The 3rd WEAR Dataset Challenge is a Human Activity Recognition prediction challenge based on the WEAR dataset. The challenge will be part of the HASCA Workshop (last year's website) at UbiComp/ ISWC 2026.
With previous iterations of the WEAR challenge focusing solely on inertial data, this year we want to challenge the wearable community to explore how to most effectively combine egocentric cameras with inertial sensors.
To do so, this year, we provide random 1-second sliding windows from a single inertial sensor, as well as pre-extracted, frame-wise features (VideoMAEv2) from the egocentric camera's video stream.
More details can be found on the Kaggle Challenge website.
The WEAR Challenge will again be hosted via Kaggle. You can find the challenge here. Submission of prediction results will only be done via Kaggle. By using Kaggle's public and private leaderboard we aim to provide a more transparent and fair ranking process. Challenge participants will further be able to discuss the challenge and share their solutions on the Kaggle platform.
We will announce winners of the challenge at the HASCA workshop. In order to be eligible for prizes and be part of the final ranking announced at the conference, participants need to submit and present their technical report at the HASCA workshop.
Please note that prize amounts will be awarded either via bank transfer or an equivalent gift card/voucher, depending on logistical constraints.
The WEAR dataset is an outdoor sports dataset for inertial- and video-based human activity recognition (HAR). The dataset comprises data from 22 participants performing a total of 18 different workout activities with untrimmed inertial (acceleration) and egocentric video data recorded at 11 different outside locations. WEAR provides a challenging prediction scenario marked by purposely introduced activity variations as well as an overall small information overlap across modalities. This challenge focuses on the inertial data only.
Please visit the Kaggle challenge page to download the training and test data. The training data is provided as a directory containing the CSV files of the 22 participants. The test data is provided as a single CSV file with each row representing one of the sliding windows of the 4 new participants, which are to be predicted. For more details please refer to the Data section on Kaggle.
Evaluation of submissions will be based on the macro F1-score. The macro F1-score is the average of the F1-scores of each activity class.
Submission of the predictions will happen solely via Kaggle. All details on the submission format and final submissions can be found on Kaggle.
The final ranking of the WEAR Dataset challenge will be announced at the HASCA workhop. Ranking of the teams, which will be eligible for prizes, will be determined based on the private leaderboard score as of the technical report submission deadline.
When submitting their technical report, participants will need to:
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