Emteq Activity recognition challenge


Emteq aims to improve lives by providing people with actionable feedback about their behaviours. To do this, we need to be able to improve our methods for activity detection as part of our disease severity detection methods for Parkinson’s disease and depression monitoring. You can read more about our company here. Previous activity recognition challenges have focused on the use of smartphones or body-worn sensors.  The aim of our Machine Learning Challenge is to predict activities of daily life from inertial measurements as would be derived from a head-mounted device such as AR glasses.

The goal is to recognise the user’s activities from inertial data. Daily activities include: walking, watching a movie, sitting at a desk, using a computer, using a smartphone, sitting on a sofa.



For this dataset four volunteers performed activities of daily living over 3 hours.

The dataset comprises 3 hours of labelled training data from 3 volunteers, and 3 hours of unlabelled test data from a different volunteer. The dataset was based on volunteers recorded in a simulated home environment with camera-based annotation of the volunteer activities. Datasets will be available online as below.

To be eligible for the cash prize, participants must be registered to UbiComp 2019 / or ISWC 2019. Emteq are offering a further prize of a pair of Emteq OCOsense activity recognition glasses (value £1500) which is available for both registered and non-registered entrants.

Submission of predictions on the test dataset

Competition entrants (the participants) will develop an algorithm pipeline that will process the sensor data, create models and output the recognised activities. The F1-scores will be used as the metric to evaluate the winner.

Results should be submitted in a CSV file containing two columns: (1) timestamp and (2) activity. Each prediction (row in CSV file) should contain a timestamp and the activity predicted.Participants should also include a detailed description of the proposed classification system, (limited to 6 to 8 pages) in IEEE format.

The participants’ predictions should be submitted online by sending an email to claire.baert@emteq.net, in which there should be a link to the predictions file, using services such as Dropbox, Google Drive, etc.

 To be eligible for the prize participants will be need to share their code to enable formal evaluation by the prize committee.




  • 1st Prize £2000

  • 2nd Prize £1500 value OCOsense smart glasses

  • 3rd Oculus GO VR headset


  • Registration via email by 16th August 2019

  • Challenge duration: 16th July – 30th August 2019

  • Submission deadline: 30th August 2019



Each team should register as soon as possible but not later than 30th August 2019, stating the:

  • The name of the participant

  • The pseudonym of the participant

  • The name of the team, if applicable

  • The organisation/company, if applicable

  • The email address of contact person

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