Damping Estimation and Analysis for High Performance Inertial MEMS for Early Detection of Neurological Disorders During Pregnancy

Abstract

High performance inertial MEMS require appropriate damping estimation and control. In any MEMS device, the ratio of the surface area to volume is large for which we need to do dynamic performance analysis. Inertial MEMS sensors find its suitability in varied applications and bio-motion sensors are no exception. One of such low frequency applications is the early detection of neurological disorders, targeted specially for pregnant women suffering from tremors, epilepsy and seizures. It becomes very important to capture the most feeble tremor (0 g) to a sudden jerk (±6 g) where the person usually falls. During Pregnancy, most of the drugs for curing neuro-disorders are not suitable to be consumed. Therefore, high performance micro-sensors are required for early detection of tremors (2−12 Hz) and seizure (0.5−29 Hz). An attempt has been made to design a micro sensor with adequate damping estimation by comparing it with the existing models. The proposed MEMS sensor simulated for the target application has an air gap of 23.5 μ which produces around 0.7 damping ratio. Finally, time dependent analysis is done by comparing both with intended damping and very low damping value. We obtain smooth characteristics for the intended damping and overriding oscillations appear for very low damping. Hence, the proposed inertial MEMS sensor has a dynamic range of ±6 g, frequency range of 0−25 Hz and damping ratio of 0.7. Therefore, the proposed microsensor makes it suitable for early detection of neurological disorders especially during pregnancy addressing maternal health care.