around two kilograms a month, regardless of what they ate [1]. Research studies showed that most of the methods used to count bite are worn type devices which has high false alarm ratio. Today trend is going toward the non-wearable device. This sensor is used to capture skeletal data of user while eating and train the data to capture the motion and movement while eating. There are specific joint to be capture such as Jaw face point and wrist roll joint. Overall accuracy is around 94%. Basically, this increase in the overall recognition rate of this system.


2021 ◽  
pp. 283-305
Author(s):  
Hirofumi Nogami ◽  
Hironao Okada ◽  
Seiichi Takamatsu ◽  
Narifumi Kawano ◽  
Takeshi Kobayashi ◽  
...  

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