A Novel Design and Modelling Procedure for Geometric Optimization of MEMS Proof Mass for Low-Frequency Operation

Parkinson’s disease (PD), a progressive neurological disorder, poses a significant challenge in health care. Among the symptoms, tremors represent a primary manifestation, affecting millions of individuals worldwide. Early detection of PD tremors is vital for timely intervention and improved patient care. The tremors occur within the 3–7 Hz frequency range, making their accurate detection paramount. The paper focuses on designing and modelling of MEMS accelerometer to precisely detect low-frequency Parkinson’s tremors and proposes an index k/m for different structures of proof mass and supporting flexures to work at low frequencies and a limited bandwidth of < 10 Hz. The proposed design captures uniaxial acceleration for early detection of Parkinson’s disease incorporating four extended support beams, resulting in a resonant frequency of 6.4 Hz and a total surface displacement of 0.537 mm. Remarkably, this design exhibits a mechanical sensitivity of 13.5 µm/g and minimum cross-sensitivity of 0.007%, enabling the precise detection of accelerations as low as 0.04 g which is the acceleration of PD tremor. The proof mass is thoughtfully optimized, weighing 0.464 g, and features a spring constant of 0.207 N/m. The optimization is based on the novel concept of introducing a scaling factor k/m to tune the resonant frequency of an accelerometer. The integration of application-specific diagnostic MEMS accelerometers into healthcare presents a novel approach to PD management, addressing challenges associated with human-based assessments also enabling continuous measurement of tremors and symptom monitoring.