physics-comp / health_tracker Goto Github PK

Develop a set of functions that analyze specific health metrics. Keep in mind that we have a function that will parse health data and create a dataframe containing specified health information.

Resting Heart Rate
Develope separate dictionaries based on age and the associated heart beat range correlating to fitness

• 18-25
• 26-35
• 36-45
• 46-55
• 56-65
• 65-100 (suppose to be 65+ however most people rarely live beyond 100)

Now based upon the following table above we can develop the logic needed to analyze fitness in relation to resting heart rate.

The basis of this class is to perform a more in-depth analysis of the XML Health data.

Comments should be more indicative of current code base

The following API will be used to analyze the structure and attributes. XML data rather than perform an analysis of the data to draw health inferences. The primary objective of this API is to create smaller subsets of the overall larger. XML health data being exported from the Apple health app, amongst other functions.

Ensure that we have the following features in the class performing the analysis of our extracted health data.

• Number of biometrics recorded
• Recorded number of entries for each health metric
• Date range for each biometric

Related updates to the Juypter notebook tutorial

Run the following unit tests on the V02 feature

Create a sub-set of data for VO2 Max. The data samples will be drawn from my health data.

Create a class that extracts all relevant metrics from your health_data file.

• List all health biometrics in XML file
• Units/values of each health metric
• Creation date
• Start and end date
• Personal records

Create a function that removes the personalized health biometrics. The format of the data in export.xml may vary depending on the version of IOS.

Calculate the V02 levels of both men and women

Currently, we have a subset of data that we are working with instead of the complete dataset (too large of a dataset). We will create separate XML files containing data from a specific health metric. The benefit of this is testing the functionality of a function while cutting down on the time of computation for testing. In the future, we will use separate XML files to create a database in Postgres SQL, however for now we want the reliability of having the data locally.

We are going to create a separate folder in the directory that will encompass small portions of the following.xml files.

• Resting Heart Rate
• Step Count
• Walking Distance
• Basal Energy Burned
• Flights of Stairs
• Exercise Time
• Resting Heart Rate
• V02 Max
• Walking Heart Rate Average
• Audio Levels
• Walking Double Support
• Six Minute Walking Distance
• Stand Time
• Walking Speed
• Walking Step Length
• Walking Asymmetry
• Sleeping Goal
• Sleep Analysis
• Stand Hour
• Meditation Time
• Low Heart Rate
• Heart Rate Variability

When converting the original healthAPI.py into what is now analysis.py I forgot to incorporate the vital components necessary to making the Analysis class object-oriented.

Track the progress of the API documentation along with documenting the possible use cases of the API.

Use case:
You want to create a data frame that incorporates heart rate, sleep metrics, and VO2 into a singular data frame. We should have a function that concatenates these biometrics into a singular data frame. The function should have the following capabilities.

• Checks to see if we have similar start and ending dates for biometrics of interest.
• Raises an error if the date ranges are not compatible (i.e., start and end dates do not match). I'm sure that this requirement is relevant; however, having biometrics within similar date ranges will yield a more accurate analysis when correlating differing biometrics.
• Once the data frame is created, convert the data frame into a CSV

BMR Definition: Your Basal Metabolic Rate (BMR) is the number of calories you burn as your body performs basic (basal) life-sustaining function. Commonly also termed as Resting Metabolic Rate (RMR), which is the calories burned if you stayed in bed all day. In either case, many utilize the basal metabolic rate formula to calculate their body’s metabolism rate.

Your BMR defines your basal metabolism rate which makes up about 60-70% of the calories we use (“burn” or expend). This includes the energy your body uses to maintain the basic function of your living and breathing body, including:

• The beating of our heart
• Cell production
• Respiration
• The maintenance of body temperature
• Circulation
• Nutrient processing

Your unique metabolism rate, or BMR, is influenced by a number of factors including age, weight, height, gender, environmental temperature, dieting, and exercise habits.

This will enable users not familiar with python scripting to still perform meaningful analysis on their health biometrics.

After partial completion of the API, there should be a printout analyzing health metrics.

Ensure analysis.py is functional by ensuring that each function within the Analysis class.

• exerciseID
• dataRange
• prelimData
• exerciseData

Upon execution of the excerciseData function within healthAPI.py I obtain no errors however, when executing the same function with the same arguments again I obtain the following error.

Code
data = exerciseData('2018-07-19','2018-07-21','HeartRate')

Error

KeyError: '2018-07-19'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)

Update Wiki documentation for VO2 max feature

The following units in this chart are mL oxygen/kg (confirm with the health kit if these are the units used)

Use the link (from Apple Health Kit) for more information. vo2Max Apple Health Kit

Use the following link to get an idea for how to structure the dictionary of functions.
https://stackoverflow.com/questions/9168340/using-a-dictionary-to-select-function-to-execute

Re-organize the file structure of the project to better organize the features and data sub-sets.