Observational studyIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2018
Automated Assessment of Movement Impairment in Huntington's Disease.
Observational study in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review.Orphanet journal of rare diseases · 2023Pooled it
- Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review.Journal of medical Internet research · 2022Pooled it
- The use of wearable/portable digital sensors in Huntington's disease: A systematic review.Parkinsonism & related disorders · 2021Pooled it
- Longitudinal assessment of chorea in Huntington's disease using digital passive monitoring.NPJ digital medicine · 2026Article
- Using wearable sensors and machine learning to assess upper limb function in Huntington's disease.Communications medicine · 2025Article
- Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review.Journal of clinical medicine · 2024Review
- Using Wearable Sensors and Machine Learning to Assess Upper Limb Function in Huntington's Disease.Research square · 2024Article
- Measurement of bradykinesia and chorea in Huntington's Disease using ambulatory monitoring.Clinical parkinsonism & related disorders · 2023Article
- Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers.Sensors (Basel, Switzerland) · 2022Article
- IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.Healthcare (Basel, Switzerland) · 2022Review
- Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2022Article
- Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review.Frontiers in robotics and AI · 2022Article
- Predicting Severity of Huntington's Disease With Wearable Sensors.Frontiers in digital health · 2022Article
- Evaluation of movement and brain activity.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2021Review
- Objectively characterizing Huntington's disease using a novel upper limb dexterity test.Journal of neurology · 2021Article
- Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Quantitative assessment of movement impairment in Huntington's disease (HD) is essential to monitoring of disease progression. This paper aimed to develop and validate a novel low cost, objective automated system for the evaluation of upper limb movement impairment in HD in order to eliminate the inconsistency of the assessor and offer a more sensitive, continuous assessment scale. Patients with genetically confirmed HD and healthy controls were recruited to this observational study. Demographic data, including age (years), gender, and unified HD rating scale total motor score (UHDRS-TMS), were recorded. For the purposes of this paper, a modified upper limb motor impairment score (mULMS) was generated from the UHDRS-TMS. All participants completed a brief, standardized clinical assessment of upper limb dexterity while wearing a tri-axial accelerometer on each wrist and on the sternum. The captured acceleration data were used to develop an automatic classification system for discriminating between healthy and HD participants and to automatically generate a continuous movement impairment score (MIS) that reflected the degree of the movement impairment. Data from 48 healthy and 44 HD participants was used to validate the developed system, which achieved 98.78% accuracy in discriminating between healthy and HD participants. The Pearson correlation coefficient between the automatic MIS and the clinician rated mULMS was 0.77 with a p-value < 0.01. The approach presented in this paper demonstrates the possibility of an automated objective, consistent, and sensitive assessment of the HD movement impairment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.