BME100 s2014:W Group3 L3

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BME 100 Fall 2013 Home
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Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6
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OUR TEAM

Name: Ana Kenia Viera
Name: Lorelei Fyfe
Name: Joseph Roskop
Name: Ben Lenschow
Name: Adnan Alsharif


LAB 3A WRITE-UP

Blood pressure cuff and watch sensor

Average: Blood Pressure Cuff: 118.5 Omcron Watch Sensor: 112.3

Standard Deviation: Blood Pressure Cuff:18.1 Omcron Watch Sensor:13.5

T-Test Results: 3.720 EE -6

Pearson's R Correlation: .0725


Pulse and Watch Sensor

Average: Omcron Watch Sensor: 87.9 Pulse Oximeter: 86.7

Standard Deviation: Omcron Watch Sensor: 13.9 Pulse Oximeter: 16.2

T-Test Results: .3516

Pearson's R Correlation: .3047

Oral thermometer and RAIIN sensor: Average: RAIIN sensor: 96.5 Oral Thermometer:97.1

Standard Deviation: RAIIN sensor: 2.03 Oral Thermometer: 1.08

T-Test Results: 1.281 EE -5

Pearson's R Correlation: .05958

Results




Analysis

Blood Pressure: Our T-test provided a result of 3.720 EE -6 which is .000003720 which is drastically smaller than our alpha value of .05 meaning that there is a statistical difference between the standard of arm blood pressure meter and the wrist gauge. Our pearson correlation is .0725 which makes senses showing very little relationship between the bicep gauge and the wrist gauge.


Pulse: Our t-test value was .3516 which is clearly greater than the .05 value meaning there is no statistical difference in pulse reading between the omcron watch sensor and the pulse oximeter and our pearson correlation value of .3047 shows there is also a positive relationship although not that strong.


Temperature: Finally the T-test value of 1.281 EE -5 which is .00001281 which is severely lower than the .05 value means there is a statistical difference in the raiin sensor and the standard oral thermometer. We also got a Pearson correlation value of .05958 which also backs our T-test value showing there is very little correlation between the raiin sensor and the oral thermometer.


All of our T-test were paired T-test since both measurments were taken at the same time intervals. We didnt use an ANOVA test since there was only two factors being tested in all devices.

Summary/Discussion

All in all, the pulse test was the only one to be found as a valid device since our T-test value was so high. The Omcron device was tested to be statistically significant to test for pulse but not blood pressure. This makes senses since the pulse is tested by hand in the wrist on occasion. The wrist is too far from the heart and is difficult to test for blood pressure because of this reason. The bicep cuff is a more valid way of testing blood pressure since the bicep has a stronger flow of blood. The Raiin device was also found to be invalid since it is both difficult to get a reading from the armpit area and it is difficult to get to connect to bluetooth and our group personally had difficulty making a connection between the iPhone and Raiin device. Our T-test and Pearson correlation were so low it backed this finding. If we were able to get more accurate readings from the Raiin device it might have been found to be a valid device but since the data from the entire class showed very little similarties, we thought even if we did have a good connection, we would have gotten data similar to the rest of the class. Possible error includes not close enough connection to the skin with the Omcron and Raiin devices and not being able to get readings from the Raiin device everytime. Also we might not have gotten enough variability in our tests with all of the devices.




LAB 3B WRITE-UP

Target Population and Need

We are targeting athletes and people who would like to increase their physical acuity by accurately monitoring caloric intake. This device will help people in need of consistent and personalized nutritional or dietary information. Moreover, it will provide faster quantitative readings for kCal, sugar, and fat than current blood tests require.

Device Design

Front view Description of image

Diagonal View Description of image

Inferential Statistics




Graph