“It’s Strava for wheelies,” my lockdown project, combining hyper-local exercise with data analytics to track and guide improvement. Practising wheelies is a great way to stay positive; after all, it’s looking up, moving forward.
I’ve been at it for a month now, logged 1,331 attempts, and seen my maximum time improve from two to nearly six seconds. In the process, I’ve generated millions of data points to guide further improvement through machine learning.
It doesn’t get much slicker than this. The hardware comprises:
Using a generous amount of…
In Melbourne’s COVID-19 lockdown, I’ve wheelied over 17km. Not all at once, though.
Over three months, I’ve spent 90 minutes with my front wheel raised. I’d like to keep it up, but as lockdown has gradually relaxed, and routines have changed, so have I landed the wheelie project, for now.
I’m pretty happy with my best 7.5s effort against my initial 8s target, and as such, the focus of this instalment is on quality over quantity.
In this post, we’ll extend the capabilities of the AI coach from Part 2. We’ll explore ML models to gain deeper insight into…
I now have an AI coach for my wheelie project. Coach has seen over 1,500 of my wheelies, and reckons they can tell pretty quickly whether my effort will be wheelie good or bad. Coach also fits on my phone, so they come on rides when I want real-time advice.
I have also recorded a wheelie at 7.9 seconds! (against target 8s). But wait — controversy! On reviewing the wheelie trace I realised that this was an effort where I had put my foot down before the front wheel “landed”. I estimate my foot went down at 6.6s —…
Works & plays with data