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REVIEW 4 major objections 6 minor 20 references

Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A neuro-symbolic model that learns ordered if-then rule lists can recognize radar hand gestures at 93% F1 while shrinking to five rules.

desk verdict Validation-set best-epoch metrics likely inflate the headline 93% F1; the paper is still a useful benchmark for interpretable radar HGR, but the central trade-off claim needs held-out test numbers. read the letter →

arxiv 2506.22443 v1 pith:CPHAEVB4 submitted 2025-06-11 cs.LG cs.HC

Sarah Seifi , Tobias Sukianto , Cecilia Carbonelli , Lorenzo Servadei , Robert Wille This is my paper · ORCID
classification cs.LGcs.HC
keywords neurosymbolicAIrulelearninghandgesturerecognitionFMCWradarinterpretablemachinetransferlistsedgedeployment
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 RL-Net, a neural network that learns ordered if-then rule lists through gradient-based optimization, can be applied to real radar hand-gesture data without losing the interpretability that makes rule-based models attractive. On a public 60 GHz FMCW radar dataset with five gestures, the model reaches about 93% F1 after user-specific fine-tuning, and every adapted model converges to five rules with fewer total conditions than the initial seven-rule model. The authors also show that adding batch normalization and a validation-time sparsity penalty makes training more stable and reduces rule complexity. The broader claim is that a practical middle ground between fully transparent rules and opaque neural networks is feasible for edge-deployable gesture sensing.

What carries the argument

The load-bearing mechanism is RL-Net's rule layer: each rule neuron acts as a logical AND over binarized input features, with ternary weights in $\{-1,0,1\}$ deciding whether a feature must be present, must be absent, or is ignored. A fixed hierarchy layer makes rules fire in order so only the first satisfied rule predicts the class, and a sparsity loss built on hard-concrete binary masks pushes most weights to zero so the surviving rule list stays short. Batch normalization and a higher validation-time sparsity weight are the stability-enhancing additions that the paper contributes.

What would settle it

A reader could train RL-Net on the same dataset with learnable binarization thresholds instead of fixed ones: if performance improves substantially, the fixed thresholds are the binding limitation; if it does not, the reported results do not depend on that binarization as much as the paper suggests. Separately, permuting rule order before training should change which rule indices survive if the fixed hierarchy is the cause of the pruning bias.

Watch

Extended reading notes

Core claim

The central claim is that a neuro-symbolic rule list learner can perform close to a black-box neural model on real radar data while keeping its decisions human-readable. With two modifications to the original RL-Net—batch normalization after the rule layer and an increased sparsity weight during validation—the user-agnostic model reaches roughly 90% F1 with far fewer active conditions, and per-user transfer learning raises average F1 to 93.05% while all adapted models settle at five rules. The paper positions this as a favorable trade-off: compared with MIRA's handcrafted rules and XentricAI's SHAP-explained GRU, RL-Net offers more learning flexibility than MIRA and better fine-tuned accuracy than XentricAI, although XentricAI additionally models the background class and performs frame-level detection.

Load-bearing premise

The load-bearing assumption is that the five frame-averaged radar features, after being binarized to 0/1 with fixed thresholds, still carry enough information to distinguish the five gestures; if that binarization discards the discriminating signal, both the accuracy and the interpretability claims collapse.

Editorial extensions

If this is right

  • If the 93% F1 result with five rules holds, radar hand-gesture recognition can be deployed as short, human-auditable rule lists on edge devices.
  • The identified hierarchy bias means that without architectural changes, later rules dominate and early rules are pruned, so improving interpretability further requires adaptive or learnable rule ordering.
  • The comparison suggests RL-Net is a practical compromise: more flexible than MIRA's handcrafted rules and more accurate after fine-tuning than the XentricAI backbone, though the latter also handles background detection.
  • Batch normalization plus validation-time sparsity reduces both rule complexity and run-to-run variance, making the learned rule lists more consistent across training runs.

Reading between the lines

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

  • The authors flag fixed thresholds for binarizing continuous radar features as a limitation; a natural extension we draw is that learnable thresholds could be the next step for personalization, at the cost of some of the method's simplicity.
  • Because the input representation is only five frame-averaged features, the approach suggests that other low-dimensional sensing tasks with strong physical structure could benefit from the same rule-list neuro-symbolic treatment.
  • A testable consequence of the hierarchy-bias story is that permuting the order of the rules before training should shift which rule indices survive, since the bias is tied to evaluation order rather than to feature content.
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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 / 6 minor

Summary. The paper applies RL-Net, a neuro-symbolic rule-list learner, to FMCW radar hand-gesture recognition. It introduces batch normalization and a validation-time regularization scheme to stabilize training, evaluates user-specific transfer learning, and compares RL-Net against a white-box rule system (MIRA) and a black-box GRU system (XentricAI). The headline claim is that RL-Net achieves a favorable accuracy-interpretability trade-off, with roughly 93% F1 and compact five-rule lists after user-specific fine-tuning.

Significance. If the empirical claims are confirmed, this would be a useful demonstration that neuro-symbolic rule lists can be applied to real radar sensor data and personalized via transfer learning. The paper uses a public dataset, reports interpretability metrics (number of rules and total conditions), and provides confidence intervals for the architectural comparison in Table 2. The contribution is primarily empirical, and the main risk lies in the evaluation protocol rather than in the internal logic of the method.

major comments (4)
  1. [§6.2, Table 3 (with §5)] The sentence "All results are reported for the best-performing epoch on the validation set" combined with the per-user 64/16/20 split implies that the accuracy and F1 columns in Table 3 are validation-set statistics selected at the best validation epoch, not held-out test estimates. The abstract's 93.03% F1 and the claim that transfer learning improves accuracy are therefore selection statistics, and no test-set evaluation is reported anywhere in Table 3. Because the central claim is a quantitative accuracy-interpretability trade-off, the authors should either report metrics on the 20% test split at the epoch selected by validation, or explicitly label all numbers as validation performance and refrain from making generalization claims. Without this, the comparison to MIRA (94.9%) and XentricAI (90.2%) is not interpretable.
  2. [§6.2, Table 3] There is a numerical inconsistency between the abstract and Table 3: the abstract states 93.03% F1, while Table 3 reports an average TL F1 of 93.06% and an average TL accuracy of 93.05%. In addition, the claim that rule complexity is "significantly reduced" is not supported for every user: users 4 and 6 retain 36 conditions, equal to the initial 36-condition baseline, and only the average (26.67) is lower. Please report per-user deltas and provide paired statistics or confidence intervals for the improvement claim.
  3. [§6.2, 'Comparison with Baselines'] The baseline numbers for MIRA (94.9%) and XentricAI (90.2%) are quoted without confidence intervals, error bars, or a statement of whether they were computed under the same train/validation/test split and the same metric definition (accuracy vs. F1). Since both baselines are from the authors' own prior work, an identically-protocoled re-evaluation, or at least a clear description of how these numbers were obtained, is needed to support the ranking "RL-Net outperforms XentricAI."
  4. [§4.3 and §6.2] The fixed-threshold binarization of the five radar features is a load-bearing preprocessing choice, and the paper acknowledges it as a limitation. However, no sensitivity analysis is provided, so the reader cannot assess how much of the reported accuracy-interpretability trade-off depends on the specific threshold values. A small threshold perturbation study, or at minimum a description of how the thresholds were selected, would address this concern.
minor comments (6)
  1. [§4.1] The dataset description is internally inconsistent: "Twelve users... Each participant completed 1,000 samples, totaling 31,000 gesture recordings" is arithmetically wrong (12 × 1000 = 12,000), and §5 also states that 12,000 samples were used in total. Please correct the total or clarify what the 31,000 counts.
  2. [§6.1, after Figure 3] The text contains the typo "It achives strong performance" and should read "It achieves strong performance."
  3. [Figure 2 caption] The caption contains the typo "Prepocessing" and should read "Preprocessing."
  4. [§5] The pretraining data split for the base model is not explicitly specified; only the per-user TL split (64/16/20) is given. Please state the train/validation/test proportions for the six-user pretraining set as well.
  5. [§3.1 and §5] The loss function in §3.1 includes an L2 term with weight λ2, while §5 states that L2 regularization was disabled. Please clarify whether λ2 is set to zero or whether the loss function is used in a modified form.
  6. [Table 3] The columns "#Rules" and "#Conditions" refer to the post-transfer-learning models, but this is not stated in the table caption or column headers. Please make this explicit.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the central claim is an empirical benchmark of an externally published model, with only minor non-load-bearing self-citation exposure.

full rationale

The paper's main result, that RL-Net reaches roughly 93% F1 after transfer learning while reducing rule complexity, is an empirical measurement reported in Tables 2 and 3, not a quantity derived from the model's own fitted parameters. RL-Net itself is taken from external prior work (Dierckx et al., ref. [7]); the radar preprocessing pipeline is from ref. [4]; and the dataset is publicly released via IEEE Dataport (ref. [20]). The same-author citations — MIRA (ref. [10]), XentricAI (ref. [15]), and the dataset (ref. [20]) — are used as comparison baselines and shared resources, not as justifications that logically force the reported outcome. No equation in the paper defines a predicted result in terms of the fitted values, and no fitted parameter is renamed as a prediction. The validation-set-based reporting in Section 6.2 ('All results are reported for the best-performing epoch on the validation set') and the fixed-threshold binarization limitation are genuine evaluation-validity concerns, but they are not circularity: they concern whether the measured number generalizes, not whether the claim reduces to its inputs by construction. Overall, the derivation chain is self-contained with respect to circularity; the score of 2 reflects only the mild self-citation exposure from using the authors' own earlier systems and dataset as benchmarks.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central result is an empirical comparison, not a derivation, so the ledger is dominated by imported domain assumptions: the FMCW feature pipeline, the gesture-window labeling rule, and the fixed binarization of inputs. The main free parameters are the hyperparameters chosen by grid search, plus unstated feature-binarization thresholds. No new physical or conceptual entities are introduced.

free parameters (5)
  • Sparsity regularization weight lambda_1 (training) = 0.025
    Chosen via grid search (Section 5); controls rule sparsity and directly shapes the accuracy-complexity trade-off.
  • Sparsity regularization weight lambda_1 (validation) = 0.3
    Custom early stopping uses a higher validation-time weight (Section 5); steers model selection toward simpler rule sets.
  • Hard concrete stretch parameters gamma, zeta, beta = -0.1, 1.1, 2/3
    Reparameterization constants for the binary mask sampling, set in Section 5; they affect which weights are pruned and hence rule complexity.
  • Optimizer, learning rate, batch size = Adam, 0.01, 40
    Selected by grid search (Section 5); these training choices influence convergence and final rule quality.
  • Feature binarization thresholds = not reported
    Section 3.1 assumes binarized inputs x_i in {0,1}; the thresholds that produce these binary features are not specified, and Section 6.2 acknowledges that fixed thresholds may limit adaptability.
assumptions (4)
  • domain assumption The FMCW preprocessing pipeline from [4] yields five features (range, Doppler, azimuth, elevation, magnitude) that preserve gesture-discriminative information.
    Section 4.3 adopts this pipeline without independent validation; all downstream classification results depend on these features.
  • domain assumption Gesture labels are assigned using a fixed 10-frame window centered on the frame with minimum radial distance.
    Section 4.2 follows [4]; if the window is misaligned, the extracted features are corrupted, affecting all model comparisons.
  • domain assumption Binarizing continuous features to {0,1} with fixed thresholds preserves enough information for rule learning.
    Section 3.1 feeds binarized features into RL-Net; Section 6.2 explicitly flags this as a limitation, making it a declared but unresolved assumption.
  • standard math Hard concrete relaxation of binary masks approximates discrete rule membership well enough for gradient training.
    Section 3.1 reparameterizes ternary weights following Louizos et al. [19]; the validity of this approximation is imported from prior work.

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Cite this review

Pith. "Pith review of Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition." pith.science (2026). https://pith.science/paper/CPHAEVB4

@misc{pith2026250622443,
  author       = {Pith},
  title        = {Pith review of: Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CPHAEVB4}},
  note         = {Machine review of arXiv:2506.22443}
}
read the original abstract

Rule-based models offer interpretability but struggle with complex data, while deep neural networks excel in performance yet lack transparency. This work investigates a neuro-symbolic rule learning neural network named RL-Net that learns interpretable rule lists through neural optimization, applied for the first time to radar-based hand gesture recognition (HGR). We benchmark RL-Net against a fully transparent rule-based system (MIRA) and an explainable black-box model (XentricAI), evaluating accuracy, interpretability, and user adaptability via transfer learning. Our results show that RL-Net achieves a favorable trade-off, maintaining strong performance (93.03% F1) while significantly reducing rule complexity. We identify optimization challenges specific to rule pruning and hierarchy bias and propose stability-enhancing modifications. Compared to MIRA and XentricAI, RL-Net emerges as a practical middle ground between transparency and performance. This study highlights the real-world feasibility of neuro-symbolic models for interpretable HGR and offers insights for extending explainable AI to edge-deployable sensing systems.

Figures

Figures reproduced from arXiv: 2506.22443 by the authors.

Figure 1
Figure 1. Comparison of model categories (white-box, gray-box, black-box) based on model complexity and predictive performance. Neuro-symbolic approaches like RL-Net occupy an intermediate "gray-box" space, bal￾ancing interpretability and performance. An ideal model has high model interpretability as well as performance. 2.4. Motivation for Our Work To date, neuro-symbolic models have not been applied to real-world FMCW radar… view at source ↗
Figure 2
Figure 2. Overview of the proposed interpretable gesture recognition pipeline. Input & Rule Layers. The input layer receives binarized features 𝑥𝑖 ∈ {0, 1}. Each rule neuron functions as a logical AND, with ternary weights W𝑇 encoding positive, negative, or excluded feature use. To enable differentiable training, these weights are reparameterized as W𝑇 = W𝑆 ∘ W𝐻, where W𝐻 ∈ [0, 1] are approximated binary masks following [19].… view at source ↗
Figure 3
Figure 3. Training dynamics of RL-Net. (A) Total number of active rule conditions over training epochs, showing progressive sparsity and improved interpretability. (B) Histogram of surviving rule indices and their number of conditions in a representative run. Due to the fixed rule evaluation hierarchy, early rules are pruned, while later rules dominate, highlighting a structural imbalance. Comparison with Baselines. As a refe… view at source ↗

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Reference graph

Works this paper leans on

20 extracted references · 17 canonical work pages

  1. [1]

    Ohn-Bar, M

    E. Ohn-Bar, M. M. Trivedi, Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations, IEEE transactions on intelligent transportation systems 15 (2014) 2368–2377

    work page 2014

  2. [2]

    Al Mudawi, H

    N. Al Mudawi, H. Ansar, A. Alazeb, H. Aljuaid, Y. AlQahtani, A. Algarni, A. Jalal, H. Liu, Innovative healthcare solutions: robust hand gesture recognition of daily life routines using 1d cnn, Frontiers in Bioengineering and Biotechnology 12 (2024) 1401803

    work page 2024

  3. [3]

    L. Yang, J. Huang, T. Feng, W. Hong-An, D. Guo-Zhong, Gesture interaction in virtual reality, Virtual Reality & Intelligent Hardware 1 (2019) 84–112

    work page 2019

  4. [4]

    Strobel, S

    M. Strobel, S. Schoenfeldt, J. Daugalas, Gesture recognition for fmcw radar on the edge, in: 2024 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT), 2024, pp. 45–48. doi:10.1109/WiSNeT59910.2024.10438579

    work page arXiv 2024

  5. [5]

    B. Yan, P. Wang, L. Du, X. Chen, Z. Fang, Y. Wu, mmgesture: Semi-supervised gesture recognition system using mmwave radar, Expert Systems with Applications 213 (2023) 119042

    work page 2023

  6. [6]

    Y. Ji, Y. Sun, Y. Zhang, Z. Wang, Y. Zhuang, Z. Gong, D. Shen, C. Qin, H. Zhu, H. Xiong, A comprehensive survey on self-interpretable neural networks, arXiv preprint arXiv:2501.15638 (2025)

    arXiv 2025

  7. [7]

    Dierckx, R

    L. Dierckx, R. Veroneze, S. Nijssen, Rl-net: Interpretable rule learning with neural networks, in: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2023, pp. 95–107

    work page 2023

  8. [8]

    Frasca, D

    M. Frasca, D. La Torre, G. Pravettoni, I. Cutica, Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review, Discover Artificial Intelligence 4 (2024) 15

    work page 2024

Show all 20 references
  1. [9]

    Gardin, R

    F. Gardin, R. Gautier, N. Goix, B. Ndiaye, J.-M. Schertzer, Skoperules, https://github.com/ scikit-learn-contrib/skope-rules, 2017

    2017

  2. [10]

    Seifi, T

    S. Seifi, T. Sukianto, C. Carbonelli, L. Servadei, R. Wille, Interpretable rule-based system for radar-based gesture sensing: Enhancing transparency and personalization in ai, in: 2024 21st European Radar Conference (EuRAD), 2024, pp. 156–159. URL: https://ieeexplore.ieee.org/...

    2024

  3. [11]

    Franceschini, M

    S. Franceschini, M. Ambrosanio, S. Vitale, F. Baselice, A. Gifuni, G. Grassini, V. Pascazio, Hand gesture recognition via radar sensors and convolutional neural networks, in: 2020 IEEE Radar Conference (RadarConf20), IEEE, 2020, pp. 1–5

    2020

  4. [12]

    B. Jin, X. Ma, Z. Zhang, Z. Lian, B. Wang, Interference-robust millimeter-wave radar-based dynamic hand gesture recognition using 2d cnn-transformer networks, IEEE Internet of Things Journal (2023)

    2023

  5. [13]

    S. M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, Advances in neural information processing systems 30 (2017)

    2017

  6. [14]

    H. Chen, J. D. Janizek, S. Lundberg, S.-I. Lee, True to the model or true to the data?, arXiv preprint arXiv:2006.16234 (2020)

    2020 arXiv

  7. [15]

    Seifi, T

    S. Seifi, T. Sukianto, M. Strobel, C. Carbonelli, L. Servadei, R. Wille, Xentricai: A gesture sensing calibration approach through explainable and user-centric ai, in: World Conference on Explainable Artificial Intelligence, Springer, 2024, pp. 232–246

    2024

  8. [16]

    Y. Li, Z. O’Neill, L. Zhang, J. Chen, P. Im, J. DeGraw, Grey-box modeling and application for building energy simulations-a critical review, Renewable and Sustainable Energy Reviews 146 (2021) 111174

    2021

  9. [17]

    L. Qiao, W. Wang, B. Lin, Learning accurate and interpretable decision rule sets from neural networks, in: Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, 2021, pp. 4303–4311

    2021

  10. [18]

    Y. Yang, W. Ren, S. Li, Hyperlogic: Enhancing diversity and accuracy in rule learning with hypernets, Advances in Neural Information Processing Systems 37 (2024) 3564–3587

    2024

  11. [19]

    Louizos, M

    C. Louizos, M. Welling, D. P. Kingma, Learning sparse neural networks through l_0 regularization, in: International Conference on Learning Representations, 2018

    2018

  12. [20]

    Seifi, T

    S. Seifi, T. Sukianto, C. Carbonelli, 60 ghz fmcw radar gesture dataset, 2024. URL: https://dx.doi. org/10.21227/s12w-cc46. doi:10.21227/s12w-cc46

    2024 doi

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Reviewed August 7, 2026 · model on record in the stance chip above.