Title: MoE-Gyro: Self-Supervised Over-Range Reconstruction and Denoising for MEMS Gyroscopes
Venue: NeurIPS 2025
Accepted: yes
Keywords: MEMS Gyroscopes; Signal reconstruction; Noise suppression
Abstract: MEMS gyroscopes play a critical role in inertial navigation and motion control applications but typically suffer from a fundamental trade-off between measurement range and noise performance. Existing hardware-based solutions aimed at mitigating this issue introduce additional complexity, cost, and scalability challenges. Deep-learning methods primarily focus on noise reduction and typically require precisely aligned ground-truth signals, making them difficult to deploy in practical scenarios and leaving the fundamental trade-off unresolved.  To address these challenges, we introduce Mixture of Experts for MEMS Gyroscopes (MoE-Gyro), a novel self-supervised framework specifically designed for simultaneous over-range signal reconstruction and noise suppression. MoE-Gyro employs two experts: an Over‑Range Reconstruction Expert (ORE), featuring a Gaussian-Decay Attention mechanism for reconstructing saturated segments; and a Denoise Expert (DE), utilizing dual-branch complementary masking combined with FFT-guided augmentation for robust noise reduction. A lightweight gating module dynamically routes input segments to the appropriate expert. Furthermore, existing evaluation lack a comprehensive standard for assessing multi-dimensional signal enhancement. To bridge this gap, we introduce IMU Signal Enhancement Benchmark (ISEBench), an open-source benchmarking platform comprising the GyroPeak-100 dataset and a unified evaluation of IMU signal enhancement methods. We evaluate MoE-Gyro using our proposed ISEBench, demonstrating that our framework significantly extends the measurable range from ±450°/s to ±1500°/s, reduces Bias Instability by 98.4%, and achieves state-of-the-art performance, effectively addressing the long-standing trade-off in inertial sensing.Our code is available at:
https://github.com/2002-Pan/Moe-Gyro


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel self-supervised framework, MoE-Gyro, to simultaneously address over-range signal reconstruction and noise reduction in MEMS gyroscopes which commonly used in inertial navigation but limited by a trade-off between range and noise. The architecture uses a Mixture-of-Experts (MoE) approach with two specialized branches: an Over-Range Reconstruction Expert (ORE) enhanced by Gaussian-Decay Attention and physics-informed loss, and a Denoise Expert (DE) equipped with dual-branch masking and FFT-guided augmentation. A lightweight gating mechanism dynamically routes signal segments to the appropriate expert. The authors also introduce ISEBench, a new benchmark, and show that MoE-Gyro significantly outperforms existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The paper is motivated by a practical real-world challenge, i.e., the inherent limitations of current MEMS gyroscope systems, particularly the trade-off between extending measurement range and achieving effective noise reduction.\n2. Motivated by the observation that naively unifying denoising and over-range reconstruction leads to reconstruction loss dominating while the denoising loss is largely ignored, the paper proposes a unified self-supervised framework based on a Mixture-of-Experts (MoE) architecture. Both the Over-Range Reconstruction Expert (ORE) and the Denoise Expert (DE) are thoughtfully and appropriately designed for their respective tasks.\n3. The paper introduces a new benchmark, ISEBench, which comprises two publicly available datasets along with a newly collected dataset by the authors. The benchmark also features a set of thoughtfully designed evaluation metrics to enable comprehensive assessment of IMU signal enhancement methods.\n4. The evaluation is thorough, including both baseline comparisons and ablation studies, as well as a real-world experiment which the reviewer finds commendable and appreciates.\n\nWeaknesses\n\n1. While the design of the MoE architecture and routing logic is well motivated by the observation that naively combining denoising and over-range reconstruction leads to training imbalance, it remains unclear to the reviewer whether the strict either-or routing choice is the most appropriate or only viable solution. It is plausible that a signal segment could be both clipped and noisy. In fact, when a sensor experiences repeated clipping events, the resulting signal may exhibit increased noise or instability, suggesting that simultaneous application of both experts could be beneficial in some cases.\n2. Although the router is lightweight, it is based on hard-coded heuristics rather than a learned gating mechanism, and it does not provide any form of confidence estimation for its expert selection.\n3. The PINN constraint employed is a simplified energy-based loss that does not explicitly enforce the true physical dynamics of the system. This simplicity could be considered a strength, provided stronger empirical evidence (particularly in demonstrating its effectiveness across diverse applications and its impact on downstream task performance). The reviewer does not find the evaluation conclusive for this point.\n4. The evaluation effectively demonstrates the superiority of the proposed method in both denoising and reconstructing clipped peaks compared to baseline approaches. However, the practical implications for real-world applications remain unclear. Specifically, it is uncertain how much improvement in downstream performance (.e.g, in navigation, localization, or sensor fusion) denoising+reconstruction actually delivers. Evaluating the method within such end-to-end pipelines would strengthen the paper's claims and its practical utility.\n\nQUESTIONS:\nSee weaknesses 1-4.\n\nLIMITATIONS:\nSee weakness 1.\n\nThe reviewer believes that a discussion of alternative MoE architectures (such as multipath or non-exclusive routing) and their implications on training dynamics, as well as the potential modifications required in other components of the design, would strengthen the paper (interests a wider audience and followup work).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper aims to achieve long-range measurement and denoising performance at the same time. It adopts a Mixture of Expert (MoE) structure consisting of two experts, one for long range reconstruction and one for denoising. This method achieves state of the art (SoTA) performance on their proposed dataset called ISEBench.

STRENGTHS AND WEAKNESSES:
Strengths:
1 The paper is in general well written, with easy-to-follow language.
2 The first self-supervised method to tackle both over range measurement and denoising together.
3 Provide an open source IMU signal enhancement test bench.

Weaknesses:
1 The paper mentioned that the main weaknesses of traditional model-based methods is on the increased power draw and manufacturing costs. However, the proposed self-supervised architecture is also relatively complex and computationally heavy.
2 The training and test split across datasets isn’t clearly described in terms of temporal or domain diversity. Namely, there’s no cross-device or cross-domain generalization test.
3 The paper claims that it is doing label-free self supervised learning. However, when training the denoise expert (DE), it make use of synthetically generated noise, which may introduce implicit supervision from the noise assumptions.

QUESTIONS:
1 During training of denoise expert (DE), why can we assume that the randomly sample real recording clip s(t) is noise-free? If not, how can you make sure that DE cancels the noise in s(t)?

2 From Table 5, we can see that the contribution of PINN is relatively small. Is it because the ideal mass–spring dynamics may not be valid in practical cases?

LIMITATIONS:
See weaknesses.
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the fundamental trade-off between measurement range and noise performance in MEMS gyroscopes: a high measurement range often comes with high noise, while low noise usually limits the range. The authors propose MoE-Gyro, which simultaneously achieves over-range signal reconstruction and noise suppression without adding extra hardware complexity. They insightfully recognize that directly using a single network to learn both “reconstructing saturated signals” and “signal denoising” would encounter gradient imbalance problems, and thus cleverly incorporate task decoupling, designing loss functions and data augmentation strategies specifically for the two modules. Experimental results demonstrate the advantages of this method over existing technologies, notably achieving substantial sensor performance improvements under unsupervised conditions. Additionally, by releasing datasets and benchmark tools, the authors make a positive contribution to advancing research in this field.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper proposes a novel unified framework that simultaneously addresses the problems of over-range reconstruction and denoising for MEMS gyroscopes. The authors introduce a mixture-of-experts model into the domain of IMU signal enhancement. Each module is well designed to align with the task characteristics, effectively tackling these challenges.\n2. The overall architecture follows a decoupling philosophy, employing two dedicated models to separately handle the reconstruction and denoising tasks, thus avoiding mutual interference between the multitasks. The gated routing strategy is simple and efficient. The overall algorithm is solidly designed and works smoothly.\n3. On the ISEBench benchmark constructed by the authors, MoE-Gyro achieves state-of-the-art performance across the metrics, showing significant improvements over traditional filtering and existing deep learning methods.\n4. The authors have also released a high-quality dataset and evaluation toolkit: the IMU Signal Enhancement Benchmark, which I believe will benefit the community.\n5. Additionally, the paper is clearly written and provides detailed methodological descriptions, from which I could effectively gain information.\n\nWeaknesses:\n1. The paper targets applications such as unmanned aerial vehicles and robotics. Thus, algorithm efficiency should also be an important evaluation metric. However, the experiments seem to lack efficiency comparisons. Is it real-time on a PC (RTX4060)? And can it achieve real-time performance on embedded platforms for real-world applications?\n2. More of a question than a weakness: the paper has been thoroughly tested on ISEBench, which includes three datasets. I would like to know how MoE-Gyro generalizes to IMU devices beyond ISEBench. Does it require fine-tuning for specific IMUs?\n\nQUESTIONS:\nNA.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MoE-Gyro, a self-supervised Mixture of Experts (MoE) framework, for simultaneous over-range signal reconstruction and noise suppression in MEMS gyroscopes. It features an Over-Range Reconstruction Expert (ORE) utilizing Gaussian-Decay Attention and physics-informed constraints, and a Denoise Expert (DE) with dual-branch complementary masking and FFT-guided augmentation. The paper conducts experiments on three public datasets and introduced a benchmark named ISEBench.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe paper proposes a novel approach to IMU precise navigation by decoupling over-range signal reconstruction and noise elimination into two self-supervised frameworks. The method seems novel. Additionally, the experimental design and provided code are comprehensive. The writing is clear, with no major issues.\n\nWeaknesses:\n\n1. The problem studied might be important for MEMS Gyroscopes. But the topic may have limited attention and influence in the NeurIPS community. It would be more appropriate to submit the paper to a conference in more relevant field.\n\n2. Some details of the proposed system framework are not discussed in sufficient depth. The method may exhibit anomalous behavior when encountering complex or unexpected IMU signals, and the paper lacks analysis and experimental validation in this regard.\n\nQUESTIONS:\n1. While task decoupling indeed resolves the gradient conflict, it might also mean that subtle synergistic effects between certain tasks are overlooked. For example, it's worth considering whether very high noise levels might change the nature of an over-range signal, or if certain over-range signals are, in fact, noise-induced.\n\n2. It's questionable whether the \"a gate then applies a simple heuristic to route each segment to...\" approach can adequately handle complex noise scenarios. For instance, how would it perform when a signal segment simultaneously contains both over-range signals and noise?\n\n3. The paper's \"FFT-guided training augmentation\" method, designed to enhance data by combining noise PSD with weak motion signals, lacks a discussion of its limitations in synthesizing data that accurately reflects complex real-world noise conditions.\n\n4. The paper contains several typographical errors. For instance, on line 54, \"Over-Range Reconstruction Expert (DE)\" should be corrected to \"Denoise Expert (DE).\"\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-06"}