Tag Archives: newyork

David Joerg: Building My Personal Operating System

David Joerg is a software developer in New York City and had some interest in personal data. Inspired by attending his first QS meetup in late 2013, he decided to take a deeper dive into the data he was collecting, add some new systems, and see if he could build something to help him better understand himself. What he ended up building was his own data dashboard, a personal operating system, that allowed him to see how he was doing across the various metrics he was interested in including, sleep, exercise, weight, unread emails, and more. In this talk, presented at the New York QS meetup group, David explains his process and what he learned from developing and using this system.

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Akshay Patil: Better Relationships Through Technology

“There was nothing in my life pushing me to to have these more intimate relationships, the few people I actually care about.”

When Akshay Patil was putting together the guest list for his wedding he realized that it had been a long time since he’d spoken with some of the the people he was inviting. Even with his good friends, he surprised by his lack of communication, his inability to stay connected. As anyone faced with this realization he decided to try and change, but the realities of life quickly crept back and as they say, old habits die hard. When he left his last job and began looking for projects to work on, this troubling area of his life crept back to the fore. Maybe there was something he could do better track and change his communication and relationships. Using his development skills, and the ability to gather data from his Android phone, he decided to build a system that helped him stay in touch with the people that mattered most to him. In this talk, presented at the New York QS meetup group, Akshay talks about what’s he’s learned from using this app, including when it fails.

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Shawn Dimantha: My Face, My Health

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Shawn Dimantha is always looking for easier ways to track his health. He uses a variety of self-tracking tools, but a few months ago he became interested in exploring what he could do given his engineering and health IT background. He was inspired by immersion, an MIT-developed email analysis tool, which helped him understand who he was communicating with, and by Wolfram Alpha’s Facebook analysis tool. Focusing on Facebook and the wealth of image-based data in his profile he asked himself if images could be a window into his health. After reading a research paper on the use of images to predict body mass index he decided to see what he could learn my implementing a similar procedure on his own images.

What Did Shawn Do?

I used photos from my Facebook account to track my health, the reason I did this because I wanted to see how a simple heuristic I used for tracking my health daily could be implemented in the online world given the huge amount of photos that are and have been shared on a daily basis. I notice when I gain or lose weight, am stressed or relaxed from my seeing my face in my mirror. I was partly inspired by the self-photo collages presented on YouTube.

How Did He Do it?

I selected photos of my face from my Facebook account, cropped out my face and used some software and manual tagging to measure the ratio of different fiducial points on my face (eye-eye length, and cheek to cheek length) over time to help serve as a proxy for my health.

What Did He Learn

Facial image data needs to be cleaned and carefully selected. Face shapes are unique and need to be treated as such. Data that is not present is often more telling than what is present. Life events effect my weight and should be put into context; however causation is harder to determine than correlation. By being more conscious of my score and I can change my behavior before things get off track.

Right now I’m turning this into a product at Enfluence.io where I’m focused on using it to help with preventive health.

Tools

Facebook (my own images)
Python / OpenCV

Slides from Shawn’s talk are available here.

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Cliff Atkinson: Storyboarding the Psyche

CliffA_Storyboard

Cliff Atkinson is a consultant who helps people tell their stories and showcase their data in clear and understandable ways. It’s no surprise that when he became interested in understanding himself he turned to his experiences with visual storytelling. In 2012, at a New York QS meetup, Cliff spoke about how he’s embarked on a project to “quantify the “unconscious.”

What Did He Do?
Cliff began this project because he was noticed that there were “recurring patterns of procrastination and motivation” going on in his life. He began trying to understand them by turning to the large body of literature on human psychology. Then he asked himself, “Would it be possible to use some quantitative methods to track what was happening.” Using what he’d learned in his research and his experiences he decided to track his body, emotions, and mind.

How Did He Do It?
Cliff used his expertise and knowledge around visual storytelling to create an interesting system of visual diaries with which he could record information in his three areas of interest: the body, emotions, and the mind. Using Penultimate, and iPad app for sketching and notation, along with some clip art, he tracked physical, emotional, and cognitive events.

What Did He Learn?
The process of creating a space to reflect and record how he’s feeling across these three chosen domains has created a space for Cliff to better understand himself and how his mind works. This is still a work in progress and it sounds like Cliff is still exploring how to better understand the data he’s capturing over a longer period of time and even correlating it with other information such as his work and speaking engagements.

“One of the models for therapy is that somebody else helps you. I think with the quantified self and the things we’re doing we can take some of that power into our own hand and start to come to some personal understanding of what’s going on in our own lives.”

Tools
Penultimate

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QS NYC Meetup Recap

This guest post comes to us from Konstantin Augemberg who covers many interesting Quantified Self topics and his personal tracking experience on the wonderful MeasuredMe blog.  

On Monday, September 30, Quantified NYC group has held its 23th meetup. The event was graciously hosted by Projective Space which offers collaborative community space to over 60 startups. With over a hundred people in attendance, interesting demos and inspiring presentations (quantifying Starcraft gaming skills, predicting choice of clothes based on weather forecast, and other self-quantified awesomeness!), it turned out to be a great evening. Here is my brief report on what I saw and loved:

We started with our Demos session during which QS entrepreneurs showcased their products and services:

  • David Joerg (@dsjoerg) presented his GGTracker, web service that uses advanced analytics to help Starcraft players to track their stats and quantify and improve gaming skills
  • Paula Murgia presented Personal Beasties app that helps people to cope with anxiety, fatigue and stress by using simple breathing exercises
  • Stefan Heeke (@Stefan_Heeke) showcased My Online Habits, a webapp that uses Gmail and Google data to help analyze your productivity and communications habits
  • Mike McDearmon (@Mike_McDearmon) demoed an awesome online dashboard that he built to visualize his outdoors activities.

Steven Dean (@sgdean) also introduced the audience to our new co-organizer, Amy Merrill. Amy will be in charge of QSXX NYC group that will be organizing meetups for women in NYC area.

The Show & Tell session was opened by Mette Dyhrberg (@mettedyhrberg) and her “The Pomodoro Recovery” presentation. Following the bouncing castle accident, Mette has been diagnosed with concussion and was recommended to rest and avoid using electronic devices in order to recover. She started tracking her symptoms, diet, and resting and working habits using Pomodoro method and Mymee app. The lack of progress has prompted her to look at her tracking data, after which she realized that she may have been misdiagnosed. The visit to another doctor has revealed that she sustained a neck injury, which luckily, could be fixed right on the spot. The treatment procedure helped her to feel better almost immediately. You can watch Mette’s presentation here.

In “Quantifying What to Wear”, Andrew Paulus (@andrewcpaulus) shared how he used self-tracking to measure impact of weather on his choice of clothes. It started when Andrew noticed that one of his morning habits included checking weather on his phone in order to decide what to wear on that day. That led to an idea to measure efficiency of this process, by tracking his choice of clothes and then assessing at the end of the day, if the choice was correct. His first attempt at quantifying weather and wardrobe was unsuccessful, due to some flaws in methodology and measurement (e.g., the weather data was collected at different times of the day; the clothes data was not very well structured). Andrew then has revised the methodology, by subscribing to more reliable and comprehensive weather data from Farmer’s Almanac, and logging wardrobe data in a more consistent manner. His girlfriend kindly agreed to co-participate in this experiment. After six months of tracking, Andrew looked at their data. He found that the overall, he tended to be slightly more accurate in choosing what to wear, compared to his girlfriend: his accuracy rate was 78%, vs. her rate of 74%. Another interesting finding was that his choices were more weather appropriate. The correlation between the clothes and weather was nearly 0.7 for him, and nearly 0 .1 for his girlfriend, which suggests that her choices are often influenced by many other factors, not just weather. You can see the full presentation here.

Amy Merrill (@amyjmerrill) shared her experiences with “Sleep Tracking with Jawbone Up”. Since April 2013, she has been tracking her sleep (deep sleep phase, in particular) using Jawbone Up, as well as social and work related activities using Google Calendar. By analyzing the patterns in her data, she was able to see how certain activities affect her deep sleep. In particular, she learned that more physical activity and sleep deprivation led to more deep sleep, where as restful days tend to result in more light sleep. Certain social activities like attending wedding and taking trips on tour bus have also had a considerable impact on quality of her sleep. For the next phase, she plans to include some aspects of the diet, including consumption of alcohol, caffeine and over-the-counter drugs. You can watch Amy’s presentation here.

The session was concluded by Andrew Tarvin’s (@HumorThatWorks) funny and inspiring presentation “The Perfect Day”, in which he discussed the tracking system that he used to build some new habits. Andre has been rating each day based on the number of goals that he achieved (e.g., waking up without snoozing the alarm, do something active for 20+ minutes, eat at least 4 fruites a day, etc.) The days with at least 3 goals met were defined as “quality days”, and the days with all 5 goals accomplished were rated as “perfect”. Andre learned that the strive for perfection was the most demotivating factor: missing one goal earlier in the day often resulted in giving up on all other habits as well. Waking up without snoozing was the most influential habit in that regard. He also learned that the “streaks” of quality and perfect days was the most motivational factor; once he had several consecutive successful days in a row, it was much easier to continue meeting the goals. Andre has been using this system for three years, and plans to continue using it to acquire new habits. You can read more about his system on his site. You can watch video of the presentation here.

As always, before and after the sessions, I had a chance to mingle and meet a lot of interesting people. Special shout out to Stefan Heeke, Mike McDearmon, Sylvia Heisel, Michael Moore and Dave Comeau.

 

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David Sweet on Fist-Sized Volume and Weight Control

It seems that food tracking can have an enormous impact on weight loss and weight control, but counting calories can be difficult. David Sweet was looking to lose weight and wanted to use a system that kept him engaged for a long period of time. He devised a unique system to track his food – the Fist-Sized Volume. Watch this interesting talk, filmed at the New York QS Meetup, to learn how he did it and what he learned (stick around for the great Q&A).

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Robert Carlsen on Tracking Weight

“I was starting to feel a little bit out of control.”

Robert Carlsen used to be an amateur bike racer. When he moved to New York and stopped racing he found that his weight was slowly creeping up. He was still leading an active lifestyle, but he soon realized that most of daily food choices were the result of guess work. In this video, filmed at the New York City QS Meetup, Robert explains how he used different apps and tools to track his caloric inputs and outputs in order to move towards his goal weight.

Tools:
Body Weight Simulator
Lose It!
Runkeeper
Strava
MobileLogger
Withings Scale + App
Nike Fuelband

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Doug Kanter on Data, Diabetes, and Marathon Training

Doug Kanter has been a Type 1 diabetic for 26 years. Through this time he’s come to learn more about his disease by using many data-gathering tools and his own work in visual analysis at the NYU ITP program. We’ve featured Doug’s compelling work here on the blog before and we were excited to hear him talk at the NY QS Meetup about his new project to understand how marathon training and running effect his blood sugar and insulin treatment.

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Self Expression From Performance Data

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Typically when we think about Quantified Self and the associated collection and visualization of personal data we’re left struggling in the world of charts, graphs, and other well-worn visualizations. That’s not to disparage those of you who love spending some time tinkering in Excel. Those are valuable tools for understanding and there is a good reason we rely on them to tell us the stories of our data. It’s important to realize that those stories rooted in data aren’t always just about finding trends, searching for correlations, or teasing out significant changes. Sometimes data can represent something more visceral and organic – the expression of a unique experience.

Vincent Boyce is a an artist and designer who spends his free time riding on asphalt and water. Those experiences on his longboard and surfboard led him to starting thinking about how his rides, his performances, could be used as inputs for generating art and “exposing the hidden narrative.” After some tinkering with hardware and software Rideware Labs was born. Vincent has designed and built a prototype sensor pack and custom interface that ingests data from his riding and outputs unique visual representations. As you can see above, these aren’t your typical bar charts.

In his great talk filmed at the New York QS Meetup Vincent describes his motivation behind building his prototype system and his goals for future versions.

This is a great first step in turning data rooted in performance into artistic representations of self-expression. What do you think? What kind of data would you like to see hanging on your wall as works of art? Let us know in the comments!

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The New York QS Meetup Survey

Today’s post comes to us from Andrew Paulus, a member of the New York QS Meetup group.

QS Meetups are a great time to see demos, hear interesting show & tells, and talk to a few people after. With hundreds of members just in the local New York group it is impossible to meet everyone and hear about why they started tracking, get their thoughts on QS, or find out what tools/apps are popular. Consequently I created a survey to answer these questions and sent it out to the NY group. We received 102 responses that helped to paint a picture of who we are as a community. Many of my assumptions were confirmed, but there were also a few surprises. Let’s dig into the data!

Demographics & Basics

Age distribution: mean = 36.2 years old, youngest = 23 years old, oldest = 74 years old
Gender distribution: 67% male / 33% female
Currently working in a QS related company or have created a QS tool: 30%
Members who have a chronic health issue: 70%

Reflecting on the heavy male slant in the respondents made me wonder if there has been enough attention given to how QS tools could specifically help women. In the entire survey I neglected to mention any QS tools that are female-specific (such as pregnancy or menstrual cycle trackers), which is surely an area where self-tracking might play a prominent role.

We started by asking how many people self-track and what prompted them to start tracking. Ninety respondents, 94% of the sample, were actively engaged in a self-tracking practice. When we asked why individuals started self-tracking we found that in our sample the majority were interested in “understanding myself” or just plain curious. Given the common connection between QS practices and health we found it very interesting that health ranked fourth, tied with “work in the field or would like to start a business in the field.”

QSTOOLS_NYCWhat Tools Are You Using?

The top 10 most used tools were: Mint, Personal GoogleDoc/spreadsheet, Other, Foursquare, 23andMe, Fitbit, Runkeeper, Zeo, MyFitnessPal, and Goodreads with the Nike+ Fuelband and Lift just missing the top 10. Clearly personal tracking solutions are still prominent QS tools. I was surprised that over 20% of respondents had taken part in genome indexing through 23andMe, I didn’t realize how prominent this was in the QS community. (Click the image to see the breakdown of tools.)

Data Sharing & Privacy

Privacy is still important. Only 49% of respondents share their data with anyone and only 27% of respondents said they were ‘vey open’ to sharing their data with others.

Share data with someone else: 51%
Share data with a spouse/partner: 39%
Share data with a health professional: 14%

QS Tool Design Considerations

Over 60% of respondents said that the ability to export the data was important. Many of the major tools currently on the market do not allow you to easily export your data and you have to wonder if consumer pressure will finally get them to open up this data to the users. This also brings up two other points: first, whether there will be a standardization of data metrics across different platforms, and second, will there be a dominant platform or many smaller platforms that allow you to aggregate your data and help you derive meaning from the large amounts of data collected with various tools. (Editors note: Please see Gary’s wonderful post about “The QS World I’d Like to Live In“)

QSTOOLS_DESIGN_NYC

 

Thanks Andrew for putting out that survey and sending us the results. If you’re interested in more data from this survey you can view the full results and Andrew’s notes here.

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