Or why cool projects often fail to get traction
Today I tried to install a project I have been working for a while on a new machine. It relies heavily on storing and quering data in “social network” manner, and hence not necessarily very well adapted to the relational databases. When I was staring to work on it back in the early 2013, I was still a fairly inexperienced programmer, so I decided to go with a new technology to underlie it neo4j graph database. And since I was coding in Python and fairly familiar with the excellent SQLAlchemy ORM and was looking for something similar to work with graph databases my choice fell on the bulbflow framework by James Thronotn. I complemented it with JPype native binding to python for quick insertion of the data. After the first couple of months of developer’s bliss and everything working as expected and being build as fast as humanely possible, I realized that things were not going to be as fun as I initially expected.
- Python 3 was not compatible with JPype library that I was accessing to rapidly insert data into neo4j from Python. In addition to that JPype was quickly dropping out of support and was in general too hard to set up, so I had to drop it down.
- Bulbflow framework in reality relied on the Gremlin/Groovy Tinkerpop stack implementation in the neo4j database, was working over a REST interface and had no support for batching. Despite several promises of implementation of batching by it’s creator and maintainer, it never came to life and I found myself involved in a re-implementation that would follow that principles. Unfortunately I had not enough experience with programming to develop a library back then, nor enough time to do it. I had instead to settle for a slow insertion cycle (that was more than compensated for by the gain of time on retrieval)
- A year later, neo4j released the 2.0 version and dropped the Gremlin/Groovy stack I relied on to run my code. They had however the generosity of leaving the historical 1.9 maintenance branch going, so provided that I had already poured along the lines of three month full-time into configuration and debugging of my code to work with that architecture, I decided to stick with 1.9 and maintain them
- Yesterday (two and a half years after start of development, when I had the time to pour the equivalent of six more month of full-time into the development of that project), I realized that the only version of 1.9 neo4j still available for download to common of mortals that will not know how to use maven to assemble the project from GitHub repository is crashing with a “Perm Gen: java heap out of memory” exception. Realistically, provided that I am one of the few people still using 1.9.9 community edition branch and one of the even fewer people likely to run into this problem, I don’t expect developers will dig through all the details to find the place where the error is occurring and correct it. So at that point, my best bet is to put onto GitHub a 1.9.6 neo4j and link to it from my project, hoping that neo4j developers will show enough understanding to not pull it down
All in all, the experience isn’t that terrible, but one thing is for sure. Next time I will be building a project I would see myself maintain in a year’s time and installing on several machines, I will think twice before using a relatively “new” technology, even if it is promising and offers x10 performance gain. Simply because I won’t know how it will be breaking and changing in the upcoming five years and what kind of efforts it will require for me to maintain the dependencies of my project.
Fitness trackers and other wearable techs are gaining more and more momentum, but because of the ostrich cognitive bias they are absolutely not reaching the populations that would benefit most from them. And as per usual, ReadWriteWeb is pretty good at pointing this out in a simple language.
To sum up, current fitness tracking has several short-comings for the population it would target:
- It is pretty expensive. Fitness band that does just the step tracking can cost somewhere between $50 and $150. If you are trying to go something more comprehensive, such as one of the Garmin’s multisport watches, you are looking for somewhere in the $300-$500. Hardly an impulsive purchase for someone who is getting under 30k a year and have kids to feed from that. However they are the group at highest risk from obesity and cardiovascular disease.
- They generate a LOT of data that is hard to interpret, unless you have some background as a trained athlete. Knowing your Vmax and hear-rate descent profile following an error is pretty cool and useful for monitoring your health and fitness, but you will never know how to do it, unless someone explains it to you or you already knew it from your previous athletic career.
- They do not provide any pull-in. As anyone with a bank account would know, saving comes from the repeated effort in duration. Same as with health capital. However, as anyone with a bank account knows, when you hit hard financial times, you watch your bank account much less than during the times where everything is going well. Just because it is rewarding in the latter case and painful in the first. Same thing with health: people who lack health but are ready to do it are self-conscious about it and need an additional driving motivation to make them last through the periods where no progress is happening
- It does not respond to an immediate worry and is one of those products that are “good to have”, but whose absence does not lead to a “I need it RIGHT NOW” feeling
With that in mind, I decided to participate in MedHacks 1.0 last weekend. My goal was to develop something that would provide an emergency warning for users that are either at high risk of stroke or undergoing it, so they would not get themselves isolated while having a stroke. With my team, we managed to hack together a proof of concept prototype in about 24 hours, which took us into finals. In order to do this, we used an audio mixing board to amplify the signal, Audacity to acquire the data on a computer, FFT and pattern matching to retrieve the data and filter out loss-of-contact issues and build an app in Android that was able to send out a message/call for help if the pattern changed.
Now, those are very simple components that could be compressed on a single sensor woven into a T-shirt and beamed onto a phone for analysis in background. We would need to do some machine learning to be able to detect most common anomalies and then validation by human experts of the acquired EKG.
However, the combination of persistently monitoring cheap device and an app that is able to exploit it opens large possibilities for fitness tracking for those most needing it.
- The reason to purchase and use the monitoring device is not fitness anymore. It is basic safety. And can be offered by someone who is worried for your health.
- The basic functionality is really clear. Something is going on wrong with you, we will warn you. Something is going really wrong, we will warn someone who can check on your or come to your rescue.
- We can build upon the basic functionality, introducing our users to the dynamics of fitness in a similar way games introduce competitive challenges: gradually and leaving you the time to learn at your pace.
- We have a very precise access to the amount of effort. Your heart rhythm will follow if you are doing a sternous directed activity and we will guide you in it
- We were able to build a prototype with very common materials. Compression and mass-production will allow us to hit the lowest market range, at a price where you are paying for a smart athletic piece of clothing only marginally more than for the same “non-smart” piece of clothing.
Sounds interesting? I am looking for someone with clinical experience in hear diseases, a hardware hacker that would have experience with wearable and someone to drive the consumer prospection and sales.