@inproceedings{xu-etal-2024-ww,
    title = "{WW}-{CSL}: A New Dataset for Word-Based Wearable {C}hinese {S}ign {L}anguage Detection",
    author = "Xu, Fan  and
      Liu, Kai  and
      Yang, Yifeng  and
      Yan, Keyu",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.lrec-main.1541/",
    pages = "17718--17724",
    abstract = "Sign language is an effective non-verbal communication mode for the hearing-impaired people. Since the video-based sign language detection models have high requirements for enough lighting and clear background, current wearing glove-based sign language models are robust for poor light and occlusion situations. In this paper, we annotate a new dataset of Word-based Wearable Chinese Sign Languag (WW-CSL) gestures. Specifically, we propose a three-form (e.g., sequential sensor data, gesture video, and gesture text) scheme to represent dynamic CSL gestures. Guided by the scheme, a total of 3,000 samples were collected, corresponding to 100 word-based CSL gestures. Furthermore, we present a transformer-based baseline model to fuse 2 inertial measurement unites (IMUs) and 10 flex sensors for the wearable CSL detection. In order to integrate the advantage of video-based and wearable glove-based CSL gestures, we also propose a transformer-based Multi-Modal CSL Detection (MM-CSLD) framework which adeptly integrates the local sequential sensor data derived from wearable-based CSL gestures with the global, fine-grained skeleton representations captured from video-based CSL gestures simultaneously."
}