BME100 s2014:T Group8 L3

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OUR TEAM

 Name: Nicole Plachecki Name: Hooyoung Kim Name: Omar Benitez Name: Callaway Freeland Name: Colton Tucker

LAB 3A WRITE-UP

Descriptive Statistics

Temperature Statistics

Oral Thermometer
Mean:96.629 °F
Standard Deviation: ±1.749
Standard Error: ±0.097

RAINN Sensor and iPhone application
Mean: 97.500 °F
Standard Deviation: ±1.254
Standard Error: ±0.070

A paired t-test was run for the data because the subject was the same for both the standard and experimental measurements.
T-test between Oral Themometer and RAINN sensor= 8.0869E-13; this is less than p=.05, the value necessary to be significant, so the results are statistically significant.
The correlation coefficient for the data was r= 0.048, a weak correlation.

Blood Pressure Statistics

Blood Pressure Cuff
Mean: 120.166 mmHg
Standard Deviation: ±17.764
Standard Error: ±1.079

Omron Watch Sensor
Mean: 113.557 mmHg
Standard Deviation: ±13.611
Standard Error: ±0.827

A paired t-test was for the data because the subject was the same for both the standard and experimental measurements.
T-test between the Blood Pressure Cuff and Omron Watch Sensor= 3.61086E-08; this is lower than p=.05, the value cutoff to be significant, so the results are statistically significant.
The correlation coefficient for the data was r= 0.275, a weak correlation.

Pulse Statistics

Pulse Oximeter
Mean: 85.618
Standard Deviation: ±17.837
Standard Error: ±1.082

Omron Watch Sensor
Mean: 83.316
Standard Deviation: ±16.503
Standard Error: ±1.001

A paired t-test was for the data because the subject was the same for both the standard and experimental measurements.
T-test between the Pulse Oximeter and Omron Watch Sensor= 0.0004; this is lower than p=.05, the value cutoff to be significant, so the results are statistically significant.
The correlation coefficient for the data was r= 0.814, a strong correlation.

Results

Figure 1: The mean temperatures and error for the oral thermometer and the iPhone application sensor.

Figure 2: The mean blood pressures and error for the standard blood pressure cuff and the watch blood pressure measurement.

Figure 3: The mean pulse rates and errors for the pulse ox standard device and the watch pulse measurement.

Summary/Discussion

At the end of the experiment it was clear that the iPhone application was the weakest way of recording the body temperature of a person, with the weakest correlation. The best device to test the body temperature of a patient with the strongest correlation would be the basic oral thermometer. Between the two, the two sets of information had very weak correlation and it was obvious that the thermometer was a much better way of measuring the body temperature of a patient.

A few design flaws were present in the RAINN device. The sensor was on the outside of the subject's body so, unlike the oral thermometer which was enclosed in the mouth during measurement, it was exposed to the environmenta temperatures. It was cool out, which may have been a contributing factor in the lower average temperature recorded using the sensor. Additionally, the RAINN sensor measured body surface temperature and these skin devices have never quite been able to accurately calculate the core temperature based on the skin temperature despite this one being placed in a generally warm location of the underarm.

Between the blood pressure cuff and the watch sensor, there was also a very weak correlation within the sets of data, so it is clearly shown that the blood pressure cuff is a much more reliable way of calculating the blood pressure of a patient rather than the Omron watch sensor.

However, when it comes to recording the pulse, both the Omron watch sensor and the Pulse oximeter were great ways of recording the data, they showed a strong correlation in both of their sets of data and both proved to be very reliable ways of calculating pulse.

LAB 3B WRITE-UP

Target Population and Need

The target population for the product are active teens and adults, athletes, and persons who may have chronic issues with increased heart rates and chronic heat intolerance, such as people with MS. The conveniently small and technical design of our product allows for accurate body readings to be taken for professional and recreational athletics alike. By retrieving and recording a consistent stream of data from the body, athletes are able to reflect on and improve their workouts/activities based on their bodies internal reaction. However, for similar reasons our product is targeted at patients who suffer from diseass and health complications where constant monitoring of the body's vitals is essential for a sustainable and comfortable life. Because the audio experience of our device, when disabled, does not interefe with patients hearing, it is essentially wearable all the time. Paired with a sleek and ergonomic design it allows for the patient to wear our product as if nothing is on their ear at all.

Device Design

Figure 3: The Prototypical Design of BeatS2 Headphones.

Inferential Statistics

Body Temperature A t-test was run for the data from the BeatS2 Thermometer compared to the Gold Standard Oral Thermometer. The value was .16, which is less than the p-value 0.50, which is the requirement to be considered Statistically Significant.
The Pearson's r Correlation between the two sets of data for Body temperature was .98--this is considered a very strong correlation.

Pulse A t-test was run for the data from the BeatS2 Pulse meter compared to the Gold Standard Pulse Oximeter and yielded a value of .02, which is less than the p-value requirement of 0.50 to be considered Statistically Significant.
The Pearson's r Correlation between the two sets of data for Body temperature was a strong .99 correlation.

Graph

Figure 2: The average Body Temperatures recorded by the Oral Thermometer Standard and the BeatS2 Thermometer, with standard error.

Figure 3: The average Pulse recorded by the Pulse Oximiter Standard and the BeatS2 Pulse meter, with standard error.