**We are happy to announce that the WEAR Challenge is now available on Kaggle!** Unlike the first iteration of the challenge, submission of prediction results will only be done via Kaggle. By switching to Kaggle we hope to make the challenge more accessible to a wider audience and provide a more streamlined submission process. By using Kaggle's public and private leaderboard we hope 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. ## IMPORTANT DISCLAIMER **To be part of the final ranking and eligible for prices, participants will be required to submit a technical report to the HASCA workshop. This includes at least one author being paying full submission fee for the main conference and HASCA workshop! The paper should contain technical description of the processing pipeline, the algorithms and the results achieved using the original WEAR dataset. An example of a technical report can be found here [winners of last year's challenge](https://doi.org/10.1145/3675094.3678453). Submissions must follow the HASCA format (see below for more details).** ## Other Important dates * **HASCA-WEAR paper submission**: July 04, 2025 AoE * **HASCA-WEAR review notification**: July 14, 2025 * **HASCA-WEAR camera ready submission**: July 30, 2025 * **HASCA Workshop**: October 12, 2025 in Espoo, Finland ## Final Ranking at Conference & Prizes 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 make sure they selected their final submission of choice on Kaggle (see "Submissions" tab on Kaggle). Prizes will be awarded to the three teams with the highest private leaderboard score **at the time of the paper submission deadline**. - 🥇 **1st place:** €300 - 🥈 **2nd place:** €150 - 🥉 **3rd place:** €75 ## Submission Guidelines ### Final Predictions Submission of the predictions will happen solely via Kaggle. All details on the submission format and final submissions can be found on here 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: - include their team name as specified on Kaggle in the technical report, and - make sure they selected the correct final submission on Kaggle to be used for ranking (see "Submissions" tab). **NOTE:** Kaggle will automatically pick your best scoring public leaderboard solution, if you do not select anything in the "Submissions" tab. Prizes will be awarded to the three teams with the highest private leaderboard score at the time of the paper submission deadline (July 4th, AoE). ### Technical Report Submission of the technical report will happen via [PCS](https://new.precisionconference.com/submissions) *(select SIGCHI / UbiComp 2025 / UbiComp 2025 Workshop - HASCA-WEAR)*. Your technical report should detail your solution as well as provide preliminary results you achieved on the train dataset. ACM requires UbiComp/ISWC 2025 workshop submissions to use the double-column template. Your technical report must be between 3 to 6 pages including references. Submissions do not need to be anonymous. For details on the template please refer to the [UbiComp/ISWC website](https://www.ubicomp.org/ubicomp-iswc-2025/authors/formatting/). For an example of a technical report please refer to the [winners of last year's challenge](https://doi.org/10.1145/3675094.3678453). ## About the WEAR Dataset 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.