ArticleBMC research notes2021
Camera-based objective measures of Parkinson's disease gait features.
Article in BMC research notes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Technology-Enhanced Health Care in Smart Homes: Scoping Review of Sensor Technologies, Clinical Applications, Integration Challenges, and Future Directions.JMIR medical informatics · 2026Article
- A Riemannian multimodal representation to classify parkinsonism-related patterns from noninvasive observations of gait and eye movements.Biomedical engineering letters · 2025Article
- Interpretable video-based tracking and quantification of parkinsonism clinical motor states.NPJ Parkinson's disease · 2024Article
- A multimodal dataset of real world mobility activities in Parkinson's disease.Scientific data · 2023Article
- Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras.Sensors (Basel, Switzerland) · 2022Article
- Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveParkinson's disease is a common, age-related, neurodegenerative disease, affecting gait and other motor functions. Technological developments in consumer imaging are starting to provide high-quality, affordable tools for home-based diagnosis and monitoring. This pilot study aims to investigate whether a consumer depth camera can capture changes in gait features of Parkinson's patients. The dataset consisted of 19 patients (tested in both a practically defined OFF phase and ON phase) and 8 controls, who performed the "Timed-Up-and-Go" test multiple times while being recorded with the Microsoft Kinect V2 sensor. Camera-derived features were step length, average walking speed and mediolateral sway. Motor signs were assessed clinically using the Movement Disorder Society Unified Parkinson's Disease Rating Scale.
resultsWe found significant group differences between patients and controls for step length and average walking speed, showing the ability to detect Parkinson's features. However, there were no differences between the ON and OFF medication state, so further developments are needed to allow for detection of small intra-individual changes in symptom severity.
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Registered trials
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