Tag Archives: running
Data gave me power to talk about the issue.
We highlight a lot of great show&tell talks here that focus on personal medical mysteries and understanding one’s own health. Well, this one really hit home for me. I’m a runner and I’m constantly battling minor injuries and recurring knee pain. It’s nothing terrible, but it’s at that level of annoying that really makes it hard to enjoy running as much as I should.
Mark Wilson was having similar issues. After running a half-marathon his knee started giving him trouble. The typical treatments didn’t work for him, but instead of giving up running he turned to self-tracking to understand his knee pain (you can see a snap shot of Mark’s running (blue) and knee pain (pink) over time in the header image of this post). In this show&tell talk, filmed at the QS San Francisco Meetup, Mark explains how he built a database that pulls information from different sources like Fitbit, Runkeeper, and his self-rated knee pain, and what he’s learned from that process.
I think most importantly putting all this data together and being able to look at it gave me power to talk about it. Because, I can’t really describe how much despair I was feeling just looking at my knee and thinking, “What the hell is wrong with you? Why is my knee hurting?” I felt like I was trying everything I could on my own and it just wasn’t working. So I wanted to collect a lot of evidence against my knee to indict it.
This data-backed indictment enabled him to have better and more productive conversations with his physical therapist and he began to understand how to move forward. Is it working? You’ll have to watch his great talk to find out:
There is something really magical about taking data and turning it into a compelling visual image. Even though I’ve already written a bit about the importance of making data visual, I am consistently amazed at how data can be made more appealing and informative by creating eye-popping graphics. Today we are devoting this NFATW post to some amazing projects with beautiful data.
Tom MacWright is an engineer for MapBox and Development Seed and spends his time creating and using amazing visual representations of his data. Here are just two of many wonderful projects.
A New Running Map
Tom wasn’t happy with the data visualization he was getting from his Garmin GPS and heart rate watch so he decided to build his own using tools he works with every day. What came out was a really interesting interactive website that visualizes his running routes along with his heart rate. Click on the image above to play around with him data.
He’s also created a unique representation of the same time of running data (GPS + HR) that anyone can play with called Ventricle. Ventricle allows you to plot your own running data if you have .gpx files.
I’ve had a long standing interest in how I spend my time interacting with my computer. As a long time RescueTime user I’ve gotten used to having something watching my computer use and informing me about my habits. Tom was also interested in his computer use, but wanted something that had less functionality while still giving him information that was important. So, he developed Minute, a keystroke counter and visualization system that constantly records and displays the keystroke frequency over time.
By using a heat map he is able to better understand the pattern of his technology usage. Interestingly, he is also able to make inferences about his sleep and leisure time as he treats them as the inverse of his keystroke time:
Minute is an open-source application hosted on github so if you’re interested in understanding your own computer use or want to contribute to the project go take a look at the source code.
We’ll wrap up today with a quote from Tom’s post on what he learned from developing and using Minute:
Tracking nearly anything you do is alarming and humbling. The aggregates of our actions are lost on us: we can watch hundreds of hours of television and write it off as a small time commitment. How much is too much? It’s hard to make pretty charts without learning something and thinking about what they should look like.
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.
Alex Grey is developing a better kind of muscle sensor, to help people see their muscle activity patterns and change behaviors like typing or running to be more effective and less painful. The sensors are wireless, stick to your skin, and can measure different kinds of muscle activity including arm/leg (EMG) and heart muscles (ECG). In the video below, Alex describes how he used these sensors to find his optimal stride rate as a runner, as well as to detect when he was starting to fatigue or compensate on one side for an old injury. Fascinating talk with lots of great data! (Filmed by the Bay Area QS Show&Tell meetup group.)
The first speaker at last week’s QS meetup in San Francisco was Alexander Grey. He told us about the muscle-activity sensor he had developed and the fascinating things he had learned about himself from using it. The result of many years of thinking and work, he’s now eager to find collaborators, so he jumped at my suggestion to participate in this series.
Q: How do you describe Somaxis? What is it?
Grey: We have developed a small, wireless sensor for measuring muscle electrical output. The sensors stick onto the body adhesively (like Band-Aids) and transmit data to our smartphone app. One version “MyoBeat” uses a well established heart metric to provide continuous heart rate measurement (like a “chest strap” style sensor). A second version “MyoFit” uses proprietary algorithms to measures the energy output of other muscles. For instance, one on your quads while running can give you insight into how warmed up you are, how much work you are doing, fatigue, endurance, and recovery level. If you use two at the same time, it can show you your muscle symmetry (when asymmetry develops during exercise like running or bicycling, it can indicate the onset of an injury). Our goal is to get people excited about understanding how their bodies work.
Grey: My parents used to run a clinic that used muscle energy technology (sEMG) along with a special training method called Muscle Learning Therapy to cure people with RSI (Repetitive Strain Injury) and other work-related upper extremity disorders involving chronic pain. Each sEMG device they bought cost them $10K. I started to develop early symptoms of TMD (Temporomandibular Joint Disorder) when I was only 10, and my father used sEMG to teach me how to control and reduce my muscles’ overuse. The training worked, and I still have it under control today.
Years later, I decided to start a company to develop and commercialize more accessible / less expensive sEMG technology, with my mom as my investor. (My father has passed away, but I think he would have supported the idea.) At first we were going after a workplace safety service — I developed an algorithm that quantified people’s likelihood of developing an RSI injury in the future, and envisioned a prevention-based screening/monitoring service to offer to progressive companies. The feedback I got from VCs was that we needed to start with a bigger market. So we redesigned the product to make it small, cheap, and completely wireless. I also started working on a new set of sports-related algorithms to interpret muscle use into useful metrics.
Q: What impact has it had? What have you heard from users?
Grey: Having this new kind of tool at my disposal has really been a lot of fun, and has allowed me to run some new kinds of experiments that haven’t really been practical before.
For example, I wondered: for a given running speed, what cadence or stride rate would use the least energy, and so delay the onset of fatigue? I put sensors on my both quads, hamstrings, and calves. I created an audio track that increased from 120 – 170 bpm in increments of 5pm, 15 seconds on each. I kept my treadmill locked at 6.5 mph (my “comfortable pace”). By adding up the work done by all 6 muscles in the legs, I got a snapshot of the energy expenditure at each stride rate / cadence. The resulting curve [see graph above] answered my question: for me, at 6.5 mph, 130 bpm is my “sweet spot” that minimizes energy expenditure. It also showed a second trough in the graph, not as low as 130, but still pretty low, at 155 bpm. So if I need to run uphill or downhill, and want to keep the same speed but take shorter steps and still try to minimize energy burn as much as possible, I should shoot for 155 bpm.
Another test that these tools allow us to do is to figure out how recovered someone is from exercise. I did a test where I ran at a fixed speed every 24 hours (that’s not enough recovery time for me – I’m not in good shape). The first day, the muscle amplitude was about 1000 uV RMS (microvolts, amplitude). The second day, the amplitude started out at 500 uV and decreased from there. So the lack of sufficient recovery showed up in the data, which was quite interesting to see.
Whenever we have volunteers in the lab offering to help out (runners, usually) they geek out over these devices and the insight that they can get into the muscles of their bodies for the first time. We’ve had about 40 volunteers help out with muscle data gathering, and about 60 with heart rate testing.
Q: What makes it different, sets it apart?
Grey: Our design goals for our sensors are “good enough” data, wireless, long battery life, and comfort (wearability). Key to this is using a low-power, low-bandwidth radio. The trade-off is a much lower sample rate and a/d resolution than medical-grade sensors. Our sensor transmits processed data, not the raw data. However, our data is good enough for sports and fitness, where you want to see some predigested metrics and not raw graphs or frequency analysis. The benefit is that our battery life is 100 hours, and our sensor is small and light enough to attach using an adhesive patch. The up-side of an adhesive-based solution is that one-size fits all, it’s very comfortable, and there is no tight and annoying strap around your chest.
Q: What are you doing next? How do you see Somaxis evolving?
Grey: We are mainly focusing on improving the physical sensor itself: rechargeable battery, completely waterproof (current version is water resistant), and a smaller size. And maybe a medical-grade version with much higher sample rate and a/d resolution.
We also want to open up the hardware platform so that others can develop applications for it. For example, maybe someone wants to develop software for Yoga that uses muscle isolation to help do poses correctly. Or perhaps someone wants to focus on a weight-lifting application that assesses power and work done during lifting. We can envision many possibilities for sports, gaming, physical therapy, and health.
Q: Anything else you’d like to say?
Grey: I would love to hear from anybody who has ideas about potential uses of our technology! Also, we are fairly early-stage, so if anyone wants to work with us (individuals) or partner with us (companies) we definitely want to hear from you. You can reach me at firstname.lastname@example.org
Product: MyoLink platform: MyoBeat (heart) and MyoFit (muscle)
Website: www.somaxis.com (coming soon – there’s nothing there right now, but check back again soon)
Platform: Sensors stream data to an iPhone app (Android under development) and certain sports watches (Garmin, etc.)
Price: $25 for a starter set of 1 Module (MyoBeat or MyoFit) and 4 adhesive patches. Or you can buy 1, 2 or 3 Modules, with a one-year supply of patches, for $75, $125, or $170, respectively.
This is the 11th 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, contact Rajiv Mehta at email@example.com.
Inspired how powerful a connection he developed to the tracking of his runs, Chris Lukic has built a website to display data from a Nike sensor and put it in a social context. There are badges to reward users for various achievements, and something akin to a leaderboard for seeing how your runs compare to those of your friends — a little friendly competition to increase motivation. Chris has also spent a lot of time working on the visualization of correlations, such as grouping runs of a similar length together or looking at how your performance changes by the day of the week. He has a few ideas for incorporating other data sources that might have interesting correlations, but is looking for feedback from the QS community, so check out the video below and give his site a whirl!
Mike Schoeffler from Roadbud talks about the effect of noisy data on self-quantifying. A popular GPS running app had been giving him trouble – magically teleporting him and missing parts of his runs. He found it frustrating enough that he built his own app. Watch the video below to see the interesting discussion with the audience that cropped up around whether or not noisy data is really a problem. (Filmed at the NY Quantified Self Show&Tell #12 at ApK Media.)
Willempje Vrins and Leonieke Verhoog thought running was boring, and wanted to find a way to make it more beautiful and fun. They invented Figurerunning, and are building a community around it. With apps like Runkeeper, they run specific routes that make make shapes like hearts and soccer players. Then they share their drawings/runs on Facebook and ask people to guess what they are. Guess along with the audience at what their creative drawings represent! (Filmed at the Quantified Self Show&Tell meetup in Amsterdam at Mediamatic)
A meditative mandala in Nepal (photo credit: Wonderlane)
Do you meditate, run, or sleep? Ramesh Rao does all three. Not only that, but this grounded Professor of Electrical and Computer Engineering at UCSD tracks his heart rate and brain waves while he’s doing these activities.
We heard a bit about quantified meditation from Robin Barooah at a recent San Francisco QS meetup. Professor Rao takes it to another level. I had the pleasure of meeting him and hearing his story this past week. He graciously agreed to let us post his findings.
In his words:
Electrical signals that trigger the beating of the heart are not quite periodic and the larger the variation the healthier the heart! A transplanted heart shows very little heart variation. Alienated. Since 1965, when the first findings on HRV were reported, numerous studies have documented the correlation between lowered HRV measures and increased fatigue, stress, exhaustion both physical and mental.
A Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology concluded in 1996 that:
Heart rate variability has considerable potential to assess the role of autonomic nervous system fluctuations in normal healthy individuals and in patients with various cardiovascular and non cardiovascular disorders.
A lay reading of the scientific literature suggests that HRV entrains many physiological, psychological and emotional responses. As a result HRV is a rich, if garbled, source of invaluable information. For close to 21 months now, I have been gathering comprehensive HRV data during my early morning aerobic work and nightly yoga practice. I also have an assortment of interesting additional recordings: a four day long trace, a recording of the bliss of sedation during a colonoscopy procedure and meditation sessions.