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Abstract
A central challenge in clinical motor assessment is to infer stable motor capacity from task observations shaped by context. We propose a multi-probe trait–state latent framework that models each standardized task as a partial observation of a subject-level motor trait. A shared latent captures cross-context regularities while probe-specific latents absorb transient effects, and the trait is inferred via precision-weighted aggregation. Resting-state gait is decoded from the shared trait alone, enabling cross-context consistency evaluation. Under subject-disjoint 12-center leave-one-center-out evaluation on 1,196 subjects, the proposed method achieves a 0.60 correlation with held-out real-gait phenotype descriptors, improves biomechanical realism, and outperforms strong conditional baselines. These results support the feasibility of trait–state latent modeling for clinical motor assessment from standardized task probes.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026/paper/3286_paper.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to the Code Repository
N/A
Link to the Dataset(s)
N/A
BibTex
@InProceedings{YuJia_Multiprobe_MICCAI2026,
author = { Yu, Jiahui AND Cui, Yanxin AND Lou, Xiaowen AND Wu, You AND Zhang, Yaping AND Pang, Ting AND Xu, Xin},
title = { { Multi-probe Trait–State Latent Modeling for Cross-Context Clinical Motor Assessment } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 16896},
month = {September},
page = {pending}
}
Reviews
Review #1
- Please describe the contribution of the paper
This work proposes a method to learn a shared representation of a person’s motor traits. It separates task-specific movements (temporary changes) from subject-specific traits (stable patterns). The goal is to improve clinical motor assessment across tasks. The method assumes that subject traits and task states follow different Gaussian distributions. The motion data is modeled in two ways: (i) task-specific input that depends on the subject, task, and clinical context, and (ii) repeated patterns of the same subject across different tasks. The final subject representation is obtained by combining information from multiple tasks. The model uses pre-trained VQ-VAE and Transformer for feature extraction, followed by two MLP heads to estimate the latent variables. Results using a leave-one-center-out setup are reported.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The problem is relevant and important.
The method effectively separates task-specific variations from stable subject traits and combines them for a consistent representation across clinical settings.
The authors design four baselines to validate the approach.
The results show improvements over the baselines and are promising.
Figures 1 and 2 are clear and informative.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
Section 2 needs to be reorganised. The current flow is hard to follow. The tokenization part can be better placed. The writing is too abstract and should be simplified for clarity.
For D = 171, why are the remaining ~300 dimensions of \beta_s ignored?
In the tokenization step, have you compared interleaving vs concatenation? What is their impact? Have you checked their individual contributions.
The method description does not clearly explain the main contribution, i. e. , disentangling subject traits and task-specific motion. It also does not fully match the pipeline shown in Fig. 2.Part (a) of Fig. 2 is missing. It is also unclear where VQ-VAE is used in the figure. Overall, the figure is not self-explanatory.
Why is Product-of-Experts (PoE) used for multi-probe aggregation? Also, why are three probes used per task?
The metrics in Table 3 are not sufficient to support the disentanglement claim. Additional statistical or visual analysis of the learned feature distributions is needed.
What is the theoretical and empirical justification for the claimed orthogonality between subject traits and task-specific latents in Section 2.2 (Eqs. 2 and 3)?
What is the impact of not using resting-state gait in reconstruction? An ablation study is needed to support such design choices.
A correlation of 0.6 with MAE of 0.38 may have limited clinical relevance. Please clarify. Also, explain what the 9 phenotypes represent.
A clear table describing the baselines (input, method, output) would improve readability. A supplementary diagram of the pipelines can be helpful.
Please correct a few spelling mistakes in some figures.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not provide sufficient information for reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(3) Weak Reject — marginally below the acceptance threshold, but would not mind if accepted, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The paper addresses a relevant problem and presents a reasonable approach with some positive empirical results. However, the overall contribution appears somewhat limited, and certain aspects of the method and evaluation could be better clarified and strengthened. In particular, the exposition of the core idea and some design choices are not entirely clear, and the experimental validation could benefit from more comprehensive analysis.
- Reviewer confidence
Somewhat confident (2)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
Reject
- [Post rebuttal] Please justify your final decision from above.
The rebuttal addresses several of my concerns, particularly regarding the scope of \beta_s, probe composition, and the role of resting-gait reconstruction, and I appreciate those clarifications. However, some important concerns remain unresolved: (i) the disentanglement claim is acknowledged to be operational rather than formally established, without theoretical justification for the orthogonality assumption; (ii) major issue is reproducibility remains limited, as no code is provided and the current methodological description appears insufficient for reproduction; and (iii) certain organizational and clarity issues in Section 2 are unlikely to be adequately resolved within the current revision cycle. Also, the reported interpretable phenotype are still not clear to me. Therefore, I maintain my rating.
Review #2
- Please describe the contribution of the paper
The paper proposes a trait–state latent framework for clinical motor assessment. The idea is to infer a subject-level motor trait from multiple task probes while separating task-specific effects. This trait is then used to generate gait as a way to validate consistency across contexts.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
- Capturing stable motor capacity across different tasks is important in clinical settings.
- The idea of separating a shared trait from task-specific effects makes sense and is very well motivated.
- The multi-center dataset and leave-one-center-out evaluation are a strong point and help with generalization.
- The evaluation looks at several aspects (descriptor fidelity, biomechanics, consistency), which is useful to address the feasibility of this approach..
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
- Similar disentanglement ideas have been explored before. 2.The model is evaluated via gait generation and correlation with descriptors, and it is not entirely clear how this translates to better clinical assessment. 3.It is still unclear what the learned “trait” actually represents in practice. The interpretation remains somewhat abstract in the text, it will be good to get better clarity on that. 4.The method is quite complex, and it is not fully clear which parts are really needed, since the ablation study is limited. 5.There is not much discussion about how robust the method is when tasks are noisy or not performed correctly.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not mention open access to source code or data, but provides a clear and detailed description of the algorithm to ensure reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(5) Accept — should be accepted, independent of rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The paper addresses a relevant problem and proposes a framework to model stable motor traits across different tasks. The idea of separating a shared latent from task-specific effects is well motivated, and the overall approach is consistent. A strong point is the multi-center dataset and the leave-one-center-out – LOCO evaluation, which provides a good test of generalization. The evaluation is also fairly addressed, covering descriptor fidelity, biomechanics, and consistency, and the method shows consistent improvements. While the novelty is moderate and the evaluation is somewhat based on gait generation and descriptor correlation, the work is well executed.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
N/A
- [Post rebuttal] Please justify your final decision from above.
N/A
Review #3
- Please describe the contribution of the paper
The paper’s main contribution is a modeling framework that aims to distinguish a person’s stable, individual motor characteristics from context-dependent variations in movement across different tasks. Rather than treating each short standardized movement task as a direct measure of motor ability, the authors try to infer an underlying latent motor capacity that remains relatively consistent across contexts such as instruction, fatigue, or environment. To do this, they represent motion in a high-dimensional latent space, encode whole-body joint and body-part information through a global tokenization scheme, and use a transformer-based architecture to learn relationships between users, movements, and contextual effects.
- Please list the major strengths of the paper: you should highlight a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
Clear practical motivation: Inferring a person’s stable motor capacity from several short standardized tasks instead of treating each task as a direct measure of ability as an underlying goal is relevant for clinical assessment. Large and representative dataset: A major strength is the dataset size and composition, especially the approximately balanced 50/50 male-female representation and the broad age range from 19 to 93 years. Appropriate evaluation framework: The paper uses generally suitable evaluation parameters, which supports the seriousness of the empirical setup. Ambitious methodological design: The work proposes a sophisticated latent modeling framework to represent motion and to separate subject-specific traits from context effects, which is methodologically ambitious and innovative. Modern modeling approach: The combination of body/joint tokenization with transformer-based modeling is a technically interesting choice and shows novelty in how motion is represented and analyzed.
- Please list the major weaknesses of the paper. Please provide details: for instance, if you state that a formulation, way of using data, demonstration of clinical feasibility, or application is not novel, then you must provide specific references to prior work.
Weak empirical support for the main claim: The reported correlation of around 0.60 seems too weak to convincingly support the paper’s central thesis about stable individual motor traits. Lack of statistical validation: The paper could provide more sufficient statistical analysis, such as significance testing or confidence intervals, to justify its conclusions. Insufficient interpretability of the mathematical formulation: The mathematical setup in my opinion is underexplained, particularly regarding the definition of variables and solution spaces, making it hard to judge whether the model is well-founded. Lack of explanation for key body coefficients and representations: The paper introduces body coefficients and joint-rotation representations without clearly explaining how these are determined or why they are appropriate. Poor writing and unclear phrasing: A major weakness is that the paper’s phrasing appears confusing, making even the main research question harder to understand than necessary.
- Please rate the clarity and organization of this paper
Poor
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission does not mention open access to source code or data, but provides a clear and detailed description of the algorithm to ensure reproducibility.
- Based on your review and your understanding of the MICCAI Scientific Code of Ethics, do you believe this submission may involve a potential ethics concern or violation?
N/A
- Optional: If you have any additional comments to share with the authors, please provide them here. Please also refer to our Reviewer’s guide on what makes a good review and pay specific attention to the different assessment criteria for the different paper categories: https://conferences.miccai.org/2026/en/REVIEWER-GUIDELINES.html
N/A
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making.
(4) Weak Accept — marginally above the acceptance threshold, but would not mind if rejected, dependent on rebuttal
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
My rating is based on the paper’s combination of a strong and relevant research question, a substantial and demographically representative dataset, and a technically ambitious modeling approach, despite important weaknesses in clarity and empirical validation. The central idea of inferring a person’s stable motor characteristics across several short standardized tasks rather than equating each task directly with motor ability is interesting and clinically meaningful, and the dataset is a clear asset, especially given its broad age range and roughly balanced male/female representation. I also think the global whole-body tokenization strategy is a thoughtful design choice, since it reduces the risk of biasing the representation toward specific joints or body parts. These aspects make the paper novel enough and potentially impactful enough that I would lean toward acceptance.
At the same time, I do not see the paper as strong enough for a higher score because the evidence supporting the main claims is not yet fully convincing. The reported correlation of around 0.60 seems relatively weak for the strength of the conclusions the authors want to draw, and the absence of stronger statistical analysis, such as confidence intervals or significance testing, makes it difficult to judge the robustness of the findings. In addition, the paper does not explain key parts of the methodology clearly enough: the latent spaces, body coefficients, and joint representations are underdefined, the mathematical formulation is difficult to follow, and the dataset construction and fold splitting are not described in sufficient detail to make the work fully interpretable or reproducible. So overall, my score reflects a paper that is interesting, original, and potentially valuable, but also insufficiently explained and not yet empirically convincing enough to justify a clear accept.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the authors’ rebuttal, please state your final opinion of the paper.
Accept
- [Post rebuttal] Please justify your final decision from above.
The authors narrowed down the results and impact of their evaluation and therefore scoped their work more sufficiently. Since I see my main concern improved I’d keep my acceptance recommendation.
Author Feedback
We thank the reviewers for the careful feedback. We address the main concerns on scope, novelty, clarity, tokenization, trait-state evidence, and robustness using only submitted results.
Scope and empirical meaning (R1,R2,R5). Our goal is representation-level cross-context motor assessment, not diagnostic prediction. Standardized tasks are partial probes of a subject-level motor trait; gait synthesis is a held-out consistency probe decoded from z. Corr.=0.60 and MAE=0.38 measure fidelity to real-gait phenotype descriptors, not disease-classification accuracy. Under subject-disjoint 12-center LOCO, there is no target-center training, tuning, or leakage. With identical tokenizer, decoder, splits, and budget, our model improves over the strongest conditional baseline from 0.45 to 0.60 in Corr., 0.55 to 0.38 in MAE, and 0.52 to 0.72 in Top-1.The nine phenotypes are gait descriptors grouped into balance, strength, and rhythm.
Novelty and method clarity (R1,R2,R5). We agree that disentanglement is a broad prior idea; our contribution is clinical multi-probe trait inference with z-only held-out gait validation, not generic disentanglement or generation. The path is: SMPL-X fitting; VQ-VAE tokenization of lower-body/trunk/global streams; transformer inference of per-probe z/u posteriors; PoE aggregation of z; and gait decoding from aggregated z. D=171 contains dynamic per-frame parameters only: 165 joint rotations, 3 root orientation, and 3 root translation. beta_s is a 300D static body-shape parameter used in fitting/reconstruction, not a frame-wise transformer input. The three probes are SLS, STS, and HK, not repeated trials. PoE uses posterior variance for precision-weighted aggregation and down-weights uncertain probes.
Tokenization and concatenation (R1). The submitted “Concat (no latent)” baseline in Table 1 is a model-level baseline: it concatenates the three task probes and predicts gait without z/u factorization or PoE. It is not an isolated token-ordering ablation. Interleaving in Sec.2.3 serializes lower/trunk/global tokens frame by frame to preserve temporal alignment, while stream/time embeddings preserve component identity. Thus, the submitted evidence supports trait-state factorization with PoE over direct/concatenated conditioning, not interleaving itself as an independent contribution.
Evidence for trait-state separation (R1,R2,R5). We use “separation” operationally, not as formal identifiability. The model imposes an informational bottleneck: task reconstruction uses z and u, whereas gait decoding accesses z but not u. Submitted Table 3 shows lower task prediction from our z than from corresponding Direct T→G/CondGen representations (0.27 vs. 0.61/0.57), high task prediction from u (0.92), and lower intra-subject z variance (0.035 vs. 0.083/0.072). These diagnostics do not prove statistical independence, but support reduced task leakage and more stable subject-level encoding.
Resting gait, robustness, and reproducibility (R1,R2,R5). Resting gait is not excluded from training reconstruction: when available, Eq.9 includes gait-token reconstruction conditioned on z. What is withheld is using held-out gait as test-time input or conditioning; it is only the evaluation target. Eq.3 excludes u from the gait decoder to prevent task-specific transients from directly driving cross-context validation. Table 2 shows graceful degradation with fewer probes and reduced paired-gait supervision, addressing incomplete/noisy clinical observations. In every LOCO fold, held-out-center data are never used for tokenizer training, model fitting, or hyperparameter selection.
Meta-Review
Meta-review #1
- Your recommendation
Invite for Rebuttal
- Please justify your decision. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. In case of an invitation for rebuttal, clarify which points are important to address in the rebuttal.
Reviewers agree that the study addresses a clinical relevant problem and proposes a reasonable approach that shows promising results. Most concerns are regarding the clarity of design choices and experimental setup. I’d encourage the authors to respond to key confusions raised by the reviewers, especially regarding subject-level traits and their disentanglement from task-level latents.
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
My opinion aligns more with R5.I think the study is novel and important and makes a great contribution. Although the concept of disentanglement is frequently explored, its formulation for this particular clinical application is critical and needs to be systematically investigated. Although not theoretically guaranteed, the study validated the effectiveness of the disentanglement operationally. The experimental setup is solid. The studied cohort is a great asset to the community. These strengths outweighs the weaknesses.
Meta-review #2
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
Based on the review concerns and author feedback, I am of the opinion that the clarifications provided have helped scope the contributions made by the work appropriately and am inclined to recommend acceptance. I find the overall approach sound and sufficiently motivated in terms of the application being considered. Their clarification of details from the paper during the rebuttal is helpful and addresses many review points raised.
Meta-review #3
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation.
This is an interesting work to learn a shared representation of a person’s motor traits by separating the task-specific movements from subject-specific traits. This can be then used to perform clinical motor assessment tasks.
All the reviewers have agreed that this an important problem setting and the design makes sense and is intuitive. The baselines are well demonstrated with their corresponding technique gains also well justified. There were some issues raised regarding the theoretical motivation of the framework, however, the authors have tried to convincingly address those during their rebuttal.
The major issue still pertinent with the paper is about the clarity and the organization of the paper with a better understanding so that the techniques and the framework could be implemented and reproducible. The authors should really consider this seriously to have a broader impact of this work.