BME100 s2016:Group12 W1030AM L3

From OpenWetWare
Jump to: navigation, search
Owwnotebook icon.png BME 100 Spring 2016 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
BME494 Asu logo.png


Name: Ryan Male
Name: Aderonke Adewuyi
Name: Stephanie Vergas
Name: Allan B.Santos
Name: Liz Delgado
Name: Antonio Lopez


Descriptive Statistics

Heart Rate Statistical Comparison

HR (BPM) Gold Std Spree
Mean 98.77 99.15
Std Dev 21.70 24.27
Error 1.253 1.401

Temperature Statistical Comparison

TEMP (°F) Gold Std Spree
Mean 96.65 95.53
Std Dev 1.923 0.8704
Error 0.1068 0.04835


The following graphs represent the averages of the heart rates and temperatures measured by the gold standards and the spree headband. The values for all of the error bars in each graph were determined using the standard deviation of their respective means.



Inferential Statistics

While testing the Spree headband and the gold standards, the two categories that were measured and compared were temperature and heart rate. The Spree headband measured temperature and displayed colors (red, yellow, or blue) in the associated smartphone app that were correlated with different temperature ranges. The golden standard that was used for temperature, on the other hand, was a standard oral thermometer and measured temperature numerically. Throughout the experiment, data was collected from the same individual on both devices in our group, and there were several other groups who carried out the experiment in identical fashion to us. Although there were multiple groups carrying out this experiment, at the end of the day, all the data was compiled all in one location and grouped into either the Spree headband or gold standard category. Because we needed to compare only these two groupings, a paired t-test made the most sense to use in this situation.

Heart rate, on the other hand, was recorded a more precisely by the Spree headband as it actually measured it numerically instead of giving a general range. The golden standard that was used for recording this body function was the pulse oximeter, and it was measured numerically as well. Like the comparison for temperature, because all the data on heart rate was compiled into two groupings of either the Spree headband or the gold standard, a paired t-test was used.

The Pearson's R correlation for temperature was determined by comparing the measurements displayed by the Spree headband with that of the gold standard. The calculation came out to 0.1928, and because the number is so far from 1, it can be determined that there is little correlation between the data sets.

The Pearson's R correlation for heart rate was also determined by comparing the measurements displayed by the Spree headband with that of the gold standard. The calculation came out to 0.7800, and because the number is close to one, it can be determined that there is a correlation between the data sets.

The charts below show inferential statistics from the experiment:

HEART RATE Gold Std and Spree
T-Test 0.6708
Pearson's R 0.7800
TEMP Gold Std and Spree
T-Test 1.097 x 10^-21
Pearson's R 0.1928

(The t-tests were obtained by using the t.test(array1, array2, 2, 1) formula)

Since the t-test output is equal to the p-value, the p-value for heart rate was .6707, and the p-value for temperature was 1.097 x 10^-21. The p-values allow statistically significant differences in the data sets of each device being tested to be seen. It is stated that a p-value <.05 means that there is a statistically significant difference between two data sets. In this case, comparing the Heart Rate data sets which looked at the data from the Spree headband and the pulse oximeter resulted in a p-value of 0.670767383. This value far exceeds .05, and, as a result, there is no statistical difference between the two data sets. However, when comparing the two data sets containing Spree Headband and Thermometer temperature readings, the p-value is 1.09676E-21. This value is extremely low and far below the required .05 needed to show statistical significance. Therefore, it is reasonable to say there is a statistical difference between the temperature data the Spree Headband gave and the data the oral thermometer gave. This is indicated by the horizontal line and asterisks over both the Spree's mean and the Thermometer's mean in the bar graph.


Although the Spree headband attempts to solve the needs of multiple users, certain flaws exist that make the device difficult to use. One design issue with this device is that it is not the most user friendly. The first flaw noticed early on was that it was difficult to tell whether or not it was turned on. With no light up indicator, it was only possible for the user to know the device was actually on when they were attempting to connect with it over the smartphone app. Providing a light with an on/off toggle switch allows the user to control when the device is turned on and ensure that it is in fact on. This might help to make the device a little easier to use. A light up indicator could also be used to help solve another one of the devices issues in connectivity. While attempting to connect to the device via Bluetooth, it became very difficult to find the right device identification using the app. This was an issue because the device name was a bunch of random letters, numbers, and symbols, and there seemed to be no way to customize it. Allowing the option to customize the device name with something recognizable might help eliminate some of the confusion in connecting, especially when many other Bluetooth products also use similar letter and number jumbles for their connections. Of course, the location that one is trying to connect to the device is key, but with an ever increasing amount of Bluetooth products (especially in gyms), this will continue to be an issue.

To delve even further into the issues with the Spree headband, once connected through the smartphone app, it was not possible to ensure that the connection was successful or not. Again, using a light up indicator on the external device and/or an integrated connection indicator in the smartphone app could help to solve this issue. Specific colors or bars could be used to indicate strength of connection as well. Another issue with this device was that when using the smartphone app, there were all kinds of delays and crashing. It is important for the app to be designed in a way that ensures optimal connection and avoidance of any kind of delay or crash. Lastly, its temperature monitoring was also very imprecise as it only gave different colors to indicate different temperature ranges. This lack of precision even resulted in a statistically significant difference between what the real temperature of the subject would be and what the Spree indicated. Therefore, this design flaw represents a major issue for the reliability of the device in general. A numerical representation of temperature levels would go a much longer way in terms of both precision and accuracy than anything else, and it would be much more helpful if this was incorporated into the design. All in all, there were many, many issues with a device as costly as the Spree headband, and there are many different design aspects that could have resulted in an overall better device.


Target Population and Need

Target Population

The market intended for this device is sevenfold. It has the best value for those that spend a lot of time outdoors, especially for those that have labor-based jobs outside, in the areas of the United States that have relatively hot and dry climates. Because there are even work regulations during certain temperature thresholds that mandate water breaks every certain period of time, this shows that a device monitoring for dehydration would help out tremendously concluding that construction companies can be interested in this device. In the geographical areas that are not predisposed to hot or dry climates, the device caters to those that are cognizant of their health and those that dehydration could be a more serious problem for. It is interesting to note that one of the most common causes of death amongst the elderly is dehydration. A clinical study from early 2015 found that approximately one out of every five seniors is dehydrated at any point in time, and those with dementia, diabetes, and kidney problems are at the highest risk. In summary, the target populations are:

People who are highly active outdoors

Physical laborers

Health and fitness enthusiasts (athletes)

High risk populations: ESRD, dementia, diabetes...etc.

People under the age of 5 or over the age of 65

There are really two approaches that can be taken with this device. It could be intended to cater more heavily towards the market that various other fitness trackers such as Fitbit fall under, or it could address the actual medical problems that arise in various population groups. Obviously, the device will be able to do both, but the marketing strategy should be geared up in one of these two directions. This decision will rest on further market analysis and future prototype capabilities.

Targeted Need

Approximately 35% of people in the United States suffer from chronic dehydration, and this device will help consumers not be a part of the statistic. Water is absolutely essential to life, and so maintaining the proper levels of it will allow for longer, healthier lives. Another reason why the detection of the onset of dehydration is important is because it can prevent serious cases of dehydration which can lead to serious complications, even death.




Device Design

Screen Shot 2016-02-27 at 2.04.59 PM.png

Screen Shot 2016-02-27 at 12.26.16 AM.png

Screen Shot 2016-02-27 at 2.05.16 PM.png

Inferential Statistics

Gold Standard Hydroband
Std Dev 25.58 24.86
Error 3.618 3.516
Averages 41.30 41.16

Pearson's Correlation = 0.9976

T-Test = 0.6017


Screen Shot 2016-03-01 at 11.22.54 PM.png