Algorithm Challenge

What is it

The Algorithm Challenge is a competition to advance methods that reconstruct motor unit spike trains from high-density surface EMG (HDsEMG). It consists of two independent tasks and two sequential phases.

Tasks

Competitors may participate in one or both tasks:

Each task yields an independent leaderboard.

Phases

Familiarization Phase

Running in parallel with the Data Challenge, algorithm developers are provided with training data from the MUniverse benchmark collection (including labels), allowing teams to build, test, and optimize their methods for both tasks.

Showdown Phase

The main algorithm competition, conducted using the MUnitQuest data collection established during the Data Challenge (labels hidden from competitors). Note that the organizers may make small alterations to the test data (e.g., injected noise, cropping signals) to limit the advantage of teams also participating in the Data Challenge.

See the Submission and Registration page for details on how to submit predictions.

Leaderboard scoring

The scoring system for both tasks will be announced soon.

Awards

All teams receive recognition on a permanent leaderboard (per task). The top 5 teams per task (Isometric and Dynamic) will be invited to share their solutions in a special issue of the Journal of Electromyography and Kinesiology.