The document describes a system to measure nutrient intake levels using non-invasive sensors. It uses piezoelectric sensors to detect throat movement during eating, ECG sensors to measure calorie intake, and temperature sensors to measure calorie burn. The Raspberry Pi collects and analyzes sensor data, which is stored and displayed on the cloud. This allows continuous monitoring of dietary intake levels using a wearable IoT device.

1. Recognition Of Nutrition Intake
level Using Sensor

2. Abstract
• The main objective of developing this product is to use non-
invasive skin motion detection in the lower trachea to
compute and track the amount of nutrients being ingested.
• We have used piezoelectric sensors to detect skin motion, ECG
sensors to determine the quantity of calories added to our
bodies, and temperature sensors to know the amount of
calories burned in our bodies.
• Raspberry Pi is used to gather and analyse the data from
various sensors, and the results are then saved and displayed
on the cloud. The level of dietary intake can be continuously
observed using wearable IOT device

3. INTRODUCTION
Food intake levels, hydration, ingestion rate, and dietary
choices are all factors known to impact the risk of obesity.
 Diet and physical activity are important lifestyle and
behavioral factors in self-management to prevent many chronic
diseases. Mobile sensors such as accelerometers have been
used in the past to objectively measure physical activity or
detect eating time.
Therefore, we need to look for nutritional information of the
human beings.

4. Objectives
 To develop a non invasive method of measuring nutrient
content of our body of the patients instantly.
Our system is user friendly and easy to acess from world wide.

5. Literature Survey
1.A Novel Method for Measuring Nutrition Intake Based on
Food Image- DOI number: 10.1109/I2MTC.2012.6229581
In this paper, a food nutrition and energy intake recognition
system for medical purposes is proposed.
 This system is built based on food image processing and shape
recognition in addition to nutritional fact tables.
Recently, countless studies suggested that the usage of
technology such as smart phones may enhance the treatments
for obesity and overweight patients.
Via a special technique, the system records a photo of the food
before and after eating in order to estimate the consumption
calorie of the selected food and its nutrients components.
Our system presents a new instrument in food intake measuring
systems which can be useful and effective in obesity
management.

7. 2. Mobile Cloud based System Recognizing Nutrition and
Freshness of Food Image-DIO
number: 10.1109/ICECDS.2017.8389528
This paper, we propose a mobile cloud-based food calorie
measurement framework.
 Their framework provides clients with advantageous and
intelligent mechanisms that permit them to track their food intake
and monitor their calorie count.
 The food recognition technique in our system uses Naive Bayes
training mechanism in a cloud computing environment with
classifier machine learning.
Also this system checks the freshness of the fruit, by using the
image processing techniques. This system improves the accuracy
of calories consumption measurement process.

8. 1

9. 3.S2NI:A mobile platform for nutrition monitoring
systerom spoken data
DOI Number:10.1109/EMBC.2016
In this paper we propose developement and validation of speech
to nutrient information (S2NI).
A Comprehens nutrition monitoring system that combines
speech processing and text mining in a unified platform to extract
nurient information
After converting the voice data to text, we identify food name
and portion size information within the text.
we then tiered matching algorithm to search the food name in
our nutrition database and the to compute calorie intake.

11. 4.Design and implementation food nutrition information
system using SURF andFat secret API
DOI Number:10.1109/ICAMIMIA.2015
In the paper we proposed to make this process smarter,faster
and efficient by developing an android application that can
shows the nutrition information
By just clicking the pictureof the food ,this process include image
processing technique.
The information is displayed in the form of
Calories,fat,carbohydrates and proteins per serving .
By using this user can get the nutritonal informaion simply by
taking a picture of the food.

13. Challenges
 In our model we have used piezoelectric sensor for collecting of
data.
Image processing methods are not used
The existing model does not give the nutrition level of the
human body

14. Flow chart
Temperature
sensor
Piezoelectric
sensor
Analog to
Digital
converter
ECG
sensor
Raspberry Pi
Cloud
computing
Leads

15. RASPBERRY PI
The Raspberry Pi is a credit card-sized single-board computer.
 Raspberry Pi has a Broadcom BCM2835 system on chip
(SoC),which includes an ARM1176JZFS 700 MHz processor,
 Video Core IV GPU ,and was originally shipped with 256
megabytes of RAM, later upgraded (Model B & Model B+) to
512 MB.
Pi 2 Model B runs 6X Faster than the B+, and comes with 1GB
of RAM--that's double the amount of RAM of the previous
model.

16. PIEZOELECTRIC SENSOR:
Piezoelectric Effect
The ability of a piezoelectric material to convert a mechanical
stress into electrical charge is called a Piezoelectric Effect.
The word Piezoelectric derived from the Greek word ‘piezein’
which means to push, press and squeeze.
Piezoelectric effect is reversible effect means when we applied
mechanical stress to the piezoelectric material we get some
electrical charge at output.
 Same as when we feed electrical charge to the sensor it gets
stretch or compresses.

17. ECG SENSOR
The sensor is cost effective board used to
measure the electrical activity of the
heart.
The electrical activity can be charted as
an ECG and output as an analog reading.
The AD8232 is an intergrated signal
conditioning blocks for ECG and
biopotential measurement applications
It is designed to extract, amplify and
filter small biopotential signal presence
of noisy condition.
The AD8232 module breaks out nine
connections from the IC and also provide
with RA,LA,RL and additionally there is
an LED indicator light that will pulsate
to the rhythm of heart beat.

18. WORKING
• The ADC board is coupled to the piezoelectric sensor,
which is positioned in the throat area.
• The 100-minute eating pattern is recorded, and the degree
of food consumption is determined.
• The ECG waveforms are used to map the blood glucose
level, from which our body's nutritional needs (protein,
lipids, and carbohydrates) are deduced.
• The temperature sensor that determines how many
calories are burned. The process can be developed as a
wearable devices and real time analysis can be done
through IOT.
• The aforementioned process is seen in cloud computing,
where it may be seen on a website from anywhere.

19. The prototype model
Raspberry Pi
Analog to Digital
Converter
ECG
Sensor

20. SIMULATION

21. Conclusion
 To develop a non invasive method of measuring nutrient
content of our body of the patients instantly.
 The project calculates the nutrition level by detecting the skin
motion in the lower trachea during ingestion using three main
sensors
 Our data are store in cloud and can be easily acessed from
anywhere.
 The prototype can also be manufactured for daily usage of the
people in day to day life
 It also can be compressed to small like a bracelet or like ring
by using nano mateials .

22. Reference
[1] R. W. Kimokoti and B. E. Millen, “Diet, the global obesity
epidemic, and prevention,” Journal of the American Dietetic
Association, vol.111, no. 8, pp. 1137–1140, 2011.
[2] E. A. Finkelstein, I. C. Fiebelkorn, G. Wang et al., “National
medical spending attributable to overweight and obesity: how
much, and who’s paying?” Health affairs-millwood va then
bethesda ma, vol. 22, no. 3; SUPP, pp. W3–219, 2003.
[3] M. Nestle, Food politics: How the food industry influences
nutrition and health. Univ of California Press, 2013, vol. 3.
[4] L. R. Wilkens and J. Lee, Nutritional epidemiology. Wiley
Online Library, 1998.
[5] K. B. Michels, “A renaissance for measurement error,”
International journal of epidemiology, vol. 30, no. 3, pp. 421–422,
2001.
[6] D. R. Jacobs Jr, “Challenges in research in nutritional
epidemiology,” in Nutritional Health. Springer, 2012, pp. 29–42.

23. Thank you