BME100 f2013:W1200 Group7 L3

From OpenWetWare
Jump to navigationJump to search
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
Course Logistics For Instructors
Wiki Editing Help


Name: Zack Silverman
Name: Thalia Lebratti
Name: Matthew Campion
Name: Carlee Farhar
Name: Ambar Khare
Name: student


Descriptive Statistics

Oral Temperature and Sensor Temperature

In the first experiment, we analyzed the temperature (oral and skin sensor app) of a human subject. The control (oral temperature of subject), had an average temperature of 97.51227273 and had a standard deviation of 1.19177037. The variable change (sensor temperature) had an average temperature of 96.55568182 and a standard deviation of 1.086157199. Both the groups had an endpoint of 88 The standard error that the control group had was 0.127043148 and the standard error the experimental variable had was 0.115784746. Also the Pearson r value came up as -0.082297737.


The above data was obtained through comparative use of an oral thermometer and the sensor being tested. The average of each was then calculated and graphed, using the standard deviation to formulate the error bars.

Contributed by Ambar Khare and Carlee Farhar.


After performing inferential statistics, we found that the new body temperature/sensor iphone app called Vitals Monitor with the RAIING device was not an effective product. Our experimental control was the measurement of temperature from the new device and our control was the oral thermometer. Since we had two variables to compare for accuracy, we performed the T-Test and got a p-value of 9.75162E-08. This p-value is less than 0.05 which means that the two data sets were statistically different. However, we wanted the p-value above 0.05 because then that would tell us that the data sets were close to each other indicating high accuracy of the new device. Since the p-value was less than 0.05, the temperatures readings were not close to the readings of the thermometer. Also, we found the Pearson's r correlation to be -0.082297737. This means the device readings and temperature readings were negatively correlated showing no similarities between the data sets. In order to know that the device was an effective product, the Pearson's r value should have been really close to 1.


Summary After the completion of our group experiment, we discovered that the new device was ineffective. The new device was supposed to give temperature readings with a correlation of close to 1 for the Pearson's r value when compared to the thermometer for body temperature. The T Test showed p value was less than 0.05. This proves there was a statistical difference meaning the two date sets were in different ranges whereas we were looking for the exact same readings, or at least close to the same readings. There was problem with one group getting the data. Their device failed to collect any readings. We found out that these readings could have been off due to design flaws. We listed these problems:

Flaws and Recommendations
Flaws: Some of flaws with the vitals monitor app and sensor include the sensor's inconsistency, issues with bluetooth connectivity and issues securing the sensor to the body.
Sensor Inconsistency: The sensor's accuracy varies depending on the surrounding environment (i.e. colder/hotter environments threw off sensor)
Bluetooth connectivity: When in in the vicinity of other sensors, it was difficult to connect to the correct sensor. Also, throughout the study, the sensor's connection to the phone would fade in and out resulting in periods of time when data was not being recorded.
Securing the sensor: There were issues when securing a solid connection to the body. Extra tape was required to attach the sensor securely and receive accurate data.
Recommendations: To improve the product moving forward, manufacturers could improve the sensor so that it only records the temperature of the skin that its in contact with. This could mean supplying better insulation from outside conditions and/or creating a higher quality sensor. Another improvement that could be made to the system is giving every sensor a unique bluetooth label. This would solve the issue with connecting to the sensor when there are multiple sensors present. Lastly, the sensor should come packaged with an armband-like product in order to better secure the sensor under the arm, that way the sensor can have better contact with the skin, and keep out the surounding temperatures.


Target Population and Need

The target population includes those who are interested in keeping better track of their general health (mainly adults), as well as those with any mild blood-pressure-related, cardiovascular, respiratory, and/or weight-related health issues. There are special modifications for the Shock Sock that are aimed specifically toward diabetics, the elderly, athletes, and infants. The main target population is the adults with health risks. The Shock Sock is focused on saving lives of all sorts of people. As said before it is good for athletic orientated people, infants, and adults. The Shock Sock is meant to provide an affordable, convenient way to measure vitals (and other possibly necessary things, such as speed and distance for serious athletes).

The Shock Sock will save lives in a cheap and affordable way.

Contributed by Matthew Campion.

Device Design

Shock Sock: A sock that does more than just keep your feet warm. The shock sock measures everything from your temperature to your blood pressure.
-Bluetooth connectivity: The sock sock links up to your the shock sock app on your smartphone phone via bluetooth. Each Shock Sock comes with a unique bluetooth identifier so that it does not get confused with any other pair of shock socks that may be nearby.
-Temperature: using sensors placed in the sock, the Shock Sock will keep a constant communication with the Shock sock app on your smartphone.
-Blood Pressure/Heart rate: Using sensors wrapped around the users toes, the shock sock keeps track of vitals such as blood pressure and heart rate. The sock is set to alert the user if either of the two hit critical levels.
-GPS: For athletes, the Shock Sock has an athletic package which comes with a built in GPS. The GPS tracks the distance traveled and then records the data on your smartphone, so that the user can keep track of their runs, along with all of their vitals throughout the run.

Inferential Statistics

The inferential data above clearly shows that the shock sock is an effective product based on various experiments. The RAIIN device has no competition compared to our new product, the Shock Sock. From our data, we can see that our device not only measures temperature but all sorts of health tests. For every health test, we used the Shock Sock to compare to a device that was specifically designed to measure a health test (ex. Echo-cardiograph for heart rate or thermometer for temperature…). We then graphed scatter plots for all the different types of tests comparing our product with the other product and found the Pearson’s r value. For all of the comparisons, we got a correlation of 1 (or very close to one) which proves that the data provided by both products are the same and therefore confirmed that our product was accurate in measurement. We took the standard deviation of each data set and for each experiment, the standard deviation came to be same for both devices meaning there was little between group variance. We also performed T.Tests for every comparison and got a value that was greater than .05 which told us that there was no statistical difference between the two data sets. This again proved our product had high accuracy since the data was the same.

Contributed by Carlee Farhar, Thalia Lebratti, and Ambar Khare


Contributed by Carlee Farhar, Thalia Lebratti, and Ambar Khare