Tag Archives: physical activity
Jamie Williams found himself with almost two years of self-tracking data including physical activity, blood pressure, and weight. Because of his interest in data visualization and coding he decided to learn how to access it the data and work on visualizing and understanding some of the trends and patterns. In this talk, presented at the QS St. Louis meetup group, he takes a deep dive into his activity and step data as well as his blood pressure data to learn about himself and what affects his behavior and associated data.
What Did Jamie Do?
Out of pure interest in seeing what the data would reveal, Jamie utilized a combination of devices to track his physical activity, blood pressure, heart rate, weight, numbers of drinks, and automobile travel. He then went on to explore ways in which he could pull down, integrate, visualize, and ultimately make sense of what he collected.
How Did He Do It?
In order to obtain his data on a minute-level resolution, Jamie had to email FitBit for a specialized use of their API. He then employed Mathematica to develop a number of (beautiful) visualizations of his activity – along with other key moments in his life (moving to St. Louis, changing job location, preparing for a Half Marathon, etc.). Jamie was able to compare his data not only to his peers through FitBit, but also to others of his demographic in the U.S. using the publicily available NHANES data set.
What Did He Learn?
Through Jamie’s Quantified Self collection and analysis efforts, he learned a lot not only about the patterns and changes in his activity, but why they were the case. He also presented great feedback about one’s mindset when comparing to peers vs. the general population.
Withing Blood Pressure Cuff
Thank you to QS St. Louis organizer, William Dahl, and Jamie for the original posting of this talk!
Last year Alex Collins was diagnosed with Type 1 diabetes. Prior to his diagnosis Alex was frequently engaged in different types of exercise and physical activity. After his diagnosis his doctor mentioned that he might have a hard time exercising and controlling his blood sugar to prevent hypoglycemia. In this talk, presented at the London QS meetup group, Alex described his process for tracking and understanding the data that affects his day-to-day life so that he could “live my life normally without a high risk of complications.” This process of collecting and analyzing data has even pushed him to continue to explore his athletic boundaries, resulting in a running a ultramarathon and setting the world record for the fastest marathon while running in an animal costume.
Slides are available here.
We are not the only ones curious about whether our activity level looks different when seen with different trackers. Bastian Greshake, co-founder of OpenSNP.org, has been comparing his FuelBand and his Fitbit for months. Here’s what he found.
Inspired by Ernesto’s post I wanted to take a look at how my data for the Fitbit and the FuelBand compare to each other. I started wearing the FuelBand in October of last year. Since then it has only left my wrist to recharge the battery. I was already carrying a Fitbit Ultra, which I’ve had since May 2012. I wear the FuelBand on my dominant arm. The Fitbit is usually clipped to the pocket of my jeans and I have it on my non-dominant arm while sleeping. From my day-to-day experience I have a sense that FuelBand steps are usually a good way below the Fitbit steps. But I also thought that the difference was getting smaller, probably due to firmware updates on the FuelBand.
Using the Fitbit-API (and it’s integration into openSNP) it’s quite easy to get a file that contains all step counts measured with the Ultra. If you have an openSNP account you can download the complete file, also including sleep data and body measurements here. Unfortunately the Nike+ API isn’t ready yet, so one needs to manually scrape the data. As this is boring work that can’t easily be automated I only got FuelBand step data back to 2013/11/16. Still, that should be enough to get a first insight on how both devices compare.
I’ve been curious about tracking physical activity since I was an undergraduate. I remember traveling to a local middle school with a researcher interested in how physical activity was taught in low-income Native American communities. Back then, the best we could do was have the children wear simple electromechanical pedometers to count their steps during their physical education classes. Fast forward about ten years and I’m still working with pedometers and physical activity sensors – but much better ones. Quantified Self toolmakers are experimenting with many upgrades to the old digital pedometers, including new ideas about syncing, more fashionable design, and – of particular interest to self-trackers – integration of optical heart rate monitors. (No chest strap.)
Below are some of the notable Quantified Self tools recently announced at CES. Did I miss one? Let me know in the comments and I’ll add it! I’ve also written a bit about what I think are some notable trends below.
The Flex appears to be Fitbit’s answer to the growing trend of wrist worn wearable activity monitors. Interestingly they’ve chosen to focus on the wireless syncing capabilities and eschew a traditional display; there is just a small glanceable LEDs to highlight goal progress.
Measures: Steps, Distance, Calorie Burn, Activity Minutes, Sleep Time, Sleep Quality
Sync: Bluetooth 4.0
Withings Smart Activity Tracker
In 2013 Withings is stepping in to the activity tracking space with their Smart Activity Tracker. While it appears to be just another accelerometer-based device Withings has also packed a heart rate pulse sensor into the small form factor.
Measures: Steps, Distance, Calorie Burn, Sleep Quality, Heart Rate
Sync: Bluetooth and Bluetooth 4.0
Omron Activity Monitor
Omron has long been a staple in the low-cost pedometer market. With the launch of their Activity Monitor they’ve shown up with a wireless activity tracker of their own. Omron is semi-wireless; syncing requires that you plug a USB accessory into your computer, then place the pedometer nearby.
Measures: Workout Time, Steps, Distance, Calories burned, Pace
Sync: NFC Plate (USB)
Omron Heart Rate Monitor
Integration of pulse tracking into activity monitors is a current trend, and we’re very curious about what we’ll learn from having continuous heart rate data. Omron’s new heart rate monitor uses optical sensing on a strapless watch, with eight hours of storage capacity. The press announcement promises pace, calories, and distance, which means the watch probably has accelerometer-based actigraphy on board as well.
Measures: Heart Rate, Pace, Distance, Calories Burned
Sync: Micro USB
The Orb is new small and sleek device that builds on their already released Fitbug Air wireless pedometer. The new pebble-like Orb is a screenless activity tracker that uses Bluetooth syncing to a mobile app in three different modes: Push for updates on demand, Beacon for timed updates on a regular interval, and Stream for real time updating. The Orb’s small form factor works with a variety of different wear options, including wrist straps and lanyards.
Measures: Steps, Distance, Calories Burned, Sleep
Sync: Bluetooth 4.0
BodyMedia Core 2
The BodyMedia armband is known for its accurate activity tracking, which comes from integrating the data off multiple sensors. A new device, the Core 2, has the same measurements that are currently available (core temperature, heat flux, galvanic skin response, and tri-axial accelerometry) in a smaller package. A version with an integrated heart rate monitor will be also be available.
Measures: Temperature, Heat Flux, Galvanic Skin Response, Activity, Heart Rate (optional)
Sync: Bluetooth 4.0
Bonus Non-Activity Device
This last device kept popping up on my various feeds yesterday. The HapiFork is designed to help you understand how you eat by tracking how many bites you take and how long it takes you to eat your meal. It will also alert you when you’re eating too fast. Will the first person to use this please give a Quantified Self show&tell talk as soon as possible?
Measures: Fork “servings”, Eating Time
Sync: Bluetooth or USB
In my current work I’m really interested in how real time information about physical activity behavior can be used to help people change their normal patterns. In our little corner of the research world we understand that self-tracking devices are wonderful tools to help people change their behavior. But, what we don’t know yet is how the data gathered by these tools can really help people in the moment. The newest crop of tools and devices may start to help us answer that question.
By now if you’ve seen one physical activity tracker then you’ve seen them all. At their core they use the same technology that’s been used for almost a decade – actigraphy. That is, most devices are based on an accelerometer, a tiny little sensor that measures
gravitational force acceleration. These sensor pass data through an algorithm that used machine learning and pattern recognition techniques to determine a variety of data points. Steps, distance, activity intensity, calorie expenditure – you’re probably familiar with all these. So what’s new in this space? How are companies starting to differentiate themselves? While looking through some of the new offerings being showcased at this week’s International Consumer Electronics Show (CES). It appears that there are two major themes that I think are coming forth: Wearability and Syncing
Wearability. The pedometers we made kids wear 10 years ago? Utilitarian hunks of plastic and electronics. Nothing you would want to show off to your friend or coworker. Looking at the latest from Fitbit, BodyMedia, and others it’s clear that companies are introducing real fashion where there used to be just electronics. Will they succeed in making activity trackers a fashion trend? A status symbol?
Syncing Capabilities. When Fitbit introduced their tracker a few years ago one of the biggest complaints was that it didn’t sync to our phones. Now, nearly every new device offers Bluetooth syncing with paired mobile apps. The rise of Bluetooth 4.0 has made it easier for nearly everybody to wirelessly sync. I’m curious about the future of low power data sharing beyond the phone. Soon we may see myriad devices talking to each other directly. What happens when your fitbit starts talking to your fridge?
Some people may be wondering how I find all the amazing people conducting neat self-tracking experiments and creating jaw-dropping personal data visualizations. Well, for the most part I just listen. I’m constantly paying attention to what’s being said on twitter about #QuantifiedSelf. When that doesn’t work I just use the power of Google to find people who are blogging about self-tracking, self-experimentation, or personal data. It’s great to look through the search results and see how many people are sharing their personal stories and insights. While doing some searching this morning I stumbled across a project that immediately brought a smile to my face. Hopefully you’re excited by this as much as I am.
Chris Volinsky is a statistician at AT&T Research and he’s no stranger to handling large data problems. Back in 2008 he was part of the team that won the $1 Million Netflix prize. He also has quite the impressive list of research papers that illustrate the many different uses of cellphone location data. But what is really interesting about Chris is his newest project: My Year of Data
Back in November of 2011 Chris started off a blog entry that with this:
My name is Chris. I am 40 years old. I am 5’9 1/2″ and weigh 174 pounds. I walked 9,048 steps and have consumed 1,406 calories today (so far).
Realizing that he’ld been gaining weight and wasn’t at his optimal health he decided to take a data-centric approach to improving his health. He is a statistician after all. So far, he’s found some interesting things. Take for instance his weight and dietary tracking.
As he explains in this post, Chris typically has a hard time tracking his diet consistently. This can be pretty frustrating when you hear about how important it is to eat this or not eat that to help with weight reduction. Rather than get frustrated Chris turned to the data to see what he could learn. When he stopped looking at the data he was entering and started looking at the missing data an interesting trend lept out. He found that fluctuations in his weight appeared to be correlated with whether or not he was logging food. Take for instance the plot below. It appears that there is a pretty clear association with periods of weight loss and periods of actively logging his food (pink zones). The opposite also appears to be true – no food logging = weight gain.
So this is where a typical NFATW post would stop. We have an interesting finding and a neat data visualization. But, Chris is doing something much more interesting than just talking about his weight data. He is on a long-term self-tracking and self-discovery journey and he is trying to enlist other interested parties to help him. Chris is going the extra step and posting all of his self-tracking data online for anyone to analyze, visualize, or just get inspired.
You can access all of his amazing data via a public dropbox folder that he’s set up. He even has a nice README file explaining the datasets and formats. So far he’s sharing the following:
- Fitbit: sleep and activity data
- FitLinxx: weight training data from gym activities
- Livestrong: dietary tracking data
- Runkeeper: running and other exercise activity data
- RescueTime: productivity tracking (computer/internet use)
All the data is open and available for you to play with. This should be a really interesting project to keep “track” of in the future (pun definitely intended). To help inspire some action on your part I took some time today and looked at Chris’s most recent available data to see what I could find out. I downloaded his Fitbit data and decided to look for any interesting patterns. Turns out that when taking a look at his daily patterns of activity there seems to be something going on on Thursdays that reduces his step count and activity time . Also, Saturday is by far the best day with an average of 9,862.56 steps and a 5.3 hours spent being active (data available here).
Make sure to reach out to Chris over at his blog and take a took at his data to see what interesting thing you can figure out!
Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.
Chloe Fan has been self-tracking since she was 14 years old and saw the first Harry Potter movie in theaters. She is currently a Ph.D. student at Carnegie Mellon’s Human Computer Interaction Institute. After finding her passion for data visualization and information design for self-tracking tools, she has decided to take a year off grad school to pursue her dreams at full speed in the Bay Area. She is available for consulting or full time positions!
There is a huge increase in the number of personal informatics tools over the last few years that help us track various aspects of our daily lives. The majority of consumer tools use visualizations, often in the form of charts (i.e., column graphs, line graphs, scatterplots), to help users understand the numerical data that they are collecting, and find meaning in their behavioral patterns. Research tools have also used nature metaphors, like a garden or a fish tank, that thrives on physically active users. While promising in motivating behavior change, they can also be punishing when users are inactive (sad fish or wilting flowers).
I am specifically interested in exploring abstract art as visualization for physical activity. It’s able to present information in an aesthetic and neutral way that is non-judgmental. Reflecting on abstract artwork by Wassily Kandinsky, Piet Mondrian, and Jackson Pollock, I created Spark, a system that uses abstract art in a dynamic ambient display for physical activity.
Each visualization is an animation that unfolds as the day progresses. Circles are created based on step counts. The size of the circle represents number of steps, and the color of the circle represents intensity (casual walking, brisk walking, or running).
Every five minutes, a circle appears in the middle of Spiral that represents the steps taken during that five-minute period. It pushes previous circles outward in a spiral, so steps taken earlier that day appear at the edge. If no steps are taken in that five-minute period, no circles appear.
In Flora, rings of color are added around a circle for every five-minute period with step counts. The result is a series of concentric circles showing periods of activity throughout the day, with the final size indicating the total step count for the day.
In Bucket, colorful circles fall from the top and fill up the screen to represent steps taken every five-minutes. We found that the use of concentric circles made the visualization more aesthetically pleasing; however, the concentric circles do not yet represent anything meaningful.
Inspired by Jackson Pollock, Pollock is the most random of these abstract visualizations. A white line draws randomly across the screen, and when there is activity, the canvas is splattered with color.
I conducted a study with Spark deployed on tablets in 5 homes over 3 weeks, just to see how people interacted with this kind of display. Everyone reacted positively to it, but the most interesting finding was that the 3 younger adults (20+ females) preferred Spiral and Rings because they were looking for specific time and intensity data regarding their gym sessions. The 3 older adults (58-71 years old) preferred Bucket and Pollock because they were interested in daily cumulative totals from walking a lot.
Some of the things people said motivated them to do more activity short-term were the colors, variety of visualizations, and the visual challenge of filling up the screen with colors. I also got lots of good feedback on displaying the data differently, like as a screensaver or hung on a wall like a piece of artwork.
There are many planned features for Spark, including:
- Weekly and monthly view showing the final visualization for each day
- Aggregate daily/weekly/monthly statistics
- Adding more charts to compare with the abstract visualizations
- Inclusion of other physical activity properties, such as speed, location, indoor/outdoor activity, and type of activity (i.e., biking vs. swimming). Currently, the Fitbit tracker does not distinguish between activities, so this feature will need data streams from other sensors.
Spark is still in early stages, but if you’d like to check it out with your own Fitbit data, you can sign up at www.sparkvis.com/fitbit/auth. It will connect to your Google account (username data only) to identify you on the Spark site, and you will also need to log in to your Fitbit account to get your Fitbit data (it’s a hassle, but the easiest way I can get it to run right now). Would love to hear your thoughts on improving Spark!
This is a guest post by Patrick Burns, who is a PhD candidate in the School of Computing and Information Systems at the University of Tasmania in Hobart, Australia. He is researching the use of technology to promote physical activity. His interests include ubiquitous and wearable computing and ambient displays.
According to the World Health Organization more than one in ten adults worldwide in 2008 was obese. This figure has more than doubled since 1980. We know that obesity is a major risk factor for heart disease, diabetes, osteoarthritis and some forms of cancer. Unsurprisingly a combination of inadequate exercise and increased consumption of fatty, sugary foods is to blame. When it comes to a lack of physical activity, some people point the finger at technology. Cars let us drive to places we used to walk. Machines do jobs we used to do by hand. Video games, DVDs, television and the Internet provide a wealth of sedentary entertainment options. But could technology actually help us to do more physical activity, and ideally help prevent obesity?
One approach is to help people to better track their activity. The hope being that if we make a person more aware of their physical activity (or lack of it) that they will be motivated to do more. There are a number of existing devices designed to do just that. Examples are FitBit, Jawbone UP and Nike FuelBand. Each integrates a motion sensor (accelerometer) into a wearable device to track how much the wearer moves around during the day. There are also stand-alone smartphone apps which use the phone’s in-built accelerometer to track physical activity. In the case of the UP and some smartphone apps, the user can supplement their activity data with information on the type of food they’re eating. The UP and FitBit also monitor users’ sleep habits.
The data collected are processed and delivered to the user in the form numbers and graphs. A count of steps taken each day. Time active vs. sedentary. Steps climbed. Calories burned. There is an assumption that, when it comes to activity tracking, more is better. That we should collect more data from more sensors. That we should perform more analyses on that data and present it to the user in multiple forms. We should make our interfaces more engaging, to encourage users to continue to monitor their activity data. In the words of Jawbone’s VP of product development, “you have to create a Facebook-like engagement that keeps people coming back”.
The truth or otherwise of these assumptions is very much dependent on individual users, and the way in which they employ a particular technology in their lives. Users who are very motivated to do exercise, sometimes playfully called “fitness freaks” or “gym junkies”, employ activity monitoring technology in a supporting role. They already do a lot of physical activity and enjoy being able to record, quantify and analyse that activity. But what about less motivated users – people who don’t do enough physical activity and know that they should do more? For those users technology plays a motivating role, one in which the technology is (and should be) more peripheral to their day-to-day lives.
If we give these users an interface that is too complex, or that requires a continuing and significant time commitment, we run the risk that they will lose interest, “burn out” and return to old habits. Many of us have had the experience of starting a diet, joining a gym or buying exercise equipment only to give up soon after. These experiences underline the need to make small changes, slowly, that can be sustained in the long term. We need to design technology to support this type of change.
I argue that for these less motivated users, simpler interfaces could be just as effective as more complex, more engaging interfaces. Do we really need to know exactly how many steps we’ve taken or how many calories we’ve burned? Or is it good enough just to know that we’ve “done well” today or that we need to “do more”. Do we really need graphs and figures, or could we convey information in a simpler way. Say, through coloured lights.
I’m currently researching the use of simple interfaces to deliver physical activity information to users, with a specific focus on wearable technology. I recently designed and evaluated such a device – ActivMON. ActivMON is a wrist-watch like device containing a motion sensor and coloured light. The motion sensor detects the user’s physical activity and the light changes colour (on a spectrum from red to orange to green) to show the user’s daily activity level as compared to an activity goal. ActivMON then shares this data through the Internet with other devices. If you’re doing physical activity then the lights on your friends’ devices will pulse to let them know. If they’re doing physical activity, your device will pulse. Supporting social influence is important, and I wanted to see if this could be done using a wearable ambient display.
This work is still in its early stages, but I feel it raises some interesting questions. Should we deliver information differently (and more or less information) depending on a user’s level of motivation to change? How engaging should interfaces be? How little information can we deliver, and yet still realise a motivational effect? This is not to argue against the quantified self. Rather to pose the question of how best to present data to users once we’ve collected it.