BME100 f2017:Group11 W0800 L3

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OUR TEAM

Name: Alexa Ng
Name: Braeden Malotky
Name: Connor Leicken
Name: Keiko Ochoa
Name: Mauro Robles Garcia

LAB 3 WRITE-UP

Descriptive Statistics

Heart Rate Golden Standard (BPM) Heart Rate Spree (BPM) Temperature Golden Standard (°F) Temperature Spree (°F)
Mean 96.6472 95.5309 98.0898 98.9538
Standard Deviation 1.9226 0.8712 23.0205 24.8996



Inferential Statistics


We chose to use an unpaired T-Test to identify the correlation between the values produced by the 'golden standard' and the spree headband for both temperature and heart rate. This was done using within Google sheets to find the resultant p-value when comparing the Spree Headband to the golden standard.

Graphing

Spree heart rate vs golden.PNG

Spree temperature vs golden.PNG



Summary of Results


Our null hypothesis for this experiment was that there is no significant correlation between the Spree Headband's temperature and heart rate values compared to the golden standard. For the temperature test, our calculated P-value was < .01, so we can reject the null hypothesis at the .05 level, and for the heart rate test, our calculated P-value was .657, so we can accept the null hypothesis at the .05 level. However, the P-value alone only shows the relationship between the values as a set without taking into consideration the spread, thus further analysis using graphs is necessary. Given the spread of the data and low R-squared values, we can conclude that there is no significant correlation between the values gathered by the Spree headband when compared to the Golden Standard. In the real world, this would signify that the Spree headband does not provide adequately accurate results for personal use as a fitness and biometric tracker.

Experimental Design of our Device

For our experiment, we will conduct multiple trials with a sample size of 30 dummies. In separated trials, we will use our device on different types of fractures. A large sample size will allow for the power of the tests to be higher than a smaller sample size which means our data will have more significance. In the trials, we can measure the approximated healing time of the fracture, the pressure of the device on the limb, the amount of time it takes for the fracture to set, the distance the bone must move to be set, the durability of the device, etc. We can use paired t tests with a traditional splint vs our inflatable splint to see whether our splint is as effective as a traditional one. We can also use a paired t test to determine the difference in force applied to the bone with our splint vs. without a splint to show that our product will actually support the leg.