BME100 s2014:T Group13 L3

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Contents

OUR TEAM

Name: Avery A. Witting
Name: Avery A. Witting
Name: Daniel K. Saunders
Name: Daniel K. Saunders
Name: Mikaela S. Hall
Name: Mikaela S. Hall
Name: Sarah Jane McBryan
Name: Sarah Jane McBryan


LAB 3A WRITE-UP

Descriptive Statistics

Temperature
Oral Therm. (Gold Std.) RAIIN sensor
Setting Mean (°F) Std. Dev. (°F) Std. Error (°F) Mean (°F) Std. Dev. (°F) Std. Error (°F)
First Lab 96.87 1.91 0.22 97.07 1.28 0.15
Outside 96.42 1.70 0.13 97.51 1.36 0.10
Second Lab 96.87 1.65 0.19 97.9 0.74 0.09


Blood Pressure
Cuff (Gold Std.) Omron Watch
Setting Mean
(mmHg, Sys.)
Std. Dev.
(mmHg, Sys.)
Std. Error
(mmHg, Sys.)
Mean
(mmHg, Sys.)
Std. Dev.
(mmHg, Sys.)
Std. Error
(mmHg, Sys.)
Lab 119.01 15.21 1.77 113.96 15.08 1.75
Post Walk 120.02 19.40 1.45 113.50 13.56 1.01


Pulse
Pulse Oximeter (Gold Std.) Omron Watch
Setting Mean (bpm) Std. Dev. (bpm) Std. Error (bpm) Mean (bpm) Std. Dev. (bpm) Std. Eror (bpm)
Lab 84.09 13.52 1.56 82.36 13.52 1.56
Post Walk 88.29 17.45 1.30 95.93 17.54 1.30


Results

Temperature Measurements: RAIIN Sensor and Gold Standard
Temperature Measurements: RAIIN Sensor and Gold Standard
Comparison of RAIIN Sensor to Gold Standard for Each Group
Comparison of RAIIN Sensor to Gold Standard for Each Group
Blood Pressure Measurements: Omron Watch and Gold Standard
Blood Pressure Measurements: Omron Watch and Gold Standard
Comparison of Omron Watch to Gold Standard for Each Group
Comparison of Omron Watch to Gold Standard for Each Group
Pulse: Omron Watch and Gold Standard
Pulse: Omron Watch and Gold Standard
Comparison of Omron Watch to Gold Standard for Each Group
Comparison of Omron Watch to Gold Standard for Each Group

Analysis

Temperature
First Lab Outside Second Lab
Comparison t-Test (p Value) Pearson's R t- Test (p Value) Pearson's R t- Test (p Value) Pearson's R
RAIIN vs. Oral 0.715 0.165 0.0640 -0.0810 0.0188 0.244


Blood Pressure and Pulse
First Lab Outside
Comparison t-Test (p Value) Pearson's R t- Test (p Value) Pearson's R
Cuff vs. Omron 0.0209 0.326 0.0209 0.621
Oximeter vs. Omron 4.49×10-7 0.894 2.83×10-5 0.980


We chose to use a t-test for all cases because we were only comparing 2 data sets for each category.

For temperature, the difference in measurements while in the lab prior to going outside was not significant, with a p-value of 0.715. While outside, the difference was also not significant (p-value=0.0640). However, in the measurements taken upon returning to the lab, there was a significant difference. The p-value was 0.0188.

When measuring blood pressure, the results were found to be significant both inside and outside the lab. Both p-values were 0.0209.

For pulse, all results were significant. For inside the lab, the p-value was 4.49×10-7, while outside the lab, the p-value was 2.83×10-5.

Summary/Discussion

Body temperature was measured with the RAIIN sensor and Vitals Monitor App, and the Oral Thermometer. There are design flaws within each device. The design flaw with the RAIIN sensor/Vitals Monitor App is that the RAIIN sensor measures surface temperature rather than core temperature. This means that the data collected will fluctuate based on the temperature outside rather than the core temperature of the subject. After moving around outside, there was a significant difference measured between the RAIIN Sensor and the oral thermometer. The subjects' internal temperatures had changed from their activity, but the Vitals Monitor App's reading did not reflect that change. To obtain accurate body temperature results, the device must be inside the body rather than outside of the body. Additionally, the RAIIN sensor was uncomfortable for the subject at first. A potential solution for this problem would be to change the shape and size of the sensor. The only apparent flaw with the Monitor App is that it depends on Bluetooth connectivity and the RAIIN sensor. The purpose of the Monitor App is to collect the data from sensor, thus all error attributed to the Monitor App will be caused by the sensor, assuming that the App has been programed appropriately. The design flaw with the Oral thermometer is that it can give faulty readings based on temperature outside or fluids or solids that are consumed by the subject. An example of this is when a subject drinks cold water, the temperature reading will be lower due to heat being transferred to the water by the tissue in the mouth. A design improvement would be creating an oral thermometer that has more surface area to absorb the heat from the mouth. By doing this, it will help to limit the error involved.


For measuring pulse and blood pressure, three devices were used: Omron Watch Sensor, Blood Pressure Cuff, and Pulse Oximeter. It was clear that the Omron watch sensor had design flaws because there was a significant difference between its measurements and those of the gold standard in every case. The main design flaw of the Omron Watch Sensor is that the device can be “tricked” based on the activity the subject is experiencing and where the subject’s arm is. The proper way to use this device is to have the subject’s arm at heart level while not moving. If the device is not in the proper position, it can affect blood pressure and heart rate. A way to fix this design flaw is to have two points at which the device measures. Instead of having one sensor, it would be favorable to have two. If there were two Omron Watch sensors, the devices could measure the pulse and blood pressure from both wrists and average the data collected. This would increase the accuracy of the data being measured and help to eliminate outlier data. Another device that was used to measure blood pressure was the Blood Pressure Cuff. The error in this device is a result of the materials used and the quality in which the device is built. The connection points are extremely weak and allow for air pressure to leak out of the device while blood pressure is being measured. A way to improve this device would be to create fixed connection points so that when pressure is increasing within the cuff, air is not leaking out of the connection points. The final device that was used was the Pulse Oximeter. The design flaw of this device is the quality of the ultra-red and red sensors and receivers. If the sensitivity of the sensor and receiver is low, the accuracy of the data being obtained will also be low. By increasing the sensitivity of the sensors and receivers, a more accurate reading would be obtained.




LAB 3B WRITE-UP

Target Population and Need

Our target population is anyone exposed to sun for extended periods of time. We will focus specifically on ASU students, who spend a lot of time in the sun and tend to be careless with sunscreen reapplication. People, especially students caught up in a game of football or other sports, have a hard time remembering to apply sunscreen and do so repetitively. SunWare acts as a reminder for users to reapply sunscreen. Unlike its competitors at UVSunSense, this device can be used and applied by anyone because of its different shades that account for a myriad of skin pigmentation.

Image:Sunware.PNG‎



Device Design

SunWare is waterproof sticky tape that changes color when it's time for the subject to reapply sunscreen. There are 6 shades available: very fair, fair, medium fair, medium, dark, and very dark. These correspond to the 6 different skin tones recognized by the Fitzpatrick Scale, which is used by dermatologists to classify the response of various skin colors to UV light. SunWare can be placed on any part of the body that will be exposed to the sun for an extended period of time. It is easily removable and will not pull out hair, yet has enough staying power to be worn while sweating or swimming. Since it is sold on a roll (like masking tape), it is easily transportable; there is no problems with airport security on vacations and it can be carried in a backpack by students with ease.

Before being exposed to UV rays: Image:SunWare_skin_tape.PNG‎

After being exposed to UV rays: Image:SunWare_Blue_Tape.jpg‎

Device design
Device design



Inferential Statistics

Before Activity, CXCL5-Associated Protein Levels
Light
Complexion
Medium
Complexion
Dark
Complexion
Control
(ppm)
Experimental
(ppm)
Control
(ppm)
Experimental
(ppm)
Control
(ppm)
Experimental
(ppm)
Mean 1.51 1.50 1.32 1.32 1.15 1.11
Std. Dev. 0.08 0.07 0.08 0.08 0.07 0.07
Std. Err. 0.02 0.02 0.02 0.02 0.02 0.02
p-Value 0.7476 1.0000 0.1755
Pearson's R 0.1638 0.4849 0.0104


After Activity, CXCL5-Associated Protein Levels
Light
Complexion
Medium
Complexion
Dark
Complexion
Control
(ppm)
Experimental
(ppm)
Control
(ppm)
Experimental
(ppm)
Control
(ppm)
Experimental
(ppm)
Mean 1.66 1.48 1.55 1.33 1.37 1.14
Std. Dev. 0.10 0.08 0.11 0.07 0.11 0.06
Std. Err. 0.03 0.02 0.03 0.02 0.03 0.02
p-Value 0.0001 0.0001 0.0001
Pearson's R -0.3728 -0.1841 0.2642

Graph

CXCL5 Expression Before 3 Hrs. Activity in Sun for Multiple Complexions
CXCL5 Expression Before 3 Hrs. Activity in Sun for Multiple Complexions
CXCL5 Expression After 3 Hrs. Activity in Sun for Multiple Complexions
CXCL5 Expression After 3 Hrs. Activity in Sun for Multiple Complexions




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