Computational analysis and optimization of a MEMS-based piezoresistive accelerometer for head injuries monitoring.

Messina, Marco; Njuguna, James; Palas, Chrys

Abstract

This work focuses on the design improvement of a tri-axial piezoresistive accelerometer specifically designed for head injuries monitoring where medium-G impacts are common, for example in sports such as racing cars. The device requires the highest sensitivity achievable with a single proof mass approach, and a very low error as the accuracy for these types of applications is paramount. The optimization method differs from previous work as it is based on the progressive increment of the sensor mass moment of inertia (MMI) in all three axes. The work numerically demonstrates that an increment of MMI determines an increment of device sensitivity with a simultaneous reduction of cross-talk in the particular axis under study. The final device shows a sensitivity increase of about 80% in the Z-axis and a reduction of cross-talk of 18% respect to state-of-art sensors available in the literature. Sensor design, modelling and optimization are presented, concluding the work with results, discussion and conclusion.

Citation

MESSINA, M., NJUGUNA, J. and PALAS, C. 2017. Computational analysis and optimization of a MEMS-based piezoresistive accelerometer for head injuries monitoring. Presented at the 2017 IEEE sensors conference, 29 October - 1 November 2017, Glasgow, UK.

• Presentation Conference Type: Poster
• Conference Name: 2017 IEEE sensors conference
• Start Date: Oct 29, 2017
• End Date: Nov 1, 2017
• Deposit Date: Feb 1, 2018
• Publicly Available Date: Feb 1, 2018
• Peer Reviewed: Peer Reviewed
• Keywords: Piezo resistive accelerometer; Sensor design; Mechanical sensor optimisation; Biomechanical device; Head injuries monitoring; TBI
• Public URL: http://hdl.handle.net/10059/2708
• Related Public URLs: http://hdl.handle.net/10059/2707
• Contract Date: Feb 1, 2018