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REVIEW 4 major objections 5 minor 44 references

mmWave Radar for Sit-to-Stand Analysis: A Comparative Study with Wearables and Kinect

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A 60 GHz mmWave radar can count sit-to-stand repetitions and track trunk motion at the same level of agreement as Kinect and wearable sensors, but it does not capture knee motion reliably.

desk verdict Useful first point-cloud radar STS comparison, but the 'reliable trunk' claim outruns the independent evidence. read the letter →

arxiv 2411.14656 v2 pith:IZ7GDVQT submitted 2024-11-22 eess.SP cs.ETstat.AP

Shuting Hu , Peggy Ackun , Xiang Zhang , Siyang Cao , Jennifer Barton , Melvin G. Hector , Mindy J. Fain , Nima Toosizadeh This is my paper · ORCID
classification eess.SPcs.ETstat.AP
keywords sit-to-standmmWaveradarposeestimationinversekinematicsfallriskassessmentKinectwearablesensorsintraclasscorrelationcoefficient
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a 60 GHz millimeter-wave radar, which works from a contact-free point cloud, can serve as a useful alternative for sit-to-stand analysis, at least for the coarse features most relevant to fall risk assessment. It trains a deep-learning pose estimator on Kinect skeletons, converts the recovered 17-joint skeleton into sagittal-plane trunk and knee angles via inverse kinematics, and compares these with Kinect and five wearable gyroscope sensors on 45 healthy adults. The result, if correct, is that radar reliably counts STS repetitions and measures trunk range of motion and trunk peak velocities, with intraclass correlations roughly 0.72 to 0.96, while knee range of motion is not captured reliably. The study therefore argues that radar is a viable non-contact, privacy-preserving option for monitoring the gross movement component of fall risk, with full-body precision still awaiting better ground truth or sensor fusion.

What carries the argument

The load-bearing mechanism is the mmPose-FK pipeline: a 60-64 GHz MIMO radar produces range-Doppler-angle point clouds, a deep neural network learned from Kinect body tracking regresses a 17-joint skeleton, forward-kinematic constraints and a smoothing filter stabilize it, and inverse kinematics with Rodrigues rotations convert the skeleton into a time series of relative joint angles for 12 joints around three axes. From those angles the pipeline segments each STS repetition, extracts duration, trunk range of motion, trunk flexion and extension peak velocities, waist-thigh range, and knee range, and scores sensor agreement with intraclass correlation and Bland-Altman analysis. The Kinect skeleton is simultaneously the training label and the main comparison reference, which is why the knee-joint jitter weakens both the learned radar model and the Kinect-radar agreement.

What would settle it

Collect simultaneous radar, Kinect, and marker-based optical motion capture data for the same sit-to-stand protocol, and compare trunk and knee features. If radar-versus-optical trunk range-of-motion ICC falls far below the reported radar-Kinect value of 0.9295, or if the radar-versus-optical knee ICC stays near zero while the optical reference is stable, the paper's claim that radar is reliable for trunk assessment is not supported.

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Extended reading notes

Core claim

The central discovery claimed is that mmWave radar point clouds, after deep-learning pose estimation and inverse kinematics, produce STS features that agree strongly with Kinect and wearable sensors for movement duration and trunk-level motion, but not for knee-level motion. Reported ICCs are 0.9556 (Kinect-radar), 0.9634 (Kinect-wearable), and 0.9526 (radar-wearable) for STS duration; trunk range-of-motion ICCs are 0.9295, 0.7865, and 0.7218 for the three sensor pairs; knee range-of-motion ICCs are only 0.3064, 0.0697, and 0.0456. The authors attribute the knee failure largely to jitter in the Kinect skeleton's lower leg, which also serves as the training target for the radar model, and conclude that radar remains a reliable tool for trunk-focused fall risk assessment rather than for fine joint detail.

Load-bearing premise

The Kinect's built-in body tracking is treated as ground truth for training the radar pose model and as the reference for comparison, despite the paper reporting that the Kinect skeleton jitters in the lower leg; if that reference is biased, the radar-Kinect agreement does not establish true measurement accuracy.

Editorial extensions

If this is right

  • Automated STS repetition counting in a 30-second chair-stand test can be done by radar placed a few meters away, without wearable devices, with agreement effectively equal to Kinect and wearables.
  • Trunk range-of-motion and trunk peak-velocity features, the ones most tied to fall risk, can be extracted from radar with useful agreement for cohort-level assessment.
  • Knee range-of-motion from radar is not yet usable for clinical decisions, and sensor comparisons reporting knee-level agreement must treat Kinect's lower-leg jitter as a confound.
  • Because the radar learns from Kinect, radar accuracy is bounded by Kinect accuracy, making a marker-based optical motion capture reference the natural next validation step.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, a natural extension is to test whether radar trunk features can separate older adults at high fall risk from younger controls using the same protocol; the current healthy sample cannot answer that.
  • Because the radar and Kinect share a coordinate calibration and the radar learns from Kinect, the high Kinect-radar trunk ICC may partly reflect training fidelity rather than independent measurement accuracy; an independent reference would disentangle these.
  • The radar's limited angular resolution suggests the knee failure is plausibly a hardware limit rather than a fundamental one, so denser antenna arrays may recover lower-leg tracking.
  • A testable extension of the multi-sensor fusion argument is to combine radar trunk features with wearable shank gyroscope data, potentially recovering knee-level detail that radar alone misses.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript presents a comparative study of mmWave radar, Kinect, and wearable inertial sensors for Sit-to-Stand (STS) motion analysis. A 60 GHz FMCW radar collects point clouds; a deep learning pose estimation model (mmPose-FK) predicts a 17-joint skeleton using Kinect body tracking as the training target. Inverse kinematics converts skeletons to joint angles; STS repetitions are segmented; features (duration, trunk ROM, trunk peak velocities, waist-thigh ROM, knee ROM) are extracted and compared across sensor pairs using ICC and Bland-Altman. The paper reports high agreement for duration and trunk-related features, low agreement for knee ROM, and concludes that radar is reliable for trunk-level STS assessment.

Significance. The work addresses a real need: non-contact, privacy-preserving motion analysis for fall-risk assessment. It builds on the authors' prior mmPose-FK work and provides an end-to-end pipeline from radar point clouds to STS features, with a 45-participant dataset. The honest reporting of the low knee ICC and the explicit acknowledgment of Kinect lower-leg jitter are commendable. The independent radar-vs-wearable agreement for trunk ROM (0.72) suggests that radar captures some trunk-level information, but the evidence is not as strong as the paper claims. The central limitation—Kinect serving as both training target and comparison reference—undermines the K-R agreement as a validation, and the absence of a gold-standard reference system means the accuracy of radar-derived angles remains unestablished. The paper is a useful feasibility study, but its conclusions require tempering.

major comments (4)
  1. [Sections III-B, IV-B and Table II] The radar pose model is trained on Kinect skeletons (Section III-B explicitly states that "the Kinect skeleton serves as the learning target for the radar"), and the same Kinect is used as the reference for the K-R ICCs. The K-R trunk ICCs (TrunkROM 0.9295, TrunkFlexionPeakVelocity 0.8723) therefore partly quantify the model's ability to reproduce its training target, not its measurement accuracy against an independent standard. The only independent comparison, radar versus wearables, gives TrunkROM ICC 0.7218, TrunkFlexionPeakVelocity 0.6209, and TrunkExtensionPeakVelocity 0.3787 (Table II). The Section IV-D conclusion that radar "remains a reliable tool for assessing trunk movements, showing high agreement" is not supported by the independent evidence, particularly for extension peak velocity. Please either soften the claim to reflect moderate trunk ROM agreement and limited extension-velocity agreement, or add an external validation against a motion-capture reference.
  2. [Section III-G] Outliers are removed using |Z|>3 before computing ICCs, but the number of removed data points per feature is not reported. Post-hoc outlier removal can inflate ICC estimates, especially with a limited number of participants. Please report the number of removed repetitions for each feature and provide a sensitivity analysis by also reporting ICCs computed without outlier removal. In addition, Table II gives only point estimates; please add confidence intervals and the effective sample size (number of participants and repetitions) for each ICC so the precision of these agreement measures can be assessed.
  3. [Section III-D] The synchronization of wearable signals is performed by cross-correlating the knee-angle signals, which is the very feature later used to evaluate KneeROM. This introduces a form of circularity: the R-W knee ICC can be inflated by the alignment procedure, and the time shift derived from knee-angle correlation also affects the temporal alignment of all other features. Although the knee ICCs are low across all pairs, the procedure should be justified as independent of the measured outcomes. Please report the distribution of the estimated lags and run a robustness check using an alternative alignment target (e.g., trunk/waist angle) or a synchronization method that does not rely on the evaluated signal.
  4. [Sections IV-A and IV-D] No gold-standard motion capture system is used to validate the Kinect skeleton that serves both as the training target and as the comparison reference for radar. The paper itself acknowledges "the Kinect skeleton's abnormal jittering in the lower leg" and suggests using a VICON system in future work. Without an external reference, the paper should not interpret the K-R agreement as evidence of measurement accuracy; the claims should be limited to cross-modal agreement. Please add an explicit statement that the current study validates agreement but not absolute accuracy, and clearly separate "learning fidelity" (K-R) from "cross-modal agreement" (R-W) throughout the discussion and conclusion.
minor comments (5)
  1. [Sections I and II-B] The phrase "they are sensitivity to lighting conditions" should read "they are sensitive to lighting conditions" (also appears as "sensitivity" in the same context in the Introduction).
  2. [Section III-D] The Kinect skeleton is repeatedly called the "ground truth" for the radar, but Section IV-B later notes the Kinect skeleton's lower-leg jitter. Using the term "reference" instead of "ground truth" would be more precise and avoid overstating the reference's accuracy.
  3. [Section III-F] The repetition-matching step accepts start-time differences up to 0.5 seconds; please report how many repetitions were excluded by this criterion and whether the excluded repetitions were distributed evenly across sensors and participants.
  4. [Table II] The table would benefit from sample sizes and confidence intervals for each ICC value, or at least a footnote stating the number of STS repetitions and participants used for each comparison.
  5. [Section IV-D] The statement "the current azimuth and elevation angle resolution is 29 degrees" is useful but it is not supported by any measurement or derivation in the text; please provide a reference to the sensor datasheet or a calculation.

Circularity Check

2 steps flagged · score 5.0 of 10

Radar-vs-Kinect agreement is partly a training-fit metric, and the only independent radar-vs-wearable trunk agreement is moderate; the 'high agreement' conclusion is partially circular.

  1. fitted input called prediction [Section III-D (Sensors Synchronization) and Section IV-B (Results of ICCs), Table II]
    "In the mmPose-FK pose estimation model, the Kinect skeleton serves as the learning target for the radar. The radar skeleton represents the model’s predicted output, while the Kinect skeleton serves as the corresponding ground truth, with both consisting of 17 joints. ... Additionally, the ICC between the radar and Kinect also reflects how effectively the radar learns from the Kinect, given that the Kinect’s skeleton data was used as ground truth during the radar’s pose estimation process."

    The radar pose model is trained to reproduce Kinect skeletons, so the K-R ICCs (e.g., TrunkROM 0.9295, TrunkFlexionPeakVelocity 0.8723) measure how well the model matches its own training labels. Reporting these ICCs as evidence that radar 'shows high agreement with the Kinect' is partly a self-consistency check rather than an independent validation. The Kinect is both the learning target and the comparison reference, so the K-R agreement is forced to some degree by construction and cannot by itself establish true measurement accuracy.

  2. fitted input called prediction [Section III-D (Sensors Synchronization)]
    "We then interpolated the signals to a common time base and computed the cross-correlation for the interpolated knee angle signals. By identifying the index of the maximum correlation, we determined the time shift (lag), subsequently adjusting the wearable time vector accordingly."

    The wearable time axis is aligned by maximizing the cross-correlation of the very knee-angle signals that are later used for KneeROM agreement, and this aligned time base is used for all feature comparisons. This injects the compared signal into the preprocessing, so the R-W agreement is not fully independent. The effect is mild for trunk features and the reported knee ICCs are low, but the time-shift parameter is fitted to the outcome signal, making the subsequent agreement partially circular.

full rationale

The central claim that mmWave radar 'remains a reliable tool for assessing trunk movements, showing high agreement with the Kinect and wearable sensors' rests on two pillars. The K-R pillar is partially circular: Section III-D states that the Kinect skeleton is the learning target for the radar, so the high K-R ICCs for trunk features (0.9295, 0.8723, 0.7862) largely reflect how well the model reproduces its training labels. The paper itself acknowledges this by noting that the K-R ICC 'reflects how effectively the radar learns from the Kinect.' The R-W pillar is independent of the Kinect training, but its trunk ICCs are only moderate-to-poor (TrunkROM 0.7218, TrunkFlexionPeakVelocity 0.6209, TrunkExtensionPeakVelocity 0.3787), which does not support the sweeping 'high agreement with wearable sensors' language. A secondary circularity is the wearable synchronization via cross-correlation of the knee-angle signals being compared. The paper is not wholly circular: it reports low KneeROM ICCs honestly, flags the Kinect jitter limitation, and includes an independent R-W comparison. However, the strongest reported agreement for trunk features is inflated by the Kinect-labeled training, and the independent evidence is weaker than the conclusion states. Score 5 reflects partial circularity where one 'validation' comparison reduces to a training-fit metric but independent evidence remains.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a learned pose model trained on Kinect labels, on the assumption that Kinect is a valid ground truth, and on several hand-chosen analysis thresholds. These are the inputs the paper pulls from prior work or chooses ad hoc rather than deriving.

free parameters (4)
  • mmPose-FK model weights = not disclosed (from prior work [15])
    The radar skeleton, the foundation of all radar features, is generated by a deep network trained on Kinect skeleton labels; its learned weights determine joint-angle accuracy and are central to every result.
  • Z-score outlier removal threshold = 3
    Chosen by hand in Section III-G to remove outliers; no sensitivity analysis is reported, so the impact on ICC values is unknown.
  • Repetition matching time tolerance = 0.5 seconds
    Chosen threshold in Section III-F for matching STS repetitions across sensors; affects how many repetitions are retained for analysis.
  • Signal filtering and smoothing parameters = not specified
    High-pass, low-pass, and Whittaker-Eilers filter parameters are referenced to prior work [43], [44] but not quantified here; they influence angle and velocity estimates.
assumptions (4)
  • domain assumption Kinect body tracking provides joint positions accurate enough to serve as ground truth for radar training and comparison.
    Section IV-A uses Kinect built-in body tracking as the reference; no validation against VICON or another gold standard is provided, and the paper itself notes Kinect lower-leg jitter.
  • domain assumption The mmPose-FK model, trained on Kinect-labeled radar point clouds, generalizes to held-out participants and environments.
    Section III-B describes LOOCV training; the paper assumes the model's skeleton outputs are accurate enough for STS feature extraction, but does not independently validate the model in this manuscript.
  • domain assumption Wearable gyroscope integration, after filtering and cross-correlation synchronization, yields valid joint-angle signals.
    Section III-D derives wearable angles by integration and aligns them by maximizing cross-correlation of knee-angle signals, assuming no drift or bias that would distort the comparison.
  • standard math Standard FMCW radar range, Doppler, and angle estimation equations produce a faithful point cloud of the human body.
    Equations (1) through (9) in Section III-A are textbook FMCW processing; the assumption is the hardware and processing chain implement them correctly.

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Cite this review

Pith. "Pith review of mmWave Radar for Sit-to-Stand Analysis: A Comparative Study with Wearables and Kinect." pith.science (2026). https://pith.science/paper/IZ7GDVQT

@misc{pith2026241114656,
  author       = {Pith},
  title        = {Pith review of: mmWave Radar for Sit-to-Stand Analysis: A Comparative Study with Wearables and Kinect},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IZ7GDVQT}},
  note         = {Machine review of arXiv:2411.14656}
}
read the original abstract

This study explores a novel approach for analyzing Sit-to-Stand (STS) movements using millimeter-wave (mmWave) radar technology. The goal is to develop a non-contact sensing, privacy-preserving, and all-day operational method for healthcare applications, including fall risk assessment. We used a 60GHz mmWave radar system to collect radar point cloud data, capturing STS motions from 45 participants. By employing a deep learning pose estimation model, we learned the human skeleton from Kinect built-in body tracking and applied Inverse Kinematics (IK) to calculate joint angles, segment STS motions, and extract commonly used features in fall risk assessment. Radar extracted features were then compared with those obtained from Kinect and wearable sensors. The results demonstrated the effectiveness of mmWave radar in capturing general motion patterns and large joint movements (e.g., trunk). Additionally, the study highlights the advantages and disadvantages of individual sensors and suggests the potential of integrated sensor technologies to improve the accuracy and reliability of motion analysis in clinical and biomedical research settings.

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Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.