Tag Archives: heart rate
Gordon Bell has been involved with self-tracking for over a decade. From his ground-breakign MyLifeBits project to his popular book on the possibilities of a fully digital life he is constantly thinking about new ways we can understand ourselves through the data we collect. We are always excited to see him at our QS events and were especially happy to have him reach out to us about presenting at our last Bay Area QS meetup.
Gordon has experienced two heart attacks, one in 1983 and another in 1996, two double bypasses, and currently is living with his third pacemaker. It probably isn’t surprising given his medical history that he has a keen interest in understanding his heart. In this talk Gordon describes what he’s been learning from the data collected from his pacemaker and the 320 days of heart rate and activity data he has collected with his Basis watch.
We’re also excited to have Gordon joining us at our upcoming QS15 Conference & Activate Expo. We’ve made some early bird tickets available for readers of the Quantified Self blog (for a limited time): Register here!
Enjoy these articles, examples, and visualizations!
OpenNotes: ’This is not a software package, this is a movement’ by Mike Milliard. I’ve been following the OpenNotes project for the last few years. There is probably no better source of meaningful personal data than a medical record and it’s been interesting to see how this innovative project has spread from a small trial in 2010 to millions of patients. This interview with Tom Delbanco, co-director of the OpenNotes project, is a great place to learn more about this innovative work.
Beyond Self-Tracking for Health – Quantified Self by Deb Wells. It was nice to see this flattering piece about the Quantified Self movement show up on the HIMSS website. For those of you looking to connect our work and the broader QS community with trends in healthcare and health IT you should start here.
So Much Data! How to Share the Wealth for Healthier Communities by Alonzo L. Plough. A great review of the new book, What Counts: Harnessing Data for America’s Communities, published by the Federal Reserve Bank of San Francisco and the Urban Institute. The book is available to read online and in pdf format.
The Ultimate Guide to Sleep Tracking by Jeff Mann. A great place to start if you’re interested in tracking sleep or just want to learn more about sleep tracking in general.
What RunKeeper data tells us about travel behavior by Eric Fischer. We linked to the recent collaboration between Runkeeper and Mapbox that resulted in an amazing render of 1.5 million activities a few weeks ago. The folks over at Mapbox aren’t just satisfied with making gorgeous maps though. In this post, Eric, a data artist and software developer at Mapbox dives into the data to see what questions he can answer.
General Wellness: Policy for Low Risk Devices – Draft Guidance for Industry and Food and Drug Administration Staff . On Friday, January 16, 2015, the Food and Drug Administration released a draft of their current approach to regulating “low risk products that promote a healthy lifestyle.” These guidelines point to a stance that will allow many of the typical self-tracking tools currently in use today to remain outside the regulations normally associated with medical devices. (A quick overview of this document is also available from our friends at MobiHealthNews)
The Great Caffeine Conundrum. A wonderfully thorough post about using the scientific process, statistics, and self-tracking data (Jawbone UP) to answer a seemingly simple question, “Does eliminating caffeine consumption help me sleep better?”
Four Years of Quantified Reading by Shrivats Iyer. Shrivels has been tracking his reading for the last four years. In this post he explains his process and some of the data he’s collected, with a special emphasis on what he’s learned from his 2014 reading behavior.
Pretty Colors by Chanlder Abraham. Chandler spent his holiday break exploring his messaging history and creating some amazing visualizations. Above you see a representation of his messaging history with the 25 most contacted people since he’s began collecting data in 2007.
Heart Rate During Marriage Proposal by Reddit user ao11112. Inspired by another similar project, this ingenious individual convinced his now fiancé to wear a hear rate monitor during a hike. Unbeknownst to her, he also proposed. This is her annotated heart rate profile.
Help CDC Visualize Vital Statistics by Paula A. Braun. The CDC has a new project based on the idea that better visualization can make the data they have more impactful. If you’re a data visualizer or design consider downloading the CDC Vital Statistics Data and joining #vitalstatsviz.
From the Forum
We hope you enjoy this week’s list!
The Global Open Data Index by The Open Knowledge Foundation. This isn’t an article, but rather an really nice portal to explore open data sets from around the world.
Eight things we learned about HealthKit from Duke, Oschner by Jonah Comstock. An interesting piece here detailing how two large healthcare systems are using Apple’s Healthkit.
Connected Health: Improving Patients’ Engagement and Activation for Cancer-Related Health Outcomes by the President’s Cancer Panel. Very short publication here that outlines how the President’s Cancer Panel is thinking about new changes in the health system and health technology.
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images by Anh Nguyen, Jason Yosinksi, and Jeff Clune. This in not a typical entry into our weekly What We’re Reading as it doesn’t appear to be directly related to self-tracking or Quantified Self. However, I found it fascinating and a great reminder that algorithms are not infallible.
Visualizing HR, HRV, and GSR While Watching ‘Interstellar’ by Bob Troia. Inspired by a Reddit user who tracked his HR while viewing Interstellar, Bob Troia set out explore his full physiological response by tracking heart rate, heart rate variability, and galvanic skin response. Some great data in here!
Stress Snail by Pavel Zakharov. Pavel uploaded this unique visualization to our QS Forum earlier this week. This visualization represents his heart rate, activity, and stress during a particularly stressful day when he was completing a driving test. If you have ideas or thoughts on the visualization make sure to share them in our forum!
This Week on QuantifiedSelf.com
Greg Schwartz: Quantified Dating
David Joerg: Building My Personal Operating System
Enjoy this week’s list!
Flipping Primary Health Care: A Personal Story by Kedar S. Mate and Gilbert Salinas. We’re leading off this week with a fascinating case study that describes what happened when one patient, Gilbert Salinas, “flipped the clinic.” After deciding to accept fellowship that would move him from California to Cambridge, MA he worked with his care team to take control of many of the tasks typically performed in the clinic.
Most importantly, I feel happier and healthier, and I am amazed that I have been able to accomplish my goal of being healthy during this year away from my providers. It has transformed my sense of what is possible and has encouraged me to take further ownership of my health.
A Case for Autonomy & The End of Participatory Medicine by Hugo Campos. I’m constantly in awe of our friend and QS community member, Hugo Campos. As a leader in the fight for access to personal data (see this great NPR piece from 2012) he’s been an inspiration for our own ongoing Access Matters work. In this post, Hugo makes the case for focusing less of patient participation in the medical system, and re-orienting towards improving patient autonomy and self-determination.
Health Data Outside the Doctor’s Office by Jon White, Karen DeSalvo, and Michael Painter. In this short post, the smart folks at RWJF introduce the new JASON group report, Data for Individual Health, which
“[…] lays out recommendations for an infrastructure that could not only achieve interoperability among electronic health records (EHRs), but could also integrate data from all walks of life—including data from personal health devices, patient collaborative networks, social media, environmental and demographic data and genomic and other “omics” data.”
A Systematic Review of Barriers to Data Sharing in Public Health by Willem van Panhuis and colleagues. In this review article, the authors outline twenty specific barriers standing in the way of sharing data that could improve global public health programs. They include numerous examples of the technical, motivational, economic, political, legal, and ethical barriers that prevent more sharing across public health systems.
#WeAreNotWaiting at the Fall 2014 D-Data ExChange: The Stars Are Aligning by Mike H. QS Labs was unfortunately unable to attend the Fall 2014 D-Data ExChange, but were excited to read this great summary of the event.
The Quantified Self and Humanities Best Friend by Kevin P. Kevin found out that he could track his dog, Lilo, along with himself when he went for walks and runs. In this short post he outlines his process, and the barriers he ran into, for collecting data from his different devices to show his progress on a recent 5k walk.
Follow-up study: on the working time budget of a university teacher. 45 years self-observation pdf hereby Dimitar Todorovsky. Dimitar is a recently retired researcher and professor of Chemistry and Pharmacy at the University of Sofia in Bulgaria. In this journal article he outlines his findings from tracking his time every day over his 45-year career. Most striking to me is that he averaged 10hr of work per calendar day for the entire 45-year period.
Heart Rate (bpm) during marriage proposal by reddit user sesipikai. Going to Rome to surprise your fiancé to be? Why not record your excitement and nervousness by wearing a heart rate chest strap!
To Big to Fail by Nicholas Felton. In this great video presentation Nicholas Felton describes the process behind building the latest in his series of Annual Reports. You can also check out the full 2013 Annual Report here.
From the Forum
Counterintuitive HRV Measurements
Active, Athletic Folks With Asthma Tracking Their Performance
Mobile Health and Fitness Apps Privacy Study
OP Innovations Sensors
Timer/logger/tracker–what kind of gadget am I looking for?
In response to the much anticipated reveal of the Apple Watch I did a bit of digging around to find out where we stand with wrist-worn wearable devices. I found over 60 different devices. The following list focuses on self-tracking tools, I intentionally left out those that work only as notification centers or secondary displays for your phone. I’m sure this isn’t all of them, but it’s as good a place to start as any. If you’re using one of these devices to learn something about yourself, or you’re just interested in these type of wearable tools we invite you to join us in San Francisco on June 18-20, 2015, for our QS15 Conference & Exposition.
(Thank you to all those who commented here, on Twitter, and on our Facebook group pointing us to additional devices to add!)
Sensors: Accelerometer, Heart Rate (optical), Blood Oxygen, Temperature
Sensors: Accelerometer, Pulse Oximeter, Temperature
Sensors: Accelerometer, Gyroscope, Heart Rate (optical)
Sensors: Materials state the ZenWatch houses a “bio sensors and 9-axis sensor.” I assume optical heart rate, accelerometer, and gyroscope.
Sensors: Accelerometer, Gyroscope, Heart Rate (optical)
Sensors: Accelerometer, Temperature, Pressure
Epson Pulsense Band/Watch
Sensors: Accelerometer, Heart Rate (optical)
Fatigue Science Readiband
Kiel Gilleade has been interested in measuring and visualizing physiological data for quite a while. In 2011, he presented his BodyBlogger project at the 2011 QS Europe Conference. In that talk he described what he learned from tracking and exploring a year of continuous heart rate data. This year, at the 2014 QS Europe Conference, Kiel returned to talk about a new project, Rhythmanalysis. Rhythmanalysis was a project centered on “visualising the biological rhythms of employees at different workplaces.” In this short talk, Kiel describes his experience working on this project and some of the lessons he learned along the way.
If you’re interested in learning more about this work I highly suggest you visit Kiel’s website where he has additional videos of visualizations he’s been working on that use data collected as part of this project.
Mark Drangsholt has been dealing with an issue with his heart since he was a young man. Since his early twenties, when he as diagnosed with paroxysmal atrial tachycardia he’s had to deal with irregular heart rhythms. In this talk Mark explains how the transition into adulthood negatively impacted his health and then how he used self-tracking and a focused athletic program to help him reduce his weight and improve his health. Most show&tell talks would end there, but Mark still had the irregular rhythm issue to deal with. After what he describes as an episode that made him think, “This is it. I’m going to die.” he decided it was time to apply his self-tracking process in order to understand his heart rhythm disorder and possible triggers. Mark also decided to go one step further and apply the principles of case-crossover design to his tracking methodology. Watch his talk below and keep reading to learn a bit more about why you might want to consider using case-crossover design in your self-tracking projects and experiments.
The following excerpt from the QS Primer: Case-Crossover Design by Gary Wolf provides a great background for his method:
Mark’s self-tracking data didn’t naturally fit with any of these approaches. To understand whether these triggers actually had an effect on his arrhythmias, he used a special technique originally proposed by the epidemiologists Murray Mittleman and K. Malcolm Maclure. A case-crossover design is a scientific way to answer the question: “Was the patient doing anything unusual just before the onset of the disease?” It is a design that compares the exposure to a certain agent during the interval when the event does not occur to the exposure during the interval when the event occurs.
Using this method, Mark discovered that events linked to his attacks included high intensity exercise, afternoon caffeine, public speaking to large groups, and inadequate sleep on the previous night. While these were not surprising discoveries, it was interesting to him to be able to rigorously analyze them, and see his intuition supported by evidence. “A citizen scientist isn’t even on the conventional evidence pyramid,” Mark notes. “But you can structure a single subject design to raise the level of evidence and it will be more convincing.”
A driver made a left turn from a stright-only lane right in front of me as I was proceeding straight through the intersection from my straight or left lane. I have occasionally turned on the accelerometer and gyro logging in FluxStream Capture while I drive. This time around, I have even more data. You can see the massive deceleration and the associated spike in my heart rate and drop in my beat spacing (RR). I haven’t pulled my GPS data yet, but I was able to spot this easily in the FluxStream graph. Those dips in the Acceleration data really stand out. Interestingly, my heart rate also reflects my mood afterward.
Initially relieved that I didn’t get hit this time, then enraged that it had nearly happened again, calming slowly as I composed in my head a letter to the City of Addison imploring them to add more signage at that intersection.
A quick post here to highlight some interesting developments in the heart rate tracking space. Tracking and understanding heart rate has been a cornerstone of self-tracking since, well since someone put two fingers on their neck and decided to write down how many pulses they felt. We’ve come a long way from that point. If you’re like me tracking heart rate popped up on your radar when you started training for a sporting event like a marathon or long distance cycling. Like many who used the pioneering devices from Polar it felt a bit odd to strap that hard piece of plastic around my chest. After time, and seeing the benefits of tracking heart rate, it became part of my daily ritual. Yet, for all the great things heart rate monitoring can do for physical training, there have been very few advances to provide people with a noninvasive method. That is, until now.
Thearn, an enterprising Github user and developer, has released an open source tool that uses your webcam to detect your pulse. The Webcam Pulse Detector is a python application that uses a variety of tools such as OpenCV (an open source computer vision tool) to “find the location of the user’s face, then isolate the forehead region. Data is collected from this location over time to estimate the user’s heartbeat frequency. This is done by measuring average optical intensity in the forehead location, in the subimage’s green channel alone.” If you’re interested in the research that made this work possible check out the amazing work on Eulerian Video Magnification being conducted at MIT. Now, getting it to work is a bit of a hurdle, but it does appear to be working for those who have the technical expertise. If you get it working please let us know in the comments. Hopefully someone comes along that provides a bit of an easier installation solution for those of us who shy away from working in the terminal. Until then, there are actually quite a few mobile applications that use similar technology to detect and track heart rate:
Let us know if you’ve been tracking your heart rate and what you’ve found out. We would love to explore this space together.
Last week we brought you a look into some of the interesting Quantified Self tools that were debuted at CES. Here are a few more we noticed from the deluge of CES coverage. Thanks to MobiHealthNews, Gizmodo, Engadget and many QS friends for the tips.
Withings Smart Body Analyzer (WS-50)
The latest wireless scale from Withings adds some interesting new sensors: resting heart rate, ambient air quality (CO2) and room temperature. The combination of physiological and environmental monitoring, while simple in this case, opens many new possibilities for Quantified Self projects.
Measures: Weight, BMI, Fat Mass, Heart Rate, Room Temperature, Room CO2
The Zensorium Tinke is a small sensor and companion app for iOS devices dedicated to helping users understand their health and wellness. This is a really interesting variation on the emerging theme of Heart Rate and Heart Rate Variability self-monitoring. The Tinke has no battery and no screen. Instead, the small optical sensor plugs directly into the iPhone.
Measures: Heart Rate, Heart Rate Variability, Blood Oxygen, Respiratory Rate
A similar approach is used by the Masimo iSpO2, where the focus is on blood oxygenation.
Measures: Blood Oxygenation, Heart Rate, Perfusion Index
The Mio Alpha boasts of continuous and strapless heart rate measurement. Using technology developed by Phillips, the Alpha uses optical heart rate sensing at the wrist and a soon to be released mobile app. What once seemed like difficult technical magic is on the verge of becoming commonplace.
The Mio Measures: Heart Rate
Sync: Bluetooth 4.0