# HEROS-GAN: Honed-Energy Regularized and Optimal Supervised GAN for Enhancing Accuracy and Range of Low-Cost Accelerometers

arXiv: https://arxiv.org/abs/2502.18064 · HTML: https://mlsanity.com/paper/2502.18064/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding](https://mlsanity.com/paper/2312.14074/) (2023)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation](https://mlsanity.com/paper/2106.06908/) (2021)
- [EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction](https://mlsanity.com/paper/2401.12561/) (2024)
- [GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting](https://mlsanity.com/paper/2407.01301/) (2024)
- [Data-Driven Denoising of Stationary Accelerometer Signals](https://mlsanity.com/paper/2206.05937/) (2022)
- [Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement](https://mlsanity.com/paper/2401.05416/) (2023)

## Built on by (most cited first, 3 of 3)

- [Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?](https://mlsanity.com/paper/2503.12496/) (2025)
- [MoE-Gyro: Self-Supervised Over-Range Reconstruction and Denoising for MEMS Gyroscopes](https://mlsanity.com/paper/2506.06318/) (2025)
- [Suite-IN++: A FlexiWear BodyNet Integrating Global and Local Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2504.00438/) (2025)

## Abstract (arXiv)

Low-cost accelerometers play a crucial role in modern society due to their advantages of small size, ease of integration, wearability, and mass production, making them widely applicable in automotive systems, aerospace, and wearable technology. However, this widely used sensor suffers from severe accuracy and range limitations. To this end, we propose a honed-energy regularized and optimal supervised GAN (HEROS-GAN), which transforms low-cost sensor signals into high-cost equivalents, thereby overcoming the precision and range limitations of low-cost accelerometers. Due to the lack of frame-level paired low-cost and high-cost signals for training, we propose an Optimal Transport Supervision (OTS), which leverages optimal transport theory to explore potential consistency between unpaired data, thereby maximizing supervisory information. Moreover, we propose a Modulated Laplace Energy (MLE), which injects appropriate energy into the generator to encourage it to break range limitations, enhance local changes, and enrich signal details. Given the absence of a dedicated dataset, we specifically establish a Low-cost Accelerometer Signal Enhancement Dataset (LASED) containing tens of thousands of samples, which is the first dataset serving to improve the accuracy and range of accelerometers and is released in Github. Experimental results demonstrate that a GAN combined with either OTS or MLE alone can surpass the previous signal enhancement SOTA methods by an order of magnitude. Integrating both OTS and MLE, the HEROS-GAN achieves remarkable results, which doubles the accelerometer range while reducing signal noise by two orders of magnitude, establishing a benchmark in the accelerometer signal processing.