Student Hack is fast growing “student only” Hackathon based in Manchester. The hack is in its fifth year and continues to grow & grow in popularity with students coming from around Europe to enter.
The Student Hack V event was last week and Cake were proud to be a Gold sponsor, we rocked up with our Mini-Event and the Challenge and were amazed at the results of the weekend, here is our summary of the weekend.
GoPiGo mini event, well this little robot turned out to be heavily affected by the environment, the naughty little tyke. Carpets affected top speed, distance travelled & overall grip on the surface, however it was still a success and a nice distraction from the main Hack. Great work & superb use of the replacement rule to ensure time penalties were kept to a minimum. Well done to Cameron for winning the challenge!
One of the sensors in the Muvr project is the magnificientPebble smartwatch. We use it to record the accelerometer values, pack them into a naïvely efficient data structure, and send them over the bluetooth connection to the mobile. The mobile then performs further processing, but that's for another blog post. In this post, I will show how we structured the recording and sending functions, and how we tested them.
As you probably know by now, Muvr performs near real-time exercise classification. It does so by fusing data from multiple (wearable) sensors, then sends the raw data to the server, in a simple binary encoding. The server decodes the data, reconstructs the sensor's data and locations, and feeds column slices to to the exercise model.
In this post, we monitor real-time streams of event data looking for pattern matches. Monitoring is performed using a novel and expressive query language based on linear dynamic logic (a generalisation of linear time temporal logic), with modern SMT provers (e.g. CVC4 and Z3) defining the pattern matching workhorse.
Lift uses Akka streaming workflows to define a flexible and generic exercise classification pipeline. The classification pipeline is able to modularly include any machine learning classifier and is able to monitor the real-time streams of classification results using a linear dynamic logic.
This post provides a summary overview of this classification pipeline with future posts introducing the implementation details.