Tag Archives: qseu14
Cathal Gurrin is a researcher at Dublin City University and the University of Tsukuba. He’s also an expert in the field of visual and data-driven lifelogging. Since 2006 he’s collected over 14 million passively collected images from different wearable cameras. Add his other sensors and he’s nearing over 1TB per year of self-tracking data. In this talk, presented at our 2014 Quantified Self Europe Conference, Cathal describes what he’s learned over the last eight years and what he’s working on in his research group including search engines for lifelogging as well as privacy and storage issues.
One interesting aspect of personal data is how it can reveal what is unique about you. Nowhere is this more true than with genetic information coming from DNA testing kits. However, people are still at an early stage on how they apply that information to their lives. Ralph Pethica, who has a PhD in genetics, was interested in what his DNA could tell him about how to train more effectively. His findings were presented as an ignite talk at the 2014 QS Europe Conference.
What did Ralph do?
Ralph loves to surf. When it is the off-season, he trains so that his body will be in good condition for when the warm weather rolls back around. He used genetic research to inform how he designed his training plans.
How did Ralph do it?
Ralph used a 23andMe kit to find out his genetic profile. He researched those genes that have been found to have an impact on fitness to see his body should respond to exercise. For example, did he possess genes that gave him an advantage in building muscle with resistance training? He then modified his training routines to take advantage of this information and monitored his results (using the Polar watch and a Withings scale) to see whether his assumptions held up.
What did Ralph learn?
Ralph found out that he has genetic disadvantages when it came to strength training. This told him that progress in this area depended more on his lifestyle. In particular, he found that eating immediately after working out was important.
When it came to cardio exercise, he had a number of genetic advantages. The unexpected downside to this is that his body adapts quickly to any training regimen, resulting in a plateau. To get around this, he varied his training plan and monitored his results. On one day, he would cycle at a steady rate, while the next, he would use high-intensity intervals. His body seemed to respond to the varied training plan and he hit fewer plateaus. Without knowing which genes he possessed, and reading current research on those genes, it is unlikely that he would have discovered these effective customizations to his training plan.
Ralph has taken what he’s learned and built a tool called Genetrainer to help people use their genetic information to inform their fitness plains. You can check it out here.
Tools: Genetrainer, 23andMe, Polar RCX5, Withings Smart Body Analyzer
Like anyone who has ever been bombarded with magazine headlines in a grocery store checkout line, Kouris Kalligas had a few assumptions about how to reduce his weight and improve his sleep. Instead of taking someone’s word for it, he looked to his own data to see if these assumptions were true. After building up months of data from his wireless scale, diet tracking application, activity tracking devices, and sleep app he spent time inputing that data into Excel to find out if there were any significant correlations. What he found out was surprising and eye-opening.
This video is a great example of our user-driven program at our Quantified Self Conferences. If you’re interest in tell your own self-tracking story, or want to hear real examples of how people use data in their lives we invite you to register for the QS15 Conference & Exposition.
One of the benefits of long-term self-tracking is that one builds up a toolbox of investigatory methods that can be drawn upon when medical adversity hits. One year ago, when Mark Drangsholt experienced brain fog during a research retreat while on Orcas Island in the Pacific Northwest, he had to draw upon the self-tracking tools at his disposal to figure out what was behind this troubling symptom.
Watch this invaluable talk on how Mark was able to combine his self-tracking investigation with his medical treatments to significantly improve his neurocognitive condition.
Here is Mark’s description of his talk:
What did you do?
I identified that I had neurocognitive (brain) abnormalities – which decreased my memory function (less recall) – and verified it with a neuropsychologist’s extensive tests. I tried several trials of supplements with only slight improvement. I searched for possible causes which included being an APOE-4 gene carrier and having past bouts of atrial fibrillation.
How did you do it?
Through daily, weekly and monthly tracking of many variables including body weight, percent body fat, physical activity, Total, HDL, LDL cholesterol, depression, etc. I created global indices of neurocognitive function and reconstructed global neurocog function using a daily schedule and electronic diary with notes, recall of days and events of decreased memory function, academic and clinical work output, etc. I asked for a referral to a neuropsychologist and had 4 hours of comprehensive neurocog testing.
What did you learn?
My hunch that I had developed some neurocognitive changes was verified by the neuropsychologist as “early white matter dysfunction”. A brain MRI showed no abnormalities. Trials of resveratrol supplements only helped slightly. There were some waxing and waning of symptoms, worsened by lack of sleep and high negative stress while working. A trial with a statin called, “Simvastatin” (10 mg) began to lessen the memory problems, and a dramatic improvement occurred after 2.5-3 weeks. Subsequent retesting 3 months later showed significant improvement in the category related to white matter dysfunction in the brain. Eight months later, I am still doing well – perhaps even more improvement – in neurocog function.
Cors Brinkman is a media artist and student. In June of 2013, he started a project to keep track of himself. He decided to start with LifeSlice, a tool to have your computer keep track of your behavior by taking a picture, screenshot, and location data every hour. After experimenting with that system Cors added in mood tracking to round out his data collection. In this talk, presented at the 2014 Quantified Self Europe Conference, Cors describes his process and some of the interesting ways he visualized and analyzed his thousands of self-portraits.
We’re always interested in the way individuals with chronic conditions use self-tracking to better understand themselves. A great example of this is our good friend, Sara Riggare. Sara has Parkinson’s Disease and we’ve featured some of her amazing self-tracking work here before. At the 2014 Quantified Self Conference, Sara gave a short talk on what she feels is her most troublesome symptom: freezing of gait. In this talk, she explains why it’s such a big part of her daily life and how she’s using new tools and techniques to track and improve her gait.
Paul LaFontaine was interested in understanding his anxiety and negative emotional states. What was causing them? When were they happening? What could he do to combat them? Using TapLog, a simple Android-based tracking app (with easy data export), Paul tracked these mental events for six months as well as the triggers associated with each one. In this talk, presented at the 2014 Quantified Self Europe Conference, Paul dives deep in to the data to show how he was able to learn how different triggers were related to his anxiety and stress. While exploring his data, he also discovered a few surprising and profound insights. Watch his great talk below to learn more!
Jenny Tillotson is a researcher and fashion designer who is currently exploring how scent plays a role in emotion and psychological states. As someone living with bipolar disorder, she’s been acutely aware of what affects her own emotions states and has been exploring different methods to track them. In this talk, presented at the 2014 Quantified Self Europe Conference, Jenny discusses her new project, Sensory Fashion, that uses wearable tracking technology and scent and sensory science to improve wellbeing. Be sure to read her description below when you finish watching her excellent talk.
You can also view the slides here.
What did you do?
I established a new QS project called ‘SENSORY FASHION’, funded by a Winston Churchill Fellowship that combines biology with wearable technology to benefit people with chronic mental health conditions. This allowed me to travel to the USA and meet leading psychiatrists, psychologists and mindfulness experts and find new ways to build monitoring tools that SENSE and balance the physiological, psychological and emotional states through the sense of smell. My objective was to manage stress and sleep disturbance using olfactory diagnostic biosensing tools and micro delivery systems that dispense aromas on-demand. The purpose was to tap into the limbic system (the emotional centre of our brain) with aromas that reduce sleep and stress triggers and therefore prevent a major relapse for people like myself who live with bipolar disorder on a day to day basis. I designed my own personalized mood-enhancing ‘aroma rainbow’ that dispenses a spectrum of wellbeing fragrances to complement orthodox medication regimes such as taking mood stabilizers.
How did you do it?
Initially by experimenting with different evidence-based essential oils with accessible clinical data, such as inhaling lavender to aid relaxation and help sleep, sweet orange to reduce anxiety and peppermint to stimulate the brain. I developed a technology platform called ‘eScent’ which is a wearable device that distributes scent directly into the immediate vicinity of the wearer upon a biometric sensed stimuli (body odor, ECG, cognitive response, skin conductivity etc). The scent forms a localized and personalized ‘scent bubble’ around the user which is unique to the invention, creating real-time biofeedback scent interventions. The result promotes sleep hygiene and can treat a range of mood disorders with counter-active calming aromas when high stress levels reach a pre-set threshold.
What did you learn?
I learnt it is possible to track emotional states through body smells, for example by detecting scent signals that are specific to individual humans. In my case this was body odor caused by chronic social anxiety from increased cortisol levels found in sweat and this could be treated with anxiolytic aromas such as sweet orange that create an immediate calming effect. In addition, building olfactory tools can boost self-confidence and communication skills, or identify ‘prodromal symptoms’ in mood disorders; they learn your typical patterns and act as a warning signal by monitoring minor cognitive shifts before the bigger shifts appear. This can easily be integrated into ‘Sensory Fashion’ and jewelry in a ‘de-stigmatizing’ manner, giving the user the prospect of attempting to offer them some further control of their emotional state through smell, whether by conscious control or bio-feedback. The next step is to miniaturize the eScent technology and further explore the untapped research data on the science of body (emotional) odor.
Today’s post comes to use from Freek Van Polen. Freek works at Sense Observations Systems, where they develop passive sensing applications and tools for smartphones. At the 2014 Quantified Self Europe Conference Freek led a breakout session where attendees discussed the opportunities, pitfalls, and ethical challenges associated with the increasing amount of passive data collection that is possible through the many different sensors we’re already carrying around in our pockets. We invite you to read his short description of the breakout below and continue the conversation on our forum.
Passive Sensing with Smartphones
by Freek van Polen
The session started out by using Google Now as an example of what passive sensing is, and finding out what people think about usage of sensor data in such a way. It quickly became apparent that people tend to be creeped out when Google Now suddenly appears to know where they live and where their work is, and especially dislike it when it starts giving them unsolicited advice. Following this discussion we arrived at a distinction between explicit and implicit sensing, where it is not so much about whether the user has to actively switch on sensing or enter information, but rather about whether the user is aware that sensing is going on.
From there the “uncanny valley” with respect to sensing on smartphones was discussed, as well as what would people be willing to allow an app to sense for. An idea for a BBC-app that would keep track of how much attention you pay to what you’re watching on television, and that would subsequently try to get you more engaged, was met with a lot of frowning. It was furthermore pointed out that passive sensing might be risky in the vicinity of children, as they are easily impressionable, are not capable of assessing whether it is desirable to have passive sensing going, and can be tricked into giving up a lot of information.
Stefan Hoevenaar’s father had Type 1 Diabetes. As a chemist, he was already quite meticulous about using data and those habits informed how he tracked and made sense of his blood sugar and insulin data. In this talk, presented at the 2014 Quantified Self Europe Conference, Stefan describes how his father kept notes and hand-drawn graphs in order to understand himself and his disease.