Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators

Hoffmann, Johannes; Roldan-Vasco, Sebastian; Krüger, Karolin; Niekiel, Florian; Hansen, Clint; Maetzler, Walter; Orozco-Arroyave, Juan Rafael; Schmidt, Gerhard

doi:10.3390/s23073594

Cited by 6 publications

(3 citation statements)

References 70 publications

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“…While SQUIDs are still the sensing device of choice for many applications due to their extremely high sensitivity, ME sensors were recently shown to be promising in highly versatile and novel applications ranging from automatic real-time magnetic localization of an ultrasound probe [ 12 ], to magnetic detection of positions and orientations of deep brain stimulation (DBS) electrodes in patients [ 13 ], to magnetic motion analysis for swallowing detection in individuals suffering from dysphagia [ 14 ]. Crucially, measurements in the operation room or even wearable solutions could be possible with ME sensors for applications such as the detection and orientation of DBS electrodes or swallowing detection, which are currently not feasible with SQUID systems.…”

Section: Introductionmentioning

confidence: 99%

A Combined Magnetoelectric Sensor Array and MRI-Based Human Head Model for Biomagnetic FEM Simulation and Sensor Crosstalk Analysis

Özden,

Barbieri,

Gerken

2024

Sensors

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Magnetoelectric (ME) magnetic field sensors are novel sensing devices of great interest in the field of biomagnetic measurements. We investigate the influence of magnetic crosstalk and the linearity of the response of ME sensors in different array and excitation configurations. To achieve this aim, we introduce a combined multiscale 3D finite-element method (FEM) model consisting of an array of 15 ME sensors and an MRI-based human head model with three approximated compartments of biological tissues for skin, skull, and white matter. A linearized material model at the small-signal working point is assumed. We apply homogeneous magnetic fields and perform inhomogeneous magnetic field excitation for the ME sensors by placing an electric point dipole source inside the head. Our findings indicate significant magnetic crosstalk between adjacent sensors leading down to a 15.6% lower magnetic response at a close distance of 5 mm and an increasing sensor response with diminishing crosstalk effects at increasing distances up to 5 cm. The outermost sensors in the array exhibit significantly less crosstalk than the sensors located in the center of the array, and the vertically adjacent sensors exhibit a stronger crosstalk effect than the horizontally adjacent ones. Furthermore, we calculate the ratio between the electric and magnetic sensor responses as the sensitivity value and find near-constant sensitivities for each sensor, confirming a linear relationship despite magnetic crosstalk and the potential to simulate excitation sources and sensor responses independently.

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Section: Introductionmentioning

confidence: 99%

A Combined Magnetoelectric Sensor Array and MRI-Based Human Head Model for Biomagnetic FEM Simulation and Sensor Crosstalk Analysis

Özden,

Barbieri,

Gerken

2024

Sensors

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“…Furthermore, the subconscious movement of To resolve these issues, a new system needs to be developed which is independent of EMG and accurately detects speech activity. Inertial Motor Unit signals have been widely used for activity recognition for a variety of purposes thus it can also be used for developing an alternate method for speech activity detection [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

A Fusion of EMG and IMU for an Augmentative Speech Detection and Recognition System

Shafiq,

Waris,

Iqbal

et al. 2024

IEEE Access

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Subvocal voice recognition is important in rehabilitation because it allows people with speech issues to communicate in an alternate manner. Capturing and interpreting subtle muscular movements during subvocalization can improve the rehabilitation process, allowing patients to have more autonomy and a higher quality of life. The scarcity of research on subvocal voice recognition, as well as the use of IMU (accelerometer and gyroscope) signals for speech activity detection, are significant challenges. The study focuses on the amalgamation of spectrotemporal feature extraction techniques for classification and IMU data for speech activity detection. The study carries out the classification of an isolated word vocabulary sets of 70 words corresponding to 6 subjects and 96 isolated words for subjects. A feature extraction algorithm was used utilizing Variational Mode Decomposition. The results demonstrated maximum accuracy rates of 98.6% for the 70-word set and 92% for the 96-word set. Automatic speech activity detection (ADSAS) was developed using only IMU data and was independent of the EMG data. The comparison was carried out by using a previously proposed EMG activity detection method based on Teager Kaiser Operator and morphological operations, The IMU based detection technique achieved a lowest error of 0.09 compared to 0.21 for the EMG based detection technique. The results proved that IMU based methods perform better than EMG based methods for detection of speech activity. Statistical analysis was carried out using paired t-test and resulted in a significant difference between the two techniques (p-value<0.05).

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“…The combination of magnetostrictive and piezoelectric Micromachines 2023, 14, 1878 2 of 21 layers in magnetoelectric composites enables simultaneous magnetic and charge ordering capabilities. This feature unlocks a wide range of applications including energy harvesting [11], mechanical actuators [12], magnetic sensors [13], electric current sensors [14], magnetic particle imaging [15], magnetocardiography [16], and swallowing detection [17].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Magnetoelectric Microresonator Using Numerical Method and Simulated Annealing Algorithm

Sadeghi,

Bazrafkan,

Rutner

et al. 2023

Micromachines

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A comprehensive understanding of the linear/nonlinear dynamic behavior of wireless microresonators is essential for micro-electromechanical systems (MEMS) design optimization. This study investigates the dynamic behaviour of a magnetoelectric (ME) microresonator, using a finite element method (FEM) and machine learning algorithm. First, the linear/nonlinear behaviour of a fabricated thin-film ME microactuator is assessed in both the time domain and frequency spectrum. Next, a data driven system identification (DDSI) procedure and simulated annealing (SA) method are implemented to reconstruct differential equations from measured datasets. The Duffing equation is employed to replicate the dynamic behavior of the ME microactuator. The Duffing coefficients such as mass, stiffness, damping, force amplitude, and excitation frequency are considered as input parameters. Meanwhile, the microactuator displacement is taken as the output parameter, which is measured experimentally via a laser Doppler vibrometer (LDV) device. To determine the optimal range and step size for input parameters, the sensitivity analysis is conducted using Latin hypercube sampling (LHS). The peak index matching (PIM) and correlation coefficient (CC) are considered assessment criteria for the objective function. The data-driven developed models are subsequently employed to reconstruct/predict mode shapes and the vibration amplitude over the time domain. The effect of driving signal nonlinearity and total harmonic distortion (THD) is explored experimentally under resonance and sub-resonance conditions. The vibration measurements reveal that as excitation levels increase, hysteresis variations become more noticeable, which may result in a higher prediction error in the Duffing array model. The verification test indicates that the first bending mode reconstructs reasonably with a prediction accuracy of about 92 percent. This proof-of-concept study demonstrates that the simulated annealing approach is a promising tool for modeling the dynamic behavior of MEMS systems, making it a strong candidate for real-world applications.

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Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators

Cited by 6 publications

References 70 publications

A Combined Magnetoelectric Sensor Array and MRI-Based Human Head Model for Biomagnetic FEM Simulation and Sensor Crosstalk Analysis

A Combined Magnetoelectric Sensor Array and MRI-Based Human Head Model for Biomagnetic FEM Simulation and Sensor Crosstalk Analysis

A Fusion of EMG and IMU for an Augmentative Speech Detection and Recognition System

Modeling of Magnetoelectric Microresonator Using Numerical Method and Simulated Annealing Algorithm

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