LABVIEW PROGRAM FOR BODY MASS INDEX
BY
OLATUNDE G. A. AND HEHAR G. S.
SUBMITED TO
DR. Timothy McDonald
FUNDAMENTAL OF INSTRUMENTATION IN BIOSYSTEMS ENGINEERING
Auburn University
DECEMBER 2011
1.0 INTRODUCTION
Instrumentation is based on the principle and operation of measuring
instruments that are used in design and configuration of automated systems. Instrumentation
typically includes automated processes which are mainly focusing on the improving of
system productivity, reliability, safety, optimization, and stability. To control the
parameters in a process or in a particular system, devices such as
microprocessors, various analog and digital sensors, programming but their
ultimate aim is to control the parameters of a system.
Fundamentals of instrumentation and control engineering applied in
biological and agricultural systems and processes include time-domain analysis
and frequency response methods. Sensors and actuators in feedback control
systems. The application in the biosystem
engineering includes various sensors and equipment for precision agriculture,
development of the automated gasifiers and pyrolysis systems in bioenergy,
instruments to control the irrigation, food processing, food machinery,
packaging, ingredient manufacturing and control.
The course exposed us to basic of instrumentation
in the Biosystem Engineering and how they are used to develop basic theoretical
programs and their practical application to various issues in design, signal
conditioning, analog devices, data acquisition and sampling, and sensor
applications. The laboratory provide hands on experience with instrumentation
equipment such as oscilloscopes, data acquisition boards, bread boarding
circuits, and other equipments for practical. Additionally the course
introduced students to C++, LabVIEW, Electronic
WorkBench, and Matlab applications in instrumentation. The practical sessions
provided student with opportunity to explore and learn at one's own initiative
and assistance from the coordinator
LabVIEW
has been used for the basic concepts and practice of data acquisition, and more
generally, instrumentation, to undergraduate, graduate, postgraduate
engineering students for their projects. The number of students who are using
LabVIEW has been gradually increased.
Currently, LabVIEW is
focusing on the data acquisition systems: this includes simple chart
recordings, through systems based on programs written in the C programming
language, to recent systems built with LabVIEW. It is the intention that all
engineering students should have a basic knowledge of modern data acquisition
with experience on LabVIEW. The graphical programming environment of LabVIEW is
suitable for students to understand basic concepts of loops, case structures,
etc., in a short period and to generate working programs.
2.0 Problem statement
Body
Mass Index (BMI) now appears to be a widely accepted index for classifying
adiposity in children and adults, however, most medical practitioners still use
old method which involves using a weighing machine and height measuring
equipment especially meter rule and the index computations are manually done
and presented to the patient. Measurement error and sometimes human error
result in wrong computation with its attendant consequences. The precision and
accuracy of the BMI index will assist the patient in proper classification of
his body fat and assist the medical personnel in proper diagnosis and drug
prescription. Therefore the need to develop automated equipment that will
acquire the weight and the height and perform the computations that are needed
and present the result to the patient in hard of soft copy and in very short
time and with high degree of precision and accuracy.
2.1 Proposed
solution
2.1.1 Sensors required
1. Distance
analog ( QT50ULB U-Gage)
2. Weight
sensor U3L Load cell, SER NO 222555, Capacity 10000
2.1.2 Calibration of sensor
The
distance measurement sensor and the weight measurement sensor have their
outputs in voltage thus the need to find the relationship between the voltage
and the measured quantities. About four sample data each were used for the two
sensors, regression analysis was carried out and the regression equations was
estimated as shown in graph 1 and 2.
2.2 Flow chart for the proposed solution
Graph
1: regression analysis of voltage against height
Graph
1: regression analysis of voltage against weight
2.3 Program
overview
The
program was divided into three time frames as listed below (plate 1):
Time frame 1:
the start button: the operator is expected to click on start button to tell the
program to run
Time frame 2:
the program acquired voltage measurement from the sensor (Weight and height
measurement sensors). About 20 samples shall be acquired and send to the next
phase
Time frame 3:
the average of the voltages shall be estimated and converted to real mass
(pound) and height (inches) through regression analysis equation. The body mass
index is estimated through equation one. The result is presented on a scale
visible to the operator.
Plate
1: Block diagram showing the program equations and flow
Plate
2: Presentation of result output
Result and discussion
Six
samples were taking from six different people. The result of the average BMI
index from the program were compared with manual computations, the estimated
error is presented. The error is 2.06 which is small/low which indicate a good
prediction.
BMI Reading 1
|
BMI Reading 2
|
BMI Average
|
Estimated BMI from formula
|
Error
|
Avg. Error
|
|
1
|
25.604
|
25.554
|
25.579
|
27.5
|
1.921
|
2.062875
|
2
|
23.8727
|
25.866
|
24.86935
|
27.5
|
2.63065
|
|
3
|
24.429
|
23.986
|
24.2075
|
26.5
|
2.2925
|
|
4
|
24.153
|
24.1256
|
24.1393
|
26.5
|
2.3607
|
|
5
|
26.1439
|
26.3863
|
26.2651
|
27.85
|
1.5849
|
|
6
|
26.243
|
26.282
|
26.2625
|
27.85
|
1.5875
|
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