BME100 f2013:W1200 Group11 L3

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BME 100 Fall 2013 Home
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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Name: Nitish Peela
Name: Shayan Naeini
Name: Meera Doshi
Name: Nathan Dacasin
Name: Angelina Ledesma


Descriptive Statistics


As evident by the results that are shown in the graph, the thermometer was able to deliver more accurate results compared to those from the sensor. The thermometer could possibly deliver these more accurate results because of the closer proximity it is to the body and the actually physical contact it has with the body. The sensor's readings have also fluctuated more than the readings from the thermometer throughout the experiment, giving unreliable readings.

The difference between the sensor's and the oral thermometer's data on the two graphs display that the iPhone app is not very accurate. It lacks reliability and validity. Also, while the oral thermometer generally gave constant readings of Patient r's temperature, the app's sensor fluctuated drastically throughout the experiment. This is represented through the large error bars from the sensor, while the oral thermometer's error bars are not as high.


Pearson's R correlation coefficient is -0.06293. Because r is close to 0, the relationship between the temperatures recorded by the thermometer and the temperatures recorded by the sensor is not a strong linear relationship. This indicates that the sensor is inaccurate because ideally, the temperatures recorded by the sensor would be similar to those recorded by the thermometer.

The p value from the t-test is 3.68 * 10^-11. The small p-value indicates that our results are significant, and that there is a clear difference between the average temperatures recorded by the sensor and the temperatures found with the thermometer.


The correlation coefficient for the class is -.063, which shows that there is no correlation between the sensor and the therometer. From our data on the experiment the temperature from the sensor and the thermometer had a weak correlation due to the variance in the temperatures of both devices. The data is signicant because of the large difference in the values of the thermometer and sensor. After using the Vital Monitor app on iPhone, we discovered a couple of design flaws for the wireless thermometer. The bluetooth on the device did not work properly a majority of the time and it took awhile to finally connect to the iPhone. Out of the entire class two groups experiments failed and were unable to collect any data. Most of the time the connection between the thermometer and the iPhone would disconnect making it harder to record the results of the patient and the temperatures measured by the sensor weren't very accurate. The rectangular structure of the thermometer made it uncomfortable for the patient to have, especially in the underarm location. When the thermometer was wrapped around the patients arm it would constantly fall off, after being on the patient for a extended amount of time. A recommendation for wireless device is to use internet connection, instead of a bluetooth connection for a more reliable network connection. Another recommendation is to make the wireless thermometer a patch or electrode like structure with highly adhesive sticking capabilities along with checking for errors in the device that would cause it to malfunction.


Target Population and Need

Our company Shine will benefit babies, children, and new families all around. Newborns are often difficult to take care of, this being said our thermal pacifier will give a fast and accurate reading of the babies, or child's temperature. Allowing the child to avoid the harsh and invasive rectal exam many must undertake. This will allow the parents to get constant readings of their child's temperature, while the newborn is comfortable and enjoying the sensations of the product.

Device Design

Inferential Statistics

This data reveals the similarity and correlation between the orally taken temperatures and the temperatures found using the pacifier temperature sensor. The mean temperatures taken by each device were similar, and the t-test confirmed that there was no significant difference between the mean temperatures with the high p-value. The high R value showed that there is a strong linear relationship between orally recorded temperatures and temperatures found using the sensor. Overall, this data shows that the pacifier sensor measures temperature accurately.

In contrast, the original sensor device measured temperature very innacurately. The temperatures found using the sensor were extremely different from those found using the thermometer; the R value was close to 0, indicating that there was no relationship between orally recorded and sensor-derived temperatures, and the P-value of the t-test was below 0.05, showing that there was a significant difference between the two measurements. Therefore, it is clear that the pacifier sensor is an improvement from the original sensor device.


This graph shows the linear relationship between the orally taken temperatures and the temperatures taken by the pacifier sensor. The R value, which is .98, very close to 1, shows that there is a strong linear relationship between these two variables. This means that the pacifier measures temperature accurately and reliably.