A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar.

Parma Hadi Rantelinggi, Xintong Shi, Mondher Bouazizi, Tomoaki Ohtsuki

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Parma Hadi RantelinggiGraduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan.ORCID 0000-0003-0532-4082

Xintong ShiGraduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan.ORCID 0000-0001-5094-3005

Mondher BouaziziFaculty of Science and Technology, Keio University, Yokohama 223-8522, Japan.ORCID 0000-0001-7055-9318

Tomoaki OhtsukiFaculty of Science and Technology, Keio University, Yokohama 223-8522, Japan.ORCID 0000-0003-3961-1426

Funding

Japan Science and Technology Agency (JST) ASPIRE JPMJAP2326, JapanLembaga Pengelola Dana Pendidikan SKPB-417/LPDP/LPDP.3/2023

6 · The paper itself

Abstract

Human skeleton estimation using Frequency-Modulated Continuous Wave (FMCW) radar is a promising approach for privacy-preserving motion analysis. However, the existing methods struggle with sparse radar point cloud data, leading to inaccuracies in joint localization. To address this challenge, we propose a novel deep learning framework integrating convolutional neural networks (CNNs), multi-head transformers, and Bi-LSTM networks to enhance spatiotemporal feature representations. Our approach introduces a frame concatenation strategy that improves data quality before processing through the neural network pipeline. Experimental evaluations on the MARS dataset demonstrate that our model outperforms conventional methods by significantly reducing estimation errors, achieving a mean absolute error (MAE) of 1.77 cm and a root mean squared error (RMSE) of 2.92 cm while maintaining computational efficiency.

Indexed as

Deep LearningRadarSkeletonAlgorithmsHumansNeural Networks, ComputerFMCW radarhuman motion analysismulti-head attentionpoint cloudskeleton detection

Identifiers

PMID40648167

DOI10.3390/s25133909

PMCPMC12252182

What Socratic holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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 Socratic graph.