{
  "id": 15818880,
  "title": "Measuring Parkinson's motor symptoms more objectively with 3D movement tracking",
  "url": "https://urgent.news/2026/10/20/measuring-parkinsons-motor-symptoms-more-objectively-with-3d-movement",
  "topic": "health",
  "section": "Health & Medicine",
  "published": "2026-10-20T23:20:16.000Z",
  "source": {
    "name": "Medical Xpress",
    "slug": "medical-xpress",
    "url": "https://medicalxpress.com/news/2026-10-parkinson-motor-symptoms-3d-movement.html"
  },
  "original_language": "en",
  "account": "Parkinson's disease can be more objectively measured using 3D movement tracking technology called TULIP. This system, developed by Duke University researchers, captures and analyzes a person's movements in three dimensions without requiring any sensors or special equipment. The machine learning algorithms reconstruct the movements as a 3D pose, providing precise quantitative measurements of the patient's movements, such as degrees of rotation or centimeters of movement.\n\nUnlike the traditional Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), which assigns a scoring system from 0 to 4, TULIP provides a more objective system for quantifying movement. This allows clinicians to more precisely track how the disease progresses over time, as subtle changes in movement can be captured. Kyungdo Kim, a Ph.D. student involved in the research, explains that instead of using a broad category like a gait score of 1, they can specify the exact changes in movement, such as a change in leg angle from 45 to 60 degrees, to indicate improvement and the potential effectiveness of medication.\n\nThe TULIP system was tested on participants performing three motor examinations commonly used to assess Parkinson's disease: hand movements, forearm rotation during walking, and whole-body gait. By combining measurements across these examinations, a more comprehensive picture of the disease's impact on movement was obtained. The system was able to automatically distinguish people with Parkinson's disease from those without it and estimate disease severity and duration based on changes in movement amplitude and speed.\n\nWhile TULIP is currently focused on Parkinson's disease, the researchers believe it could be useful for other movement disorders like Alzheimer's disease, stroke, and other neurological injuries. They envision TULIP as a complement to existing clinical assessments, providing more precise measurements to help physicians monitor changes in individual patients over time and offering clinical researchers a more sensitive tool for evaluating the effectiveness of treatments in clinical trials.\n\nThe team plans to continue refining the system by testing it on larger and more diverse patient populations to further establish its clinical potential.",
  "summary": "When clinicians evaluate someone with Parkinson's disease, they typically ask patients to perform a series of movements, like making a fist, rotating their forearms or walking across a room. They'll then assign a score from 0, indicating normal movement, to 4, indicating severe impairment.",
  "key_points": [
    "TULIP system measures Parkinson's movements objectively using 3D tracking without sensors.",
    "Machine learning reconstructs movements as precise 3D poses for quantitative analysis.",
    "System distinguishes Parkinson's patients, estimates disease severity and duration."
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}