Huimin Shen

Real-Time Orientation-Invariant Magnetic Localization and Sensor Calibration Based on Closed-Form Models

This paper presents a real-time orientation-invariant magnetic localization method based on a closed-form analytical inverse model. This method requires only a pair of inexpensive 3-axis magnetic sensors to acquire data for solving the unique solution of the inverse problem. Taking advantage of gravity, a permanent-magnet-upright device is designed to maintain a constant magnetic moment direction. An effective calibration method for the sensor orientation is proposed based on orthogonality to assure high performance. Then, a prototype magnetic localization system is developed. An in-depth experimental analysis is presented demonstrating the design feasibility of the tracking system in real time with a cycle-time less than 0.1 ms, and root-mean-square translation and rotation errors of 40 μm for a 40 mm × 40 mm × 50 mm volume and 0.16° for [-10°, 10°], respectively.

Multi-motion robots control based on bioelectric signals from single-channel dry electrode

This article presents a multi-motion control system to help severe disabled people operate an auxiliary appliance using neck-up bioelectric signals measured by a single-channel dry electrode on the forehead. The single-channel dry-electrode multi-motion control system exhibits several practical advantages over its conventional counterparts that use multi-channel wet-electrodes; among the challenges is an effective technique to extract bioelectric features for reliable implementation of multi degrees-of-freedom motion control. Using both time and frequency characteristics of the single-channel dry-electrode measurements, motion commands are derived from multiple feature signals associated with concentration demands and different eye-blink actions for use in a two-level control strategy that has been developed to control predefined multi degrees-of-freedom motion trajectories. Test paradigms were designed to pre-calibrate the users’ feature signals to statistically account for individual variances. Experimental trials were then carried out on able-bodied and disabled volunteers to validate the universal applicability of the algorithms. The classification success rates for two different eye-blink feature signals were approximately 95% with an average time of 2.4 s for executing a concentration feature signal. The single-channel dry-electrode–based technique has been validated on a 6-degree-of-freedom robot arm demonstrating its significant potentials to help patients suffering severe motor dysfunctions operate a multi-motion auxiliary appliance in everyday living where the ease of use is a priority.

A novel method for locating PM marker based on magnetic field reconstruction

PM marker along with an electrical system sensing its magnetic field is commonly used to determine the parts locations in industry. This paper proposes a novel method combining computation and experiment to improve the localization accuracy of the traditional PM marker approach. Based on the current-free property of the external space of the PM marker (modeled as an ideal dipole), the method computes (reconstructs) the magnetic field in the space by solving a Laplaces equation with measured boundary conditions. Thus, the employ of reconstructed magnetic field with better quality improves the location estimation with higher accuracy. In the paper, the reconstruction and corresponding estimation algorithms are presented firstly. Its effect is then validated on a simulation model by a comparison with traditional PM marker approach, which shows that 80% error is eliminated at most.

Integrated Giant Magnetoresistance Technology for Approachable Weak Biomagnetic Signal Detections

With the extensive applications of biomagnetic signals derived from active biological tissue in both clinical diagnoses and human-computer-interaction, there is an increasing need for approachable weak biomagnetic sensing technology. The inherent merits of giant magnetoresistance (GMR) and its high integration with multiple technologies makes it possible to detect weak biomagnetic signals with micron-sized, non-cooled and low-cost sensors, considering that the magnetic field intensity attenuates rapidly with distance. This paper focuses on the state-of-art in integrated GMR technology for approachable biomagnetic sensing from the perspective of discipline fusion between them. The progress in integrated GMR to overcome the challenges in weak biomagnetic signal detection towards high resolution portable applications is addressed. The various strategies for 1/f noise reduction and sensitivity enhancement in integrated GMR technology for sub-pT biomagnetic signal recording are discussed. In this paper, we review the developments of integrated GMR technology for in vivo/vitro biomagnetic source imaging and demonstrate how integrated GMR can be utilized for biomagnetic field detection. Since the field sensitivity of integrated GMR technology is being pushed to fT/Hz0.5 with the focused efforts, it is believed that the potential of integrated GMR technology will make it preferred choice in weak biomagnetic signal detection in the future.

Effects of reconstructed magnetic field from sparse noisy boundary measurements on localization of active neural source

Localization of active neural source (ANS) from measurements on head surface is vital in magnetoencephalography. As neuron-generated magnetic fields are extremely weak, significant uncertainties caused by stochastic measurement interference complicate its localization. This paper presents a novel computational method based on reconstructed magnetic field from sparse noisy measurements for enhanced ANS localization by suppressing effects of unrelated noise. In this approach, the magnetic flux density (MFD) in the nearby current-free space outside the head is reconstructed from measurements through formulating the infinite series solution of the Laplace’s equation, where boundary condition (BC) integrals over the entire measurements provide “smooth” reconstructed MFD with the decrease in unrelated noise. Using a gradient-based method, reconstructed MFDs with good fidelity are selected for enhanced ANS localization. The reconstruction model, spatial interpolation of BC, parametric equivalent current dipole-based inverse estimation algorithm using reconstruction, and gradient-based selection are detailed and validated. The influences of various source depths and measurement signal-to-noise ratio levels on the estimated ANS location are analyzed numerically and compared with a traditional method (where measurements are directly used), and it was demonstrated that gradient-selected high-fidelity reconstructed data can effectively improve the accuracy of ANS localization.

Simulation Study of Dipole Localization in MEG-Based BCI Using Magnetic Field Reconstruction

Brain-computer interface BCI based on magnetoencephalography MEG provides intentional signals with high spatial resolution for communications of patients suffering severe motor dysfunctions. However, the incomplete measurements of the extremely weak magnetic field outside head bring significant uncertainties to active neural source ANS localization. This paper presents a novel computational method to improve the ANS localization accuracy. Based on the fact that the external magnetic field obeys the Maxwell equations quasistatic, the field can be reconstructed via solving a Laplaces equation with measured boundary conditions. By numerically solving Laplaces equation with finite element method FEM, the signal-to-noise ratio of the reconstruction can be improved with high-order interference eliminated. The inverse estimation model, the reconstructions, and reconstruction selection are presented, and validated via simulation. Results show that about half of the dipole localization error is eliminated compared with method utilizing only measurements.

Magnetic field estimation in measurement dead domain for dry calibration of electromagnetic flowmeter

Advances in computing technology enable dry calibration of large-diameter electromagnetic (EM) flowmeters at low cost, which has been recognized as an effective alternative to traditional flow rigs. Dry calibration requiring no actual liquid in the measuring pipe utilizes the magnetic field distribution reconstructed from measured boundary conditions to determine the sensitivity of the EM flowmeter. However, because sensors have finite sizes, and the fact that inner linings of the measuring pipe deform due to mechanical stresses, a measurement dead domain (MDD) exists between the measured boundary surface and the pipe wall. As the MDD is often close to the magnetic exciting unit, neglecting it results in significant errors in dry calibration. This paper offers a practical method combining iterative optimization and reconstruction to estimate the magnetic field in the MDD from the field data on the measured boundary surface. The method has been validated on an off-the-shelf industrial EM flowmeter by comparing the estimated field in the MDD with experimental measurements. It has been demonstrated that accurately accounting for the immeasurable field in the MDD eliminates more than two-thirds of the dry calibration errors. The estimation method illustrated here can also be extended to measure other physical fields which obey similar governing equations.