WSEAS Transactions on Computer Research
Print ISSN: 1991-8755, E-ISSN: 2415-1521
Volume 13, 2025
Biasing Voltage Optimization in MEMS Wireless Sensors for Enhanced Multiple Sclerosis Tremor Detection
Authors: , ,
Abstract: The objective of this work is to present the complete design and simulation of a microelectromechanical system (MEMS) based differential capacitive accelerometer developed to detect tremor signals in patients with Multiple Sclerosis (MS). The primary challenge is to address the difficulties of sensing at low frequencies (below 10 Hz) associated with tremors in multiple sclerosis (MS). The design mainly focuses on the 3.5 to 7.5 Hz band of frequencies. The methods used in the design of the accelerometer consider these multiple attributes to provide optimization with regard to resonance frequency, mechanical stability, and sensitivity. The design is validated by performing finite element analysis (FEA) in COMSOL Multiphysics software. The mechanical properties of the accelerometer are characterized by the development of analytical models to compute resonance frequency and effective spring constant. The FEA results show that the system has a resonance frequency of 5.5 Hz, and the maximum displacement is around 1.77 μm under an acceleration of 0.04 g taking into account bias voltage at operation 10 V in air as external condition for this study; hence mechanical sensitivity was found to be about 44.25 μm. The accelerometer exhibits a considerable dynamic range: from static forces up to near resonant frequencies with very high level sensitivities; linearity also outperforms previous research studies. The feasibility of using a MEMS differential capacitive accelerometer in the effective and accurate evaluation/quantification of tremor signals from MS patients is demonstrated as an emerging technology. Specific documentation and analyzed tremors could have a dramatic impact on many areas of disease identification/management especially in the area of multiple sclerosis.
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Keywords: Accelerometer, Diagnosis, Biasing, Multiple Sclerosis, Tremor Detection, Differential capacitive sensing, Proof mass dynamics, Analytical validation, Dynamic range assessment
Pages: 225-235
DOI: 10.37394/232018.2025.13.21