{
  "abstract": "Work-related musculoskeletal disorders are highly prevalent in physically demanding occupations, yet the publicly available kinematic datasets that researchers use to study them are dominated by laboratory mock-ups and recruited volunteers, with very few corpora capturing experienced workers during actual production. Hospital logistic operations, in particular, remain almost entirely undocumented despite imposing physical demands comparable to those of warehouse and food-industry work. We present an in-the-field full-body kinematic dataset recorded at the logistic centres of the Centre Hospitalier Régional Universitaire (CHRU) of Nancy, France. Six permanent employees, with up to 32 years of professional experience, were recorded with a full-body inertial motion capture system while performing their daily work at different workstations across the Industrial Laundry and the Industrial Kitchen Units. For each recording, we release the raw and exported files, associated sub-task labels, anthropometric and demographic information, and, when available, synchronised load data from the workstation scale. Technical validation was performed on inertial measurement unit data. The dataset is intended as a benchmark for in-the-field activity recognition and ergonomic risk estimation, and as a reference for the design of assistive technologies for hospital support services.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "Anna Bucchieri"
    },
    {
      "name": "Noemie Forest"
    },
    {
      "name": "Pauline Maurice"
    },
    {
      "name": "Khalid El Madiani"
    },
    {
      "name": "Steve Jamis"
    },
    {
      "name": "Loic Becker"
    },
    {
      "name": "Thomas Thockler"
    },
    {
      "name": "Nicla Settembre"
    },
    {
      "name": "Serena Ivaldi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7cqmck1zg33d2.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "7cqmck1zg33d2",
  "issns": [],
  "landing_url": "https://hal.science/hal-05785599v1",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.phys"
    ],
    [
      "hal",
      "1.phys.meca"
    ],
    [
      "hal",
      "2.phys.meca.biom"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.science/hal-05785599/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7cqmck1zg33d2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:50Z",
      "native_id": "hal-05785599",
      "source": "hal",
      "url": "https://hal.science/hal-05785599v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "hal:0.phys",
      "subfield": "general"
    }
  ],
  "title": "Full-Body Motion Capture Dataset of Hospital Laundry and Kitchen Logistics Operations in Real-World Environments",
  "title_text": "Full-Body Motion Capture Dataset of Hospital Laundry and Kitchen Logistics Operations in Real-World Environments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:50Z",
  "url": "https://research.draconitic.com/paper/7cqmck1zg33d2",
  "venue": "HAL",
  "versions": []
}