Apple Watch: It’s all in the Ecosystem

Unless you were under busy living under a rock, you probably saw the Apple announcements last week launching the iPhone 6 and the Apple watch. I’ve doing a lot of thinking lately about the intersection of Big Data and the internet of things and specifically how they apply to the Quantified Self movement. 

 

A little about me

 

Currently, I’ve got a lot of stuff going on in my life. Specifically going through a separation from my wife of ten years. One of the ways that I’m choosing to deal with this is to try and focus on the moment and the daily activities. To set small goals for myself and to track them using various methods to see if I’ve achieved at the end of the day or not.  Most days, I’m hitting the goal. Others I miss and I come back that much more determined the next day to get my life back on track. Three amazing kids are an awfully powerful motivation. 

 

My devices

 

I’ve been loosely tracking my stats for a few years now and I’ve had a bunch of different devices in that time more or less in chronological order from when I started using them. Here’s the non-inclusive list that I can remember off the top of my head, although I’m sure there’s a couple I’ve missed. 

 

Garmin 305 Forerunner ( Circa. 2008)

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This was one of my first entires into the QS movement. It was a good product. Stable. Built in GPS and connected to an ANT+ hear-rate monitor. I used this a lot at the time. I’m sure if I go back you can see my tracks over Europe, Canada, US, and even a few weeks in Vietnam.

Not a bad application to go with it, but the data was pretty much locked in. They did eventually kill the 32-bit App in favour of a web interface, but by the time that was released, I had moved on. 

 

Wii Fit (Circa, 2008 )

NewImageI first got into the wii fit with the original release. Lots of family fun. Getting my kids active is important to me and that’s not always easy. This was arguably the first gamification of fitness.  It worked. The kids loved it.

I’ve upgrade these to the latest Wii Fit U which is currently a favourite of my kids. The balance board is an awesomely accurate scale as far as tracking balance. The biggest problem I have is there’s no way to get all this awesome data out of the game. Locked completely in Nintendo’s hands. I can see the data over time, but there’s no way to pull it out and do any data mashups to see if anything interesting comes out of the combined data. 

 

Fitbit ( original ) (Circa. 2012)

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I got a fitbit when they were first released around 2012. I lost the first one while walking because of a bad design on the built clip, lost the second on e trip coming back from Barcelona, and I’m onto my 3rd iteration which I’m happy to say has a much better built clip and is currently hanging from my belt. One of the things I like the most about the fitbit is that they have allowed other vendors, like Runkeeper to access the data and use it in their own applications. I’ve tried a couple, but so far, I always end up coming back to the Fitbit apps, whether the iOS or Webapp, they are still the way I prefer to look at that data. 

 www.fitbit.com

 

 

 

 

 

Apple iPhone 5s

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With the build in motion sensor, the 5s some interesting capabilities. I’ve not taken advantage of them to be honest, but I’m aware the data is there, if only I locked. 

 

 

 

 

 

 

 

Pebble Smartwatch

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Pebble’s got the tech to be able to do a lot of what the fitbit does, but so far, I haven’t seen anyone taking advantage of it. There are the golf training apps which show the potential of the hardware platform, but I just haven’t had a chance to play with that yet. not a golfer 🙂 

 

http://www.getpebble.com




Wahoo Bluetooth Heartrate reader

Self Explanatory I think. The Wahoo sends heart-rate data to the iPhone. The apps, like run keeper, can then access the data as it tracks your pace, speed, GPS position etc… and help to provide you some “your heart rate goes this fast when you move this fast” style of observations. 

http://www.wahoofitness.com

Fit Aria Scale

This is an interesting Fitbit product that takes the pain out of tracking your weight. Sure, I could write it down and later manually input it into a system, but the Aria connects directly through my wi-fi network and auto uploads the results of each weigh-in into the fitbit online system.  I can also weigh myself in the morning, afternoon and evening and have all that data, complete with the timestamp of the measure to be able to look at fluctuations. 

http://www.fitbit.com

Muse personal EEG by InterAxio

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The muse is an interesting product that I wrote a bit about here.  Personal EEG reader. Good SDK, but all manual. You can do anything with the data you want as long as you can write the code yourself.  The app is decent and they just updated it, taking into account user feedback and improving what their customers told them was important. I’m starting to expect good things from this company. So far, I’m impressed with the product, the packaging, and especially the willingness to engage and listen to their customers and enhance the product based on customer feedback.

http://www.choosemuse.com

 

 

 

 

Sense

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 This is a kickstarter project which takes the QS movement and applies it to sleep. Sure, my fitbit can give me an idea of how I slept last night? Number of times awake, how restless, etc.. and quantify some measurement on how good my sleep was, but it doesn’t give me any insight into WHY my sleep was the way it was. That’s where Sense comes in. The sense has a sleep sensor that attaches to your pillow which looks like it will gather a lot of the same data as the fitbit. Where the sense differs greatly is the base station which also gathers audio and air quality data, and potentially other pieces of data such as level of light during the night and then attempts to correlate those different pieces of data with the quality of your sleep.  Did a car alarm go off at 3:17am?  Was there an abnormally high amount of dust or pollen in the air? Was it too dark/toolight?  how did all these factors affect the measured quality of your sleep.  Again, I don’t actually have the product, so the final features may differ, but the concept is there at a price which caused me to jump in. 

http://www.hello.is 

 

My Apps

 The other side of the hardware equation is the apps. I’ve used quite a few of them over the last few years. Here’s another non-inclusive list with some quick comments. Almost all of these devices have their own app ( fat or web ) and most of them also have an API that one could choose, if one had the inclination and ability, to mine for data. 

Garmin Forerunner App

Fat 32 bit app. Locked in data. It worked, but they stopped updating it and moved to a web. I think it might still be around, but I abandoned it long ago. 

Fitbit Website

This is my return-to interface for my health data currently. My aria scale and my Fitbit both publish nicely into this interface with no action on my part. The interface is clean and they do make an effort to make improvements to both the iOS apps as well as the web on a regular basis. 

Runkeeper

Runkeeper is something I use on and off. I tend to use Runkeeper when I am focused and going to the gym daily. When work gets too busy and I’m not able to make it to the gym, I stop using it and fall back on the fitbit apps to track my physical activity and make sure I get my 15,000 steps a day. 

Fitocracy

Interesting app. Abandoned it quickly. Social + workout may be good for some people but didn’t motivate me. 

Endomundo

Lots of people love it. I found it similar to run keeper and not enough difference to commit to trying something new. It’s a good app and I’m sure I would be there if I hadn’t tried runkkeeper first. I’m sure there are differences that would make a person choose one over the other. I just don’t know about those particular differences so I stuck with runkkeeper/fitibit combo.

Nike+

I had a Apple iPod Nano with the Nike+ pedometer built into it, but I stopped using it when I got my pebble. The historical data is in the Nike+ website and I have no idea how I might get it out.  

So what does the Apple Watch do for me?

I’ve got no special access to any data around the Apple Watch ( although I’d love to review it if someone sent me one! hint! hint! ) so I’m basing my comments on the Apple Watch launch last week. This device appears the be a nice combination of some of the devices I already own. At first glance, it could replace the Wahoo heart rate reader as well as potentially the fitbit, although what information the Apple Watch is tracking or the accuracy is still mostly unknown. Steps, sure, but what about the ability to track flights of stairs like the fitbit does? Or sleep patterns like the fitbit does? I would guess no noise pollution or air quality like the sense, but Apple has surprised us before. I suppose they could have hidden a smell sensor and they could definitely leverage the microphone in the iPhone or iPad to gather noise data. The Watch certainly looks capable, but considering what else it’s doing, I imagine the battery life may become a problem.

Data all over the place

“Data Data everywhere and not a drop to drink”

The main problem that I currently see with the QS movement, and my personal attempt to derive some data-inspired observations of my life is the fact that the data is all tied into a particular vendors data structures and repositories. Of the different tracking devices that I’ve used over the year, the most accessible of these has been, by far, the fitbit.  Fitbit has put out a pretty decent API and has allowed other vendors, like Runkeeper or MyFitnessPal to be able to draw out the fitbit data into their own webapp. There’s also a custom watch face for the Pebble smart watch which can also draw out the fitibit data through an android or iPhone and display how you’re doing on a given day at-a-glance right there on your wrist. You’re keenly aware of where you sit for your movement goals that day every time you look at your wrist. But fitbit does not allow access to all of the data they track through their API. There are some portions, like the sleep data, that they appear to be keeping to themselves, for either business or resourcing reasons. They seem to be a great company, so I’ll just assume that they are too busy building out great new features to extend the APIs for sleep data right now. 

 

The Garmin device? I had to abandon the data completely. The Wii is great, but a data blackhole. Anything that goes in does not come out. The Muse is new and extremely accessible, decent SDK, etc…

Long story short: All of these devices have left me with an extremely disjointed collection of data and data sources that are oozing with unconnected potential insights, if only I had the time and patience to sit down and create a framework to pull it all together. 

Ecosystem in the making?

Apple makes great products. Period. I own many of them and I’m deeply entrenched in, what I think is going to be the really value proposition of the Watch, the Apple Ecosystem. Apple has done a phenomenal job of connecting the various different iPods, iPhones, iPads, Apple TVs and OSX running machines all together through a common Ecosystem all accessed, primary, through the iTunes and AppStore interfaces. This brings together a common interface, a common user experience, and a common expectation throughout the entire range of Apple devices. They have done what I consider to be an amazingly good job of connecting those devices and the applications running on those devices.  What’s most amazing to me is that they actually extended this functionality to their developer ecosystem as well, allowing the ISV’s to be able to take advantage of those same connection points to provide a more seamless user experience.  And if reports are to be believed, this is only going to get tighter with the OSX Yosemite and iOS 8 releases.

I believe that Apple Watch and the Health sensors could possibly pave the way for a framework which would allow independent hardware and software vendors to plug into, very similar to what is done with the iPhone and iPad platforms today. Run an app?  Sure!  Have a custom peripheral that you want to use to send data to the device like the Muse? Absolutely! Come one come all! 

I expect to see Apple create a health framework to receive all the Apple Watch and currently available iPhone health sensor data. In the first iteration, it will most likely be Apple only and most likely limited in functionality. But as they iterate and extend, I think we’re going to see the Apple Health framework become the defacto standard to which health related IoT devices are going to send their data and to which ISV’s are going to look to as the primary access point for consuming this data. 

Granted, there are a bunch of potential privacy concerns that may get in the way of this, but Apple managed to get the record industry to bend beneath their will. I think that if there’s a company out there that could possibly tackle the issues and come out with something useful, Apple is more likely than most to rise to the challenge.

 

What the Future holds

With the democratization of all of this data, I’m extremely excited to the possibilities and insights into the nature of the human condition that can be derived from having such an abundance of data across such a huge proportion of the population. There’s a lot of work to be done to figure out how to categorize the contributors in useful demographics that allow us to start grouping and sifting the data for interesting correlations. 

Imagine if all of that data can be sanitized and drawn into a connected series of data sources which are all uniformly accessible through a common set of Apple HealthNet (I’m making the name up!) APIs which allow App developers to write to a common API and allows Hardware developers a common schema to which they can deploy their data. If they need something else allow them to extend it themselves or have them work with Apple to extend the schema where necessary so all devices can take advantage of it.

Even better, have the medical community give input into the schema as well allowing them to actively solicit different types of data from the collective apple-bearing masses. Crowd sourcing huge amounts of data.

There are only a couple of ways to improve accuracy in statistical analysis. Increase the number of samples in a given time period or increase the number of time periods across which you sample. Either way, more data leads to more accurate data. 

 

Are there privacy issues?  Sure there are. How do we allow medical researchers to be able to mine that data pool while protecting the individual right to privacy.  One of the ways to do that is for a single organization to take on the burden of such responsibility and allow other entities to then access through the structure, secure methods.  Kinda sounds like Apple might be in that position soon.

Am I crazy?  What do you think? Looking forward to comments below

 

@netmanchris

 

 

 

 

http://www.wahoofitness.com/

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Quantified Self Meets BIg Data: A Meeting of the Minds

As I’ve written about before, I’m diagnosed ADHD. I’m not one of those “squirrel!” joking guys who is “sure” they have ADHD but have never been tested. I’ve been on meds and done a ton of reading over the years to develop coping strategies to deal with the challenges that are presented by the different way that my brain works to try and mitigate the drawbacks and take full advantage of all the gifts that come with ADHD.

 
One of the the coping strategies that I’ve always been very interested in is that of bio-feedback. Imagine if you could actually “see” what you’re brain is doing. Imagine that you could actually “watch” your attention lapse in near real-time! How amazing would that be? Imagine the insights that could be derived and the potential to identify potential triggers in attention deficits. ( For the record, I’ve not struggled as much with an inability to focus, so much as an inability to SHIFT focus when i need to. )
 

Enter the year of the portable EEG.

 
2014 is the year of the portable EEG. In 2013, there were at least three different projects focusing on bringing brain science to the masses that I’m aware of. 
 
For the record, I”m not a brain scientist and any assessments that I make here are PURELY my own very limited ability to judge. 
 

Emotive Insight:


This seems to be the most technically advanced of the three projects. The kickstarter project has been slow to say the least. They’ve had a few set-backs over the course of the project. But they have been fairly consistent with feedback and the company seems to have more participation in the academic community. 

I can’t make any judgement call on the actual device, as they are behind on delivery.  ( April 2013 est delivery date ).  But I have high expectations on this one. 
 
The SDK will probably be quite mature as I’m pretty sure they will be leveraging tech from their earlier products. 
 
In the latest update, they mentioned a company called Neurospire who currently uses EEG data for marketing purposes (very cool concept!). Turns out they are changing their game a bit to something closer to my heart. They just won their first round of funding to develop a biofeedback application aimed directly at aiding children with ADHD.  I’m very excited to see what they come up with and see if they come up with something that can help my kids as they learn to deal with the pros and cons of their differences. 
 

Melon

 
Melon seems to be more of a fun project. The science and tech seem to be there, but the focus seems to be more on bringing the fun. They have made some adjustments to their original, based on kickstarted backers feedback, to allow the headband to adjust from kids to gargantuan cranium size. The application is also more focused on fun, or so I’ve been led to believe. The app measures your focus, and IF you can stay focused, it will allow you to fold origami animals.  Sounds kinda funny, but I can tell you my kids are actually excited about this one. 
 
Imagine… Folding. Paper. With. Your. Mind.    
 
Yeah. I know, right?
 
SDK is also an unknown at this point as it’s still listed as “available soon”.
 
Looking forward to this one which is also on the late shipping train. The est. ETA was November 2013, but according to the latest update, we should be seeing it in late September. 
 

Muse:  

 
I actually got turned on to this one by @beaker.  They went the indigogo.com way rather than kickstarter.com.  I didn’t end up getting in on the funding on this one, so no deal for me.  But…  they actually shipped. 
 
Yup. I put in an order and it arrived 2 days later at my door. InterAxon, the company who makes muse, is actually out of Toronto, so this is one of the RARE occasions that I’ve not had to wait or pay extra for shipping to Canada! ( woo-hoo! ). 
 
This product just started shipping, but they already have an SDK in place, as well as apps, titled Calm, for both iOS and Android.  Being an apple-guy, I tried it out and was actually pretty impressed. Clean interface, simple for now, but the concept works. In a nutshell, the weather gets calmer when you get calmer. 
 
The hardware seems solid, There’s one of the sensors that I have a little bit of trouble with, but I’m not sure if that’s just more practice or something actually wrong with the unit. Only time will tell I guess. 
 
The SDK seems not too bad either. I had some trouble getting the Muse to connect on OSX, but that’s MOST likely because I’m running a beta of a pre-release version of a certain fruity OS.   
 
The Windows and the OSX install were pretty similar to be honest. The SDK is python based and requires python 2.7 ( WHY NOT Python 3????) and a few typical libraries ( numby and Scipy from memory ). Pretty well documented on the choosemuse.com website. 
 

Big Data meeting of the minds.

 
One of the truly cool things which the quantified self movement brings is the sudden  influx of contributors to datasets.  The Calm application for the Muse allows the user to share their data in a non-identifiable way back to the InterAxon servers. There’s the obvious demographic questions that get asked as part of the initial registration, 
 
Imagine how Big Data algorithms can be applied once enough of us start to donate the output of our sessions along with enough demographic information to allow data scientist to create K-plots and run Baysian functions and start pulling some interesting observations. 
 
Imagine how baysian algorithms can suddenly pull out astonishing insights when you combine the EEG readings from the Insight with the activity level and sleep patterns from the fitbit, throw in a little dash of air quality and noise pollution from the sense.  Mix it up in “the cloud” and start comparing our sanitized non-personally identifiable with other peoples sanitized non-personally identifiable of similar demographics and we start to have enough data to start pushing the envelope of our understanding of our behaviours. 
 
The scariest thing for me is that we might actually be able to quantity what normal actually is. 🙂  
 
Ok… so maybe that last one is a bit of a stretch, but it’s certainly going to be interesting watching what happens in the next couple few years as this data starts to coalesce. Data gravity starts to kick in and we have suddenly have a large enough data set for things to get REALLY interesting.
 
Anyone else out there donating data? Scared? Paranoid?  Anyone else looking forward?