BME100 f2013:W900 Group16 L3

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Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
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Contents

OUR TEAM

Laura Stanovich
Laura Stanovich
Aakriti Gupta
Aakriti Gupta
Paul Lim
Paul Lim
Meilin Ossanna
Meilin Ossanna
Kevin Liao
Kevin Liao

LAB 3A WRITE-UP

Descriptive Statistics

In order to test the success of the iPhone app and device (RAIING) that was created, data was gathered from 18 different groups. There were two types of data collected: oral data (body temperature readings gathered from thermometers) and sensor data (body temperature readings gathered from the sensor/app). Out of the 18 groups, 3 of the groups had sensors that were not successful and therefore were not able to collect data from. Based on the data from the other 15 groups, it was found that the average body temperature collected orally was: 97.53242. Similarly, the average body temperature readings gathered from the sensor was: 95.08182. Then the standard deviation for both groups (oral readings and sensor readings) were calculated and the results were: 0.844800044 and 1.849185238 respectively. An additional piece of information that was important was the count or the endpoint number which was the number of readings that were gathered. There were 22 values gathered for the oral readings and the sensor readings each. Using the standard deviation and the endpoint number/count, the standard error could be calculated. This gave the value of: 0.180111965 for the oral readings and 0.394247567 for the sensor readings.

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Results

OralvsSensorGraph

The graph above represents the average body temperature readings for the oral and sensor data. It also includes the standard error bars. The average temperature for the oral test was higher than the average for the sensor test. The standard error for the sensor test was higher than the standard error of the oral test.




Analysis

In order to determine whether there was a statistical difference between the oral readings and the sensor readings, a statistical t-test was ran using Microsoft Excel. When this statistical test was ran, the p-value that was calculated was: 2.9459E-80. Putting this in context of the experiment, it could be conclude that there is a statistical significance between the oral data and the sensor data. Now that it was evident that there was a statistical difference the data analysis was taken one step further and the Pearson's r-correlation was calculated to determine whether there was a connection between the two groups. To calculate this value, all of the data was plotted into a scatter plot. The scatter plot was used to determine the R value. The r-value calculated was 0.57504.


OralvsSensorGraph



Summary/Discussion

Results and Statistical Analysis

In order to determine whether there was a statistical difference between the oral readings and the sensor readings, a statistical t-test was ran using Microsoft Excel. When the statistical test, the p-value that was calculated was: 2.9459E-80. Putting this in context of the experiment, it was concluded that there is a statistical significance between the oral data and the sensor data. Now that the results were narrowed down to the fact that there is a statistical significance between the two groups, Pearson's r-correlation was calculated to determine whether there was a connection between the two groups. To calculate this value, all of the data was plotted into a scatter plot and used this information to determine the R value. The R value was 0.330681.

Design Flaws

Upon setting up the RAIING sensor there were significant problems in the syncing process via bluetooth technology. It required pressing the power button on the unit multiple times to get it to connect with the iPhone, and then it would not sustain the connectivity. With multiple units powered in discoverable mode it became difficult to distinguish which device was being used. A recommendation would be to make it so the Bluetooth could connect and stay connect from a broader distance. In this case though, it would be critical that members of different groups did not have their sensors near their peers because it would pick up on their Bluetooth signal. Then, when connected the temperature rose at a slow rate. The device was unable to quickly tell the user what an accurate reading of their temperature was until the sensor had adjusted to skin contact. Therefore in the experiment it was unclear of when to begin recording body temperature off the body. In order to fix this, it would come in handy if the application said exactly when the product was ready to be used and able to be accurate, especially because the sensor has to go from being room temperature to a warm body very quickly. This made it difficult to know when the product could be used.





LAB 3B WRITE-UP

Target Population and Need

This device is meant for monitoring children's temperature. Therefore, the target population is families with younger aged children. The parents would be able to check their child's temperature if they are feeling sick. At night when a child is asleep, parents can use their iPhone to monitor if medication is working and if the child is still in the normal body temperature limits.





Device Design

Company Logo Image:Design!.png

This image displays the front and back of the sensor that will be monitoring temperature. The sensor has a display screen, warning light, and a sound feature. The display screen continuously shows the temperature of the person who is wearing the device. The warning light is always green, unless it goes to a temperature that is out of range. The sound will also alarm if a given temperature is out of range. These settings can be changed with the use of an application on an iPhone. This device will be placed inside an adhesive sleeve. This sleeve will have a backing of mesh inside of the adhesive so that the sensor can properly obtain results with skin contact.



Inferential Statistics

Image:tt.png

This table displays the data collected from the sensor and from an oral thermometer.


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This table displays the descriptive statistics resulting from the data sets. The average temperature for device reading is listed as "average".

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This table lists the inferential data analysis resulting from the data sets. A t-test was conducted to evaluate the significance between the two device reading. the p-value which was calculated from the data collected (the value listed next to "ttest") is 0.99249, which falls far outside the boundaries of the a statistically significant p value. In this case, however, it is important for the p-value not to be statistically significant. Another one of the company's goal is for the sensor to be as accurate as possible to an individual's actual temperature and a statistically significant p-value would indicate that the sensor is providing the wrong results. The Pearson's r value is 0.99413.

Graph

Image:Graphtemp.png

This graph shows the relationship between the temperatures collected from the oral thermometer and sensor. The r correlation is listed at the top right corner of the graph.






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