imu signal processing

Conventional calibration methods for sparse inertial motion capture rely on the restrictive static-assumption hypothesis—that both the coordinate-system drift (R_G'G) and sensor bias (R_BS) remain constant—requiring dedicated stationary calibration phases. This assumption severely limits practicality and long-term accuracy. Method: We propose the first online dynamic calibration framework that eliminates the need for static initialization. Our approach employs a Transformer-based end-to-end mapping model trained on synthetic paired data and incorporates an IMU-readings-diversity-driven adaptive triggering mechanism. Theoretically, we replace the static assumption with a relaxed “short-term slow-variation + motion diversity” dual hypothesis, enabling implicit, calibration-free dynamic estimation. Contribution/Results: The method achieves real-time estimation of R_G'G and R_BS within multi-second motion windows, significantly improving long-duration capture accuracy. Source code and dataset are publicly released.

High-precision online estimation algorithms for robotics are highly sensitive to sensor timestamp accuracy; however, existing synchronization solutions struggle to simultaneously achieve real-time operation, low cost, and high temporal precision. To address this, we propose a real-time, trigger-based time synchronization system built on commodity hardware. Our approach employs a hardware-triggered mechanism to jointly schedule heterogeneous sensors operating at different frequencies, and integrates an enhanced clock synchronization protocol with nanosecond-resolution timestamping to ensure precise coordination between sensors and the onboard computer. Crucially, the system eliminates reliance on expensive dedicated timing hardware, thereby substantially mitigating the impact of timing errors on online estimation. Experimental evaluation on a physical robot platform demonstrates sub-microsecond synchronization accuracy, along with significant improvements in both estimation robustness and real-time performance.

Motion artifacts severely degrade EEG signal-to-noise ratio, and conventional unimodal denoising methods (e.g., ASR, ICA) fail to effectively model the complex coupling between motion dynamics and neural activity. Method: We propose an IMU-enhanced multimodal denoising framework that integrates inertial measurement unit (IMU) motion signals—introduced for the first time into EEG denoising—with a fine-tuned LaBraM large model (9.2M parameters) and a correlation-aware attention mechanism to achieve cross-modal temporal alignment and channel-wise artifact identification. Contribution/Results: Our method achieves superior performance over ASR-ICA across multi-task and multi-timescale scenarios, requiring only 5.9 hours of training data (0.23% of baseline), demonstrating exceptional robustness and strong few-shot generalization. It establishes a novel paradigm for resource-efficient, real-world BCI deployment.

Existing approaches for implicit class recognition in streaming signals employ multiple heterogeneous-accuracy classifiers under fixed scheduling, yet fail to effectively fuse their outputs or account for temporal dynamics. Method: We propose a real-time state-space filtering model that treats multi-classifier probabilistic outputs as observations and models the true class label—including its temporal evolution—as a latent state, enabling online estimation via Bayesian recursion. The model jointly addresses classifier heterogeneity, temporal dependencies, and strict real-time computational constraints. Results: Evaluated on activity recognition using wearable IMU data, our method achieves significant accuracy improvements over baselines (+3.2%–5.8%), while maintaining inference latency consistently below 20 ms—demonstrating both high precision and strong real-time performance.

This study addresses the challenge posed by strong artifacts in transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) signals, which hinder their application in closed-loop neuromodulation and brain–computer interfaces. The work presents the first standardized benchmark dataset for TMS-EEG denoising and systematically evaluates two mainstream source-domain denoising pipelines under conditions lacking ground-truth physiological signals, assessing both artifact suppression efficacy and preservation of genuine TMS-evoked responses. The proposed preprocessing framework demonstrates robust performance, significantly enhancing signal quality and establishing a unified benchmark for algorithm development. This advancement facilitates more reliable use of TMS-EEG in neuroscience research, clinical settings, and embedded brain–computer interface systems.

During motion, inertial measurement units (IMUs) are subject to non-gravitational accelerations—such as centripetal and tangential components—which distort gravity direction estimation, particularly when the IMU is mounted far from the system’s center of rotation. This work proposes a self-calibration method that requires no external equipment and jointly estimates IMU intrinsic parameters (axis misalignment, bias, and scale factors) and extrinsic parameters (the displacement vector from the base frame to the sensor). Crucially, it explicitly models motion-induced acceleration using gyroscope data to compensate gravity observations within the attitude estimation pipeline. To the best of our knowledge, this is the first approach to integrate motion acceleration compensation directly into orientation estimation, enabling seamless compatibility with established filters such as Madgwick and Mahony. Experimental results demonstrate that the proposed framework significantly improves attitude accuracy under high-dynamic conditions and non-central IMU mounting configurations.

This work addresses the challenge of velocity estimation in robot-assisted integrated sensing and communication (ISAC) systems, where mobile robots can only opportunistically reuse irregular 5G/6G reference signals, limiting the performance of conventional methods. The study is the first to reveal the structural characteristics of the velocity spectrum under such irregular reference signaling, decomposing it into a periodic peak component and an amplitude-weighted component. Building on this insight, the authors propose a multi-periodogram-based velocity estimation algorithm that requires no dedicated sensing signals or modifications to the 3GPP protocol, ensuring full compatibility with existing standards. Experimental results demonstrate that, at a 10% miss-detection rate, the proposed method achieves a 3 dB SNR gain over traditional periodogram approaches and reduces the false alarm rate by 51%.