{
  "annotation_id": "4d6408b1-cd88-46fd-a24d-90d976225ba7",
  "date_created": "2026-02-17T05:53:05.097000Z",
  "date_modified": "2026-02-17T05:53:05.097000Z",
  "file_hash": "3708c1fce8c529348c5c2301851484cb80bf4c59585e979c8c472eb80ee46206",
  "private": false,
  "record": {
    "abstract": "To allow the development and assessment of physical rehabilitation by an intelligent tutoring system, we propose a medical dataset of clinical patients carrying out low back-pain rehabilitation exercises and benchmark on state of the art human movement analysis algorithms. This dataset is valuable because it includes rehabilitation motions in a clinical setting with patients in their rehabilitation program. This paper introduces the Keraal dataset, a clinically collected dataset to enable intelligent tutoring systems (ITS) for rehabilitation. It addresses four challenges in exercise monitoring: motion assessment, error recognition, spatial localization, temporal localization",
    "arxiv_id": "2601.06138",
    "authors": [
      "Sao Mai Nguyen"
    ],
    "categories": [
      "cs.CV",
      "cs.HC",
      "cs.RO"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Low-Back Pain Physical Rehabilitation by Movement Analysis in Clinical Trial",
    "url": "https://arxiv.org/abs/2601.06138",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "3f0354a5-ecce-4a65-b485-194eee7bc719",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}