...Do you even? Exercise in exercise analysis
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ios iOS notifications Feb 19, 2015
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README.md

README.md

For most up-to-date version, head over to https://github.com/muvr/open-muvr.

lift

A demonstration of an application that uses wearable devices (Pebble)—in combination with a mobile app—to submit physical (i.e. accelerometer, compass) and biological (i.e. heart rate) information to a CQRS/ES cluster to be analysed.

The result is automatically constructed exercise log, which includes:

  • the kind of exercise (biceps curl, shoulder press, and other such tortures)
  • the intensity (light, moderate, hard)—nota bene that intensity != weight

It is also an excellent demonstration of large reactive application. It is event-driven: throughout the application, it uses message-passing to provide loose-coupling and asynchrony between components. It is elastic: the users and their exercises—the domain—is sharded across the cluster. It is resilient: its components can recover at the appropriate level, be it single actor, trees of actors or entire JVMs. It also uses event sourcing to ensure that even catastrophic failures and the inevitable bugs can be recovered from. It is responsive: it does not block, and it is capable of distributing the load across the cluster.

It combines near-real-time machine classification needed for immediate exercise feedback, with "offline" model upgrades. Once upgraded, the event-sourced nature of the system allows us to re-apply the new model to the old data, and thus provide the users with better data.