From 36560a8279f98100799bb9fde8243d281d90b835 Mon Sep 17 00:00:00 2001 From: jrtechs Date: Sat, 4 May 2019 13:55:17 -0400 Subject: [PATCH] Added data visualization stuff to the blog post. --- .../a-closer-look-at-fitbit-data.md | 576 ++++++++++++++++++ blogContent/posts/data-science/fitbit.md | 224 ------- .../media/fitbit/activityHistory.png | Bin 0 -> 70490 bytes .../media/fitbit/fitBitMonthly.png | Bin 0 -> 8960 bytes .../data-science/media/fitbit/fitbitDaily.png | Bin 0 -> 14712 bytes .../media/fitbit/fitbitDailyActivities.png | Bin 0 -> 86981 bytes .../media/fitbit/morningRoutine.png | Bin 0 -> 71050 bytes .../media/fitbit/oneWeekHeartRateGraph.png | Bin 0 -> 77871 bytes 8 files changed, 576 insertions(+), 224 deletions(-) create mode 100644 blogContent/posts/data-science/a-closer-look-at-fitbit-data.md delete mode 100644 blogContent/posts/data-science/fitbit.md create mode 100644 blogContent/posts/data-science/media/fitbit/activityHistory.png create mode 100644 blogContent/posts/data-science/media/fitbit/fitBitMonthly.png create mode 100644 blogContent/posts/data-science/media/fitbit/fitbitDaily.png create mode 100644 blogContent/posts/data-science/media/fitbit/fitbitDailyActivities.png create mode 100644 blogContent/posts/data-science/media/fitbit/morningRoutine.png create mode 100644 blogContent/posts/data-science/media/fitbit/oneWeekHeartRateGraph.png diff --git a/blogContent/posts/data-science/a-closer-look-at-fitbit-data.md b/blogContent/posts/data-science/a-closer-look-at-fitbit-data.md new file mode 100644 index 0000000..5efb4dd --- /dev/null +++ b/blogContent/posts/data-science/a-closer-look-at-fitbit-data.md @@ -0,0 +1,576 @@ +Health trackers are the current craze. After I bought a Fitbit, I +wanted to determine what exactly could I do with my Fitbit data. Can we +learn something from this data that we did not know before? +Most people don't need a watch to tell them that they walked a lot +today or that they got a ton of sleep. We typically have a pretty good +gauge of our basic physical health. I am interested in figuring out +how we can use data science to look at our health data over a longer +period of time and learn something useful. + +Lets first look at a few things that people typically use Fitbit data for +before we jump into the weeds. + +- Setting Goals +- Motivation +- Tracking Progress + +Ever since I bought a Fitbit, I found that I went to the gym a lot +more frequently. Having something which keeps track of your progress +is a great motivator. Not only is your daily steps recorded for your +own viewing, you can share that data with your friends as a +competition. Although I only have one friend on Fitbit, I found that was +a good motivator to hit ten thousand steps per day. + +Goals which are not concrete never get accomplished. Simply +saying that "I will get in shape" is a terrible goal. In order for you +to actually accomplish your goals, they need to be quantifiable, reasonable, and +measurable. Rather than saying "I will improve my health this year", +you can say "I will loose ten pounds this year by increasing my daily +step count to twelve thousand and going to the gym twice a week". One +goal is wishy washy where the other is concrete and measurable. Having +concrete data from Fitbit allows you to quantify your goals and set +milestones for you to accomplish. Along the way to achieving your +goal, you can easily track your progress. + +Simply knowing your Fitbit data can help you make some better educated +decisions about your fitness. By comparing your data against what is +healthy you can tweak your lifestyle. For example: if you notice that +you are only getting 6 hours of sleep per night, you can look up the +recommended amount of sleep and tweak your sleep routine until you hit +that target. + +Alright, lets do some data science! + +![Tom and Jerry Data Science Meme](media/fitbit/dataScience.jpg) + +# Getting The Data + +There are two options that we can use to fetch data from Fitbit. + + +## Using Fitbit's API + +Fitbit has an [OAuth 2.0 web +API](https://dev.fitbit.com/build/reference/web-api/) that you can +use. You first have to register your application on Fitbit's website +to receive a client ID and a client secret. + +I decided to fetch the Fitbit data using an Express app with node. +Fetching the data this way will make it really easy to use on a +live website. Node has tons of NPM modules which makes connecting to +Fitbit's API really easy. I'm using Passport which is a pretty common +authentication middleware for Express. + + +```javascript +/** express app */ +const express = require("express"); + +/** Manages oauth 2.0 w/ fitbit */ +const passport = require('passport'); + +/** Used to make API calls */ +const unirest = require('unirest'); + +/** express app */ +const app = express(); + +app.use(passport.initialize()); +app.use(passport.session({ + resave: false, + saveUninitialized: true +})); + + +var FitbitStrategy = require( 'passport-fitbit-oauth2' ).FitbitOAuth2Strategy; + + +var accessTokenTemp = null; +passport.use(new FitbitStrategy({ + clientID: config.clientID, + clientSecret: config.clientSecret, + callbackURL: config.callbackURL + }, + function(accessToken, refreshToken, profile, done) + { + console.log(accessToken); + accessTokenTemp = accessToken; + done(null, { + accessToken: accessToken, + refreshToken: refreshToken, + profile: profile + }); + } +)); + +passport.serializeUser(function(user, done) { + done(null, user); +}); + +passport.deserializeUser(function(obj, done) { + done(null, obj); +}); + +passport.authenticate('fitbit', { scope: + ['activity','heartrate','location','profile'] +}); +``` + +Since our authentication middlware is all set up, we just need to add +the express routes which are required when authenticating. + +```javascript +app.get('/auth/fitbit', + passport.authenticate('fitbit', { scope: + ['activity','heartrate','location','profile'] } +)); + +app.get( '/auth/fitbit/callback', passport.authenticate( 'fitbit', { + successRedirect: '/', + failureRedirect: '/error' +})); + + +app.get('/error', (request, result) => +{ + result.write("Error authenticating with Fitbit API"); + result.end(); +}); +``` + +Now that we are authenticated with Fitbit, we can finally make +queries. I created a helper function called queryAPI which attempts +to authenticate if it is not already authenticated and then fetches +the API result from a provided URL. + +```javascript +const queryAPI = function(result, path) +{ + return new Promise((resolve, reject)=> + { + if(accessTokenTemp == null) + { + result.redirect('/auth/fitbit'); + resolve(false); + } + + unirest.get(path) + .headers({'Accept': 'application/json', 'Content-Type': 'application/json', Authorization: "Bearer " + accessTokenTemp}) + .end(function (response) + { + if(response.hasOwnProperty("success") && response.success == false) + { + result.redirect('/auth/fitbit'); + resolve(false); + } + resolve(response.body); + }); + }); +}; + +app.get('/steps', (request, result)=> +{ + queryAPI(result, 'https://api.fitbit.com/1/user/-/activities/tracker/steps/date/today/1m.json').then((data)=> + { + if(data != false) + { + result.writeHead(200, {'Content-Type': 'text/html'}); + result.write(JSON.stringify(data)); + result.end(); + } + else + { + console.log("Validating with API"); + } + }); +}); +``` + +## Exporting Data from Website + +On [Fitbit's website](https://www.fitbit.com/settings/data/export) +there is a nice page where you can export your data. + +![Fitbit Website Data Export](media/fitbit/fitbitDataExport.png) + +The on demand export is pretty useless because it can only go back a +month. On top of that, you don't get to download any heart rate data. +The only data that you do get is aggregated by day. This might be fine +for some use cases; however, this will not suffice for any interesting +analysis. + +I decided to try the account archive option out of curiosity. + +![Fitbit Archive Data](media/fitbit/fitbitArchiveData.png) + +The Fitbit data archive was very organized and kept meticulous records +of everything. All of the data was in JSON format and was organized +in separate files labeled by date. Fitbit keeps around 1MB +of data on you per day; most of this data is from the heart rate +sensors. Although 1MB of data may sound like a ton of data, it is probably a +lot less if you store it in a format other than JSON. Since Fitbit +hires a lot of people for hadoop and SQL development, they are most +likely using [Apache Hive](https://hive.apache.org/) to store user +information on the backend. Distributing the data to users as JSON is +really convenient since it makes learning the data schema very easy. + +# Visualizing The Data + +Since the Data Archive is far easier, I'm going to start visualizing the +data retrieved from the JSON archive. In the future I may +use the Fitbit API if I decide to make this a live website or something. +Using R to visualize this would be easy, however; I want to use some +pretty javascript graphs so I can host this as a demo on my website. + +## Heart Rate + +My biggest quirk with the Fitbit website is that it only displays your continuous +heart rate in one day intervals. If you zoom out to the week or month view, it aggregates it +as the number of minutes you are in each heart rate zone. +This is fine for the fitbit app where you have limited screen space and no good ways of zooming in +and out of the graphs. + +![Fitbit Daily Heart Rate Graph](media/fitbit/fitbitDaily.png) + +![Fitbit Monthly Heart Rate Graph](media/fitbit/fitBitMonthly.png) + +I really want to be able to view my heart rate over the course of a +few days. To view my continuous heart rate I'm going to +use [VisJS](http://visjs.org/docs/graph2d/) because +it works really well with time series data. + +This is some Javascript code which imports user selected JSON files +to the web page and parses it as Javascript objects. + +```html +
+ + + +
+... + +``` + +The actual Javascript objects look like this: + +```json +[{ + "dateTime" : "04/22/19 04:00:05", + "value" : { + "bpm" : 69, + "confidence" : 2 + } +},{ + "dateTime" : "04/22/19 04:00:10", + "value" : { + "bpm" : 70, + "confidence" : 2 + } +} +... +] +``` + +I found it interesting that each point had a confidence score associated with it. I wonder +how Fitbit is using that confidence information. Since it does not directly appear anywhere in the app, +they may be using it to exclude inaccurate points from the heart rate graphs to make it smoother. +A really annoying thing about this data is that the time stamps don't contain any information on the +timezone. When graphing this data, I will shift the times by 4 hours so that it aligns +with eastern standard time. + + +After we read the data from the user selected heart rate files, we can treat this object as an array +of arrays. Each array represents a file or an entire days worth of heart rate data. Each day is an +array of time stamped points with heart rate information. Using the code from the +[VisJS example](http://visjs.org/docs/graph2d/), it is relatively straightforward to plot this data. + +```javascript +function generateHeartRateGraph(jsonFiles) +{ + var items = []; + for(var i = 0; i < jsonFiles.length; i++) + { + console.log(jsonFiles[i].length); + for(var j = 0; j < jsonFiles[i].length; j++) + { + var localTime = new Date(jsonFiles[i][j].dateTime); + items.push({y:jsonFiles[i][j].value.bpm, x:localTime.setHours(localTime.getHours() - 4)}); + } + } + var dataset = new vis.DataSet(items); + var options = { + dataAxis: { + showMinorLabels: true, + left: { + title: { + text: "Heart Rate" + } + } + } + }; + var container = document.getElementById("heartRateGraph"); + var graph2d = new vis.Graph2d(container, dataset, options); + graph2d.on('rangechanged', graphMoved); + graphsOnPage.push(graph2d); +} +``` + +It works! As an example, this is what my heart rate looks like over a week. + +![Heart Rate for One Week](media/fitbit/oneWeekHeartRateGraph.png) + + +## Time Line + +Fitbit does a pretty good job of detecting and recording health related activities. +The two major things that Fitbit detects is sleep and workout activities. +Although the app does a good job at informing you about these activities, the app is lacking +a comprehensive timeline. Rather than provide a timeline for these activities, +the app only displays a simple list. + +![Fitbit Activity History Log](media/fitbit/activityHistory.png) + +The JSON files for sleep store a ton of data! For the sake of the time line I am only interested +in the start and finish times. Unlike the heart rate data, this actually stores the time zone. + +```json +[{ + "logId" : 22128553286, + "dateOfSleep" : "2019-04-28", + "startTime" : "2019-04-27T23:09:00.000", + "endTime" : "2019-04-28T07:33:30.000", + "duration" : 30240000, + "minutesToFallAsleep" : 0, + "minutesAsleep" : 438, + "minutesAwake" : 66, + "minutesAfterWakeup" : 1, + "timeInBed" : 504, + "efficiency" : 86, + "type" : "stages", + "infoCode" : 0, + "levels" : { + "summary" : { + "deep" : { + "count" : 4, + "minutes" : 103, + "thirtyDayAvgMinutes" : 89 + }, + "wake" : { + "count" : 33, + "minutes" : 66, + "thirtyDayAvgMinutes" : 65 + }, + "light" : { + "count" : 24, + "minutes" : 214, + "thirtyDayAvgMinutes" : 221 + }, + "rem" : { + "count" : 16, + "minutes" : 121, + "thirtyDayAvgMinutes" : 93 + } + }, + "data" : [{ + "dateTime" : "2019-04-27T23:09:00.000", + "level" : "wake", + "seconds" : 30 + },{ + "dateTime" : "2019-04-27T23:09:30.000", + "level" : "light", + "seconds" : 900 + }, +``` + +The JSON file for each activity stores a lot of information on heart rate. +Similar to the heart rate file, this date format does not take into account time zones. Grr! +Rather than storing a finish date like the sleep JSON file, this keeps track of the total duration +of the event in milliseconds. + +```json +[{ + "logId" : 21092332392, + "activityName" : "Run", + "activityTypeId" : 90009, + "activityLevel" : [{ + "minutes" : 0, + "name" : "sedentary" + },{ + "minutes" : 0, + "name" : "lightly" + },{ + "minutes" : 1, + "name" : "fairly" + },{ + "minutes" : 30, + "name" : "very" + }], + "averageHeartRate" : 149, + "calories" : 306, + "duration" : 1843000, + "activeDuration" : 1843000, + "steps" : 4510, + "logType" : "auto_detected", + "manualValuesSpecified" : { + "calories" : false, + "distance" : false, + "steps" : false + }, + "heartRateZones" : [{ + "name" : "Out of Range", + "min" : 30, + "max" : 100, + "minutes" : 0 + },{ + "name" : "Fat Burn", + "min" : 100, + "max" : 140, + "minutes" : 6 + },{ + "name" : "Cardio", + "min" : 140, + "max" : 170, + "minutes" : 24 + },{ + "name" : "Peak", + "min" : 170, + "max" : 220, + "minutes" : 1 + }], + "lastModified" : "04/06/19 17:51:30", + "startTime" : "04/06/19 17:11:48", + "originalStartTime" : "04/06/19 17:11:48", + "originalDuration" : 1843000, + "hasGps" : false, + "shouldFetchDetails" : false +} +``` + +After we import both the sleep files and activity files from the user we can use the VisJS library +to construct a timeline. + + +```javascript +function generateTimeline(jsonFiles) +{ + var items = []; + + for(var i = 0; i < jsonFiles.length; i++) + { + for(var j = 0; j < jsonFiles[i].length; j++) + { + if(jsonFiles[i][j].hasOwnProperty("dateOfSleep")) + { + var startT = new Date(jsonFiles[i][j].startTime); + var finishT = new Date(jsonFiles[i][j].endTime); + items.push({content: "Sleep", + start:startT, end:finishT, group:0}); + } + else + { + var localTime = new Date(jsonFiles[i][j].startTime); + var timeAdjusted = localTime.setHours(localTime.getHours() - 4); + var timeFinish = localTime.setMilliseconds( + localTime.getMilliseconds() + jsonFiles[i][j].activeDuration); + items.push({content: jsonFiles[i][j].activityName, + start:timeAdjusted, end:timeFinish, group:0}); + } + } + } + console.log("Finished Loading Heart Rate Data Into Graph"); + + var dataset = new vis.DataSet(items); + var options = + { + margin: + { + item:20, + axis:40 + }, + showCurrentTime: false + }; + + var grpups = new vis.DataSet([ + {id: 0, content:"Activity", value:0} + ]); + + var container = document.getElementById("heartRateGraph"); + var graph2d = new vis.Timeline(container, dataset, options); + graph2d.setGroups(grpups); + graph2d.on('rangechanged', graphMoved); + graphsOnPage.push(graph2d); +} +``` + +To make both the heart rate graph and the activity timeline focused on the same region at the +same time, I used the 'rangechanged' event to move the other graphs's window of view. + +```javascript +function graphMoved(moveEvent) +{ + graphsOnPage.forEach((g)=> + { + g.setWindow(moveEvent.start, moveEvent.end); + }) +} +``` + +I am pretty pleased with how these two graphs turned out. When you zoom too far out of the graph, the +events get really small, but, it does a pretty good job at visualizing a few days worth of data at a time. + +![Fitbit Activity TimeLine With Heart Rate](media/fitbit/fitbitDailyActivities.png) + +![Fitbit Activity TimeLine](media/fitbit/morningRoutine.png) + +# Pulling Outside Data + + +# Analysis diff --git a/blogContent/posts/data-science/fitbit.md b/blogContent/posts/data-science/fitbit.md deleted file mode 100644 index aea98d1..0000000 --- a/blogContent/posts/data-science/fitbit.md +++ /dev/null @@ -1,224 +0,0 @@ -Health trackers are the current craze. After I bought a Fitbit, I -wanted to determine what exactly could I do with Fitbit data. Can we -actually learn something from this data that we did not know before? -Most people don't need a watch to tell them that they walked a lot -today or that they got a ton of sleep. As humans we have a pretty good -gauge of our basic physical health. I am interested in figuring out -how we can use data science to look at our health data over a longer -period of time and learn something useful. - -Lets look at a few things that people typically use Fitbit data for -before we jump into the weeds. - -- Setting Goals -- Motivation -- Tracking Progress - -Ever since I bought a Fitbit, I found that I went to the gym a lot -more frequently. Having something which keeps track of your progress -is a great motivator. Not only is your daily steps recorded for your -own viewing, you can share that data with your friends as a -competition. Although I only have 1 friend on Fitbit, I found that was -a good motivator to hit the ten thousand steps per day. - -Goals which are not concrete nearly never get accomplished. Simply -saying that "I will get in shape" is a terrible goal. In order for you -to actually accomplish your goals, they need to be quantifiable and -measurable. Rather than saying "I will improve my health this year", -you can say "I will loose ten pounds this year by increasing my daily -step count to fifteen thousand and going to the gym twice a week". One -goal is wishy washy where the other is concrete and measurable. Having -concrete data from Fitbit allows you to quantify your goals and set -milestones for you to accomplish. Along the way to achieving your -goal, you can easily track your progress. - -Simply knowing your Fitbit data can help you make some better educated -decisions about your fitness. By comparing your data against what is -healthy you can tweak your lifestyle. For example: if you notice that -you are only getting 6 hours of sleep per night, you can look up the -recommended amount of sleep and tweak your sleep routine until you hit -that target. - -Alright, lets do some data science! - -![Tom and Jerry Data Science Meme](media/fitbit/dataScience.jpg) - -# Getting The Data - -There are two options which we can use to fetch data from Fitbit. - - -## Using Fitbit's API - -Fitbit has an [OAuth 2.0 web -API](https://dev.fitbit.com/build/reference/web-api/) that you can -use. You first have to register your application on Fitbit's website -to recieve a client ID and a client secret. - -I decided to fetch the Fitbit data using an Express app with node. -Fetching the data this way will make it really easy to use on a -website. Node has tons of NPM modules which makes connecting to -Fitbit's API really easy. I'm using Passport which is a pretty common -authentication middleware for Express. - - -```javascript -/** express app */ -const express = require("express"); - -/** Manages oauth 2.0 w/ fitbit */ -const passport = require('passport'); - -/** Used to make API calls */ -const unirest = require('unirest'); - -/** express app */ -const app = express(); - -app.use(passport.initialize()); -app.use(passport.session({ - resave: false, - saveUninitialized: true -})); - - -var FitbitStrategy = require( 'passport-fitbit-oauth2' ).FitbitOAuth2Strategy; - - -var accessTokenTemp = null; -passport.use(new FitbitStrategy({ - clientID: config.clientID, - clientSecret: config.clientSecret, - callbackURL: config.callbackURL - }, - function(accessToken, refreshToken, profile, done) - { - console.log(accessToken); - accessTokenTemp = accessToken; - done(null, { - accessToken: accessToken, - refreshToken: refreshToken, - profile: profile - }); - } -)); - -passport.serializeUser(function(user, done) { - done(null, user); -}); - -passport.deserializeUser(function(obj, done) { - done(null, obj); -}); - -passport.authenticate('fitbit', { scope: - ['activity','heartrate','location','profile'] -}); -``` - -Since our authentication middlware is all set up, we just need to add -the express routes which are required when authenticating. - -```javascript -app.get('/auth/fitbit', - passport.authenticate('fitbit', { scope: - ['activity','heartrate','location','profile'] } -)); - -app.get( '/auth/fitbit/callback', passport.authenticate( 'fitbit', { - successRedirect: '/', - failureRedirect: '/error' -})); - - -app.get('/error', (request, result) => -{ - result.write("Error authenticating with Fitbit API"); - result.end(); -}); -``` - -Now that we are authenticated with Fitbit, we can finally make -queries. I created a helper function called queryAPI which attempts -to authenticate if it is not already authenticated and then fetches -the API result from a provided URL. - -```javascript -const queryAPI = function(result, path) -{ - return new Promise((resolve, reject)=> - { - if(accessTokenTemp == null) - { - result.redirect('/auth/fitbit'); - resolve(false); - } - - unirest.get(path) - .headers({'Accept': 'application/json', 'Content-Type': 'application/json', Authorization: "Bearer " + accessTokenTemp}) - .end(function (response) - { - if(response.hasOwnProperty("success") && response.success == false) - { - result.redirect('/auth/fitbit'); - resolve(false); - } - resolve(response.body); - }); - }); -}; - -app.get('/steps', (request, result)=> -{ - queryAPI(result, 'https://api.fitbit.com/1/user/-/activities/tracker/steps/date/today/1m.json').then((data)=> - { - if(data != false) - { - result.writeHead(200, {'Content-Type': 'text/html'}); - result.write(JSON.stringify(data)); - result.end(); - } - else - { - console.log("Validating with API"); - } - }); -}); -``` - - -## Exporting Data from Website - -On [Fitbit's website](https://www.fitbit.com/settings/data/export) -there is a nice page where you can export your data. - -![Fitbit Website Data Export](media/fitbit/fitbitDataExport.png) - -The on demand export is pretty useless because it can only go back a -month. On top of that, you don't get to download any heart rate data. -The only data that you do get is aggregated by day. This might be fine -for some use cases; however, this will not suffice for any interesting -analysis. - -I decided to try the account archive option out of curiosity. - -![Fitbit Archive Data](media/fitbit/fitbitArchiveData.png) - -The Fitbit data archive was very organized and kept meticulous records -of everything. All of the data was in JSON format and was organized -nicely in in separate files labeled by date. Fitbit keeps around 1MB -of data on you per day; most of this data is from the heart rate -sensors. Although 1MB of data may sound intimidating, it is probably a -lot less after you store it in a format other than JSON. Since Fitbit -hires a lot of people for hadoop and SQL development, they are most -likely using [Apache Hive](https://hive.apache.org/) to store user -information on the backend. 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