Computational analysis and optimization of a MEMS-based piezoresistive accelerometer for head injuries monitoring.
Messina, Marco; Njuguna, James; Palas, Chrys
Professor James Njuguna email@example.com
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.
|Presentation Conference Type||Poster|
|Start Date||Oct 29, 2017|
|Publication Date||Nov 1, 2017|
|Institution 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.|
|Keywords||Piezo resistive accelerometer; Sensor design; Mechanical sensor optimisation; Biomechanical device; Head injuries monitoring; TBI|
|Related Public URLs||http://hdl.handle.net/10059/2707|
MESSINA 2017 Computational analysis and optimization (POSTER)
You might also like
Recent developments in graphene oxide/epoxy carbon fiber-reinforced composites.
Sustainable energy for emerging nations development: a case study on Togo renewable energy.