Abstract
Due to their small size, low weight, low cost and low energy consumption, MEMS accelerometers have achieved great commercial success in recent decades. The aim of this research work is to identify a MEMS accelerometer structure for human body dynamics measurements. Photogrammetry was used in order to measure possible maximum accelerations of human body parts and the bandwidth of the digital acceleration signal. As the primary structure the capacitive accelerometer configuration is chosen in such a way that sensing part measures on all three axes as it is 3D accelerometer and sensitivity on each axis is equal. Hill climbing optimization was used to find the structure parameters. Proof-mass displacements were simulated for all the acceleration range that was given by the optimization problem constraints. The final model was constructed in Comsol Multiphysics. Eigenfrequencies were calculated and model's response was found, when vibration stand displacement data was fed into the model as the base excitation law. Model output comparison with experimental data was conducted for all excitation frequencies used during the experiments.
Highlights
A new technology usually begins with experimentation
The aim of this research work is to investigate the applicability of optimization techniques for identification of MEMS accelerometer structures for human body measurements, when any information available from the accelerometers’ producer is limited
±16 g acceleration data should be chosen. It is common for the MEMS accelerometer to have its sensing part fit into area of ~1 mm2 [10]
Summary
A new technology usually begins with experimentation. Anything that is ever built must be designed first. Optimization of the design of such a system requires a thorough understanding of the coupling effects of their working environments, their physical structural parameters, their electronic construction, and their fabrication processes. With growing concern about obesity in older adults and disabled people, this paper deals primarily with the estimation of energy expenditure in the human body. It supports the localization of footstep sources, extraction of statistical parameters on daily living patterns, and identification of pathological gait patterns. The aim of this research work is to investigate the applicability of optimization techniques for identification of MEMS accelerometer structures for human body measurements, when any information available from the accelerometers’ producer is limited.
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