Tag Archives: moves
Like many people paying attention to the press around Quantified Self, self-tracking, and wearable technology I was intrigued by the many articles that focused on a newly published research letter in the Journal of the American Medical Association. The letter, Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data, authored by Meredith A. Case et al., described a laboratory study that examined a few different smartphone applications and self-tracking devices. Specifically, they tested the accuracy of steps reported by the three different apps: Moves (Galaxy S4 and iPhone 5s), Withings Health Mate (iPhone 5s), and the Fitbit app (iPhone 5s), three wrist-worn devices: Nike Fuelband, Fitbit Flex, and the Jawbone UP24, and three waist-worn devices: Fitbit One, Fitbit Zip, and the Digi-Walker SW-200. Participants walked on a treadmill at 3.0 MPH for trials of 500 steps and 1500 steps while a research assistant manually counted the actual steps taken. Here’s what they found:
As the data from this research isn’t available we’re left to rely on the authors description of the data. They state that differences in observed vs device recorded steps counts “ranged from−0.3% to 1.0% for the pedometer and accelerometers [waist], −22.7%to −1.5% for the wearable devices [wrist], and −6.7% to 6.2% for smartphone applications [phone apps].” Overall the authors concluded that devices and smartphone apps were generally accurate for measuring steps. However, much of the press around this study dipped into the realm of sensationalism or attention grabbing headlines, for instance: Science Says FitBit Is a Joke.
Part of our work here at Quantified Self Labs is to encourage and help individuals make sense of their own data. After reading this research letter, or one of the many articles which covered it, you might be asking yourself, “I wonder if my device is accurate?” or “Should I be using a step tracking device or just my phone?” In the interest of helping people make sense of their data so that they can come to their own conclusions I decided to do a quick analysis of my own personal data.
For this analysis I examined the step data derived from my Fibit One and the Moves app I have installed on my iPhone 5. (Important note: the iPhone 5 does not have the M7 or M8 chip present on the 5s and 6/6+, respectively, which natively tracks steps.) I had a sneaking suspicion that my data experience differed from the findings of Case and her colleagues. Specifically, I had a hypothesis that the data from every day tracking via the Moves app would be significantly different than data from my Fitbit One.
First, I downloaded and exported my daily aggregate Fitbit data for 2014 using our Google Spreadsheets Fitbit script. I then exported my complete Moves app data via their online web portal. To create a daily aggregate step value from my Moves data I collapsed all activities in the summary_2014.csv file for each day. (Side note: We’ll be publishing a series of how-to’s for doing simple data transformations like this soon). This allowed me to create a file with daily aggregate step data from both Moves and my Fitbit for each day of 2014. Unfortunately I did not have my Fitbit for the first few weeks of 2014 so the data represents steps counts for 342 days (1/24/14 to 12/31/14).
I found that my Fitbit One consistently reports a higher number of total steps per day than my Moves app. Overall, for the 342 days I had 689,192 more steps reported by Fitbit than by the Moves app. The descriptive information is included in the table below:
Another way to look at this is by visualizing both data sets across the full time-frame:
There a few interesting things to point out in this dataset. On two days I have 0 steps reported from my Moves app. One day, Moves was unable to connect with their online service due to me being in an area with little to no cell signal. On the other day my phone was off, probably due to an iOS 8 release and having to reboot my phone a few times.
It is also clear to me that differences in data are related to how I wear my Fitbit and use my phone. For my Fitbit, it is basically on my hip from the time I wake up until the time I go to bed each night. However, my phone isn’t always “on my body” throughout the day. I think this is probably the case for more people.
Since I wear my Fitbit at all times some of the data it captures erroneously is included in the total step count. For instance, for the last few months in this data set I was commuting about 10 miles per day during the week by bike. This data is accurately captured as cycling by Moves, but captured as steps by my Fitbit. Therefore some over-reporting by Fitbit is present in the data.
For my own data I found that the Fitbit reports higher steps on most, if not all days, than the Moves app on my iPhone 5. There are a few caveats with this data and analysis that are worth mentioning. First, this exploration was intended to begin a conversation around the real-world use of activity monitoring apps and devices, and the data they collect. It was not intended as a statement on truth or validity (however I would welcome the help of a volunteer to follow me around with a manual clicker counting all my steps). Second, this analysis was undertaken in part to help you understand that scientists of all types, be it citizen or academic, have the ability to work with their own data in order to come to their own conclusions about what works or doesn’t work for them. Lastly, this analysis was completed very quickly and I am sure that other individuals may have different ideas about how to explore and analyze the data. For this reason I’m posting the daily aggregate values in a open Google Spreadsheet here.
Bob Troia was interested in his blood glucose. While he’s not a diabetic and he’s not out of range, he wanted to see if he could lower his fasting glucose levels. He started a long-term tracking experiment where he tested his blood glucose and began to explore the effects of supplementation and lifestyle factors. In this talk, presented at our 2014 Quantified Self Europe Conference, Bob talks about his experiment and what he learned from analyzing his data. Make sure to read his take on what he did, how he did it, and what he learned below.
You can also view the slides here [pdf].
We also asked Bob to answer the three prime questions:
What did you do?
After learning via my 23andMe results that I had an elevated risk for Type 2 diabetes (and having an interest in the longevity benefits of maintaining low blood glucose levels), I began tracking my daily fasting glucose and the effects that diet, exercise, supplements, and stress have on glucose levels so I could take whatever steps I needed to proactively understand, control, and optimize it.
How did you do it?
Over the course of 7 months, each morning I would take a fasting glucose reading using a handheld glucose meter. After establishing a 30-day baseline of daily fasting glucose readings, I began to take supplement called oxaloacetate. It’s been shown to lower and more tightly regulate fasting glucose by mimicking the effects of caloric restriction. It’s a naturally occurring compound found in lots of foods, such as spinach, potatoes, or apples, and it’s as safe as Vitamin C. After several weeks, there was a noticeable improvement in my average values! I then started looking at day-of-week trends in addition to how exercise (in my case, playing soccer) and other things such as travel affected my glucose.
What did you learn?
I learned that I could indeed improve and better stabilize my fasting glucose levels using oxaloacetate – but only in conjunction with intense, interval-type exercise like soccer. My average fasting glucose levels are highest on Mondays (stress of a new work week) and lowest on the weekends. Long airplane travel can adversely effect my glucose levels for several days. Surprisingly, alcohol consumption did not have an effect.
In the Quantified Self community we focus on projects and ideas that help people access and get meaning out their personal data, including the information you can collect with your smartphone. If you have an iPhone, Android, or Windows phone you’re already have carrying of the world’s most sophisticated self-tracking tools. The GPS, accelerometer, the microphone, all of these tiny sensors make up a great set of tools you can use to understand how you move around the world.
I’m going to focus this short “how to” on geolocation data and mapping your movement, specifically using data gathered by the Moves application. Moves is a passive activity and location tracking tool available for the iPhone and Android. We’ve written a bit about it in the past and had a chance to interview their CEO, Sampo Karjalainen. I’ve been using it since May, 2013 and I wanted to share some neat tools and methods for getting a bit more out of the data Moves collects.
I find that visualizing my data on a map to be incredibly powerful. It might by my inner cartographer, but seeing my patterns of movement (or lack there of) in reference to known places and landmarks is a great mechanism for inducing recall and reflection on where I’ve been and what I’ve done. Hopefully you’ll use one of the tools or methods below to map you data and learn something new!
Moves Connected Apps
Like many self-tracking applications and devices, Moves has a API that many different developers have built services on top of. Here are just a few of the services that allow you to see your data on a map. Be advised that each of these services has access to your data. Make sure to read their Terms of Service before agreeing to the data transfer.
WebTrack. This is by far the most utilitarian data mapping tool. However, you shouldn’t get discouraged by the lack of fancy design because it gives you an very unique data view. When you use Moves on your phone you typically only see the “storyline” and the detected places you’ve spent time at. However, Moves is constantly pinging and recording your location when it detects movement. WebTrack allows you to see all those movement points by hovering over the associated timestamp.
Fluxtream. You might know Fluxtream as Friend of QS and a great open-source data aggregation tool. They’ve set up a “Moves Connector” that allows you to import and visualize your Moves data. Because Fluxtream is set up as an aggregation and visualization tool you can also map other interesting data sets. Want to know where you were tweeting last week? Fluxtream will map it for you. (You can see me tweeting on a CalTrain ride between San Francisco and Palo Alto below.)
Zenobase. Another interesting data aggregation service here. Zenobase treats your Moves data bit differently. Rather than importing all the movement geolocation data it focuses on your place data and visualizes those locations. I like the high-level view it start with, but make sure to keep zooming in to see more specific place data.
Resvan Maps. This mapping application adds a unique twist to the typical mapping visualizations. It will plot your places, paths, and categorize paths depending on the activity (transport, walking, running, and cycling). Additionally, you can create “analysis cirlces” and have the application compute the time you spent in a certain location you bound (it aggregates to hours:minutes per day).
MMapper. This method for mapping your data, developed by Nicholas Felton, is by far the most technical, but it produces some really neat visualizations. You’ll have to download Processing and follow the instructions Nicholas provides on the Github repository page here. The great thing here is that the mapping and data access is all happening locally.
Move-O-Scope. Another great mapping application here from the folks at Halftone.co. They’ve probably completed the most thorough mapping and exploration tools for your Moves data. After linking your Moves account you can explore maps by activity type, day of the week, and custom data ranges. Additionally, they’ve implemented a neat feature for exploring place data. You can see how many times you’ve visited a specific place, where you’ve come from and where you go next, what days you typically visit, and your typical time of day at that place. (See this post for background on why they created this nifty tool.)
Map It Yourself!
If you don’t want to trust your data to a third party, but you still want to explore your movement maps there is really great option for you. Our friend and co-organizer of the QS LA Meetup, Eric Blue, recently published a method for easily exporting your data: the Moves CSV Exporter. You’ll have to login and use the Moves pin system in order to download your data, but Traqs isn’t storing your data, just providing a way for you to access it. The tool allows you to download and explore your activity, summary, tracks and place data. We’ll focus on the place data for creating maps. You can also use your full tracks history for mapping all the geolocation points Moves collects.
Because this data is based on latitude/longitude coordinates there are many different methods available that you can use to map your data. I’m going to focus on two here: Google Fusion Tables and CartoDB (if you know of others share them in the comments or our forum).
Google Fusion Tables
Fusion Tables are a new Google Drive tool that you can use to store, analyze, and visualize many different types of data. Once you download your Moves places.csv file you can upload it to a new Google Fusion Table. Once you upload your data, which takes about 2 minutes, you’ll see a menu bar and three tabs: Rows, Cards, Map of longitude. Just click on the “Map” tab and you’ll see your data already placed on a map. If you want to see a heatmap rather than a point map just navigate to Tools -> Change Map and you’ll see an option for a heatmap on the lefthand side. This is just the tip of iceberg for mapping fusion table data. You can learn more about different mapping methods and tricks here.
CartoDB is a visualization and analysis engine for geospatial data. I’ve been using it to play around with a few of the different geolocation datasets that I have (I actively keep three). Although it is paid service, they do offer a free plan for smaller datasets, which is perfect for your Moves data. Again, you’ll have to upload your places.csv file to a new table once you set up your account. Once the data is uploaded there are quite a few different map visualization wizards you can use to view your data in different ways. Pesonaly I like playing with the “Torque” visualization that gives you a real feeling of space-time to your data.
TileMill is an interactive map design tool from the folks over at Mapbox. If you’re looking to create custom maps with your data that you can format, style, and share then this is a wonderful tool to use. At first glance it’s a little daunting because it looks like a mashup of a CSS editor and map tool. That actually gives it the unique power to drive customization. Don’t be afraid, it’s not too hard to get started with. Mapbox has provided a great “crashcourse” to get you started with importing data, saving it as a new layer on your map, and then manipulating how it looks on your screen. If you want to go just a bit farther you can also add legends and informative popups to describe your data points. Mapbox also offers a free hosting plan if you want to share your interactive maps on a webpage. For example check out my MovesMap here, where I added a quick styling to manipulate the point size in relation to the time spent at a location.
Hopefully you’ve learned something new from this. If you map your Moves data (or any other geolocation data) we want to see it! Leave a link in the comments, post it in the location mapping thread on the QS Forum or get in touch on twitter!
This entry comes to us from Bob Troia. Bob runs the excellent Quantified Bob blog where he explores self-tracking and experimentation. Make sure to check out this post where he explains how he created this great visualization of his movement data.
Here’s a cool visualization of approximately 1 month of my location data in and around New York City using Moves and a Processing sketch Nicholas Felton put together. Yellow lines are walking (you’ll see the hot spots where I walk my dog or around my office, blue are cycling (usually to/from the soccer field), and gray are subways/car/taxi. Pretty neat! It shows that I am very much a creature of habit (or I walk the same routes all the time to conserve willpower!
We invite you to take part in this project as we share our favorite personal data visualizations.If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along.
Today we are happy to bring you another interview in our Toolmaker Talk series. We had the great pleasure of speaking with Sampo Karjalainen, the designer and founder of Moves. Over fifty percent of U.S. adults have a smartphone. That’s a lot of people walking around with a multi-sensored computer in their pockets. Moves is another example of how developers and designers are focusing on the smartphone as a Quantified Self tracking and experience tool. This is an exciting space, and one we intend to keep a close eyes on moving forward.
Watch our conversation or listen to the audio (iTunes podcast link coming soon!) then read below to learn more about Sampo and the Moves app.
How do you describe Moves? What is it?
Moves is an effortless activity tracker. It’s a bit like Fitbit or Jawbone UP, but in your smartphone. There’s no need to buy, charge and carry one more device. In addition to steps and active minutes, the app also automatically recognizes activity type: walking, running, cycling or driving. It also shows routes and places and builds ‘a storyline’ of your day. It helps you remember your days and see which parts of your day contribute to your physical activity. It’s a simple, beautiful app that hasn’t existed before.
What’s the backstory? What led to it?
We started Moves to motivate us to move more. Aapo Kyrola was doing his Ph.D at Carnegie Mellon University, working hard, gaining weight and lacking the motivation to exercise. We began discussing how to motivate people like Aapo to move more. The first prototype used game motivations: we had badges, leaderboards and virtual pet to motivate people. The problem was that they still had to remember to start and stop tracking. We quickly learnt that people didn’t remember to use it for everyday walks. That made us think that maybe we could make it work continuously in the background. It took plenty of R&D to find a way to minimize battery use while still collect enough data to recognize activity types and places correctly.
What impact has it had? What have you heard from users?
We’re seeing that when you make activity visible, people start to think about it. And when they think about it, they start to do small changes in their lives. They may park their car a bit further or consider biking instead of car. They may choose to walk just to get some steps and take a break from everyday hurries. It also helps people see how long it takes to travel between places and how much they actually use time in different places.
What makes it different, sets it apart?
Other phone-based trackers are good for tracking one run or one biking event. Moves is made to track all-day activity. Compared to activity gadgets, Moves recognizes activities by type, recognizes places and shows routes. It’s collecting a new type of a dataset that hasn’t been available before. And best of all, we now have a public API, so you can use your data as you like!
What are you doing next? How do you see Moves evolving?
Currently we’re busy with the Android version of Moves and adding some features to the iPhone version. Over time we see that Moves will become a tool to understand not only your physical activity, but also your use of time, travels – your life in general.
Anything else you’d like to say?
Moves is collecting your location in time and space. It’s a great ‘backbone’ for connecting all kinds of other data. We’re excited to see what type of visualizations and mashups people create!
This is the 20th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned? In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a toolmaker and want to participate in this series please contact Ernesto Ramirez.