ArticleScientific data2026
A Spatial Millimetre-Wave Radar Point Cloud Dataset for Health Applications.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A Spatial Millimetre-Wave Radar Point Cloud Dataset for Health Applications.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
This work presents a controlled millimetre-wave radar point-cloud dataset for analysing guided daily and rehabilitation-related movements in standardised indoor settings. The dataset is designed to support radar-based human motion analysis: radar point clouds serve as the primary sensing modality, while the Azure Kinect provides 3D skeleton labels for supervised learning and validation. Data were collected from 26 healthy, able-bodied adults aged 25-57 years. Participants followed reference videos to perform a set of daily and rehabilitation-related movements under two visual conditions: normal lighting and low-light settings. After quality control, the release contains approximately 3.47 minutes of retained movement data per participant per lighting condition. The original recording-level data include 265,487 radar frames and 322,833 Kinect-derived skeleton-label frames, while the processed aligned data contain 137,717 paired radar-skeleton frames. Each aligned radar frame is annotated with an action label, subject ID, and corresponding Kinect-derived 3D skeleton. By combining radar point clouds with aligned skeleton labels across controlled lighting conditions, the dataset supports tasks such as radar-based action classification, identity recognition, and keypoint detection.
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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.