{
  "abstract": "Objective quantification of motor abnormality in Parkinsons disease (PD) remains challenging because conventional clinical assessments are episodic, observer-dependent, and based on coarse ordinal ratings. Wrist-worn wearable sensors provide a scalable opportunity to capture task-specific motor patterns; however, most existing approaches focus on discrete classification rather than deriving continuous latent markers of motor abnormality. In this paper, a multi-stream deep representation network is introduced to derive a latent motor abnormality score using wrist-worn inertial sensor signals. The proposed network includes a local feature extractor based on one-dimensional convolutional layers, a global feature extractor based on Transformer layers, and an embedding layer that maps each task-specific signal into a 64-dimensional embedding vector. A new training procedure is proposed based on a combination of supervised contrastive learning and center loss to cluster the embeddings of PD patients and healthy control (HC) subjects, and to build a latent motor abnormality score based on the average embedding distance from the centroids of PD and HC training embeddings. Across five-fold subject-level cross-validation, the proposed method was assessed for PD/HC classification and achieved an accuracy of 83.10% {+/-} 3.15%, balanced accuracy of 85.89% {+/-} 3.93%, precision of 97.02% {+/-} 2.33%, and ROC-AUC of 0.917. The motor abnormality score showed clear group separation, with healthy controls generally obtaining negative scores and PD subjects obtaining positive scores. Statistical analysis confirmed a significant difference between groups (F = 222.99, p < 0.001), with a substantial effect size ({superscript 2} = 0.3871). Moreover, exploratory analyses showed that the score tended to increase with higher non-motor symptom burden and longer disease duration, suggesting that the learned latent representation captured disease-related motor abnormalities.",
  "abstract_shown": true,
  "affiliations": [
    "Iran University of Science and Technology"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. M. Mohtavipour"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/90dxtern4fwce.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.03.26362173",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.03.26362173v1.full",
  "id": "90dxtern4fwce",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.03.26362173v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "neurology"
    ],
    [
      "openalex",
      "1707|Context-Aware Activity Recognition Systems"
    ]
  ],
  "openalex": "W7211867160",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.03.26362173v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/90dxtern4fwce.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:53Z",
      "native_id": "10.64898/2026.09.03.26362173",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.03.26362173v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:neurology",
      "subfield": "neurology"
    }
  ],
  "title": "Deep Representation Learning of Wrist-Worn Sensor Signals for Latent Motor Abnormality Scoring in Parkinson's Disease",
  "title_text": "Deep Representation Learning of Wrist-Worn Sensor Signals for Latent Motor Abnormality Scoring in Parkinson's Disease",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:53Z",
  "url": "https://research.draconitic.com/paper/90dxtern4fwce",
  "venue": "medRxiv",
  "versions": []
}