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Performance evaluation of raspberry Pi platform for bioimpedance analysis using least squares optimization

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Abstract

One method for analyzing bioimpedance measurements collected from a tissue applies equivalent electrical circuits to represent the data. This typically requires curve-fitting or optimization procedures to determine the circuit parameters of the selected equivalent circuit model that best fit the collected data. This work describes the performance (in terms of accuracy and execution time) of a nonlinear least squares optimization implementation using SciPy on a Raspberry Pi (RPi) for the analysis of simulated bioimpedance measurements, compared to MATLAB implementations of the same method. The SciPy/RPi implementation yielded similar accuracy to the MATLAB counterparts though the execution time ranged from 1.4 × to 2.1 × longer than the MATLAB environments and 10× greater than the same Python implementation running on a desktop or laptop environment. The performance of this optimization implementation on the RPi does support its suitability for further bioimpedance applications and its further use for applications requiring postprocessing in addition to data collection from connected sensors.

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  1. Department of Electrical and Computer Engineering, The University of Alabama, Box 870286, Tuscaloosa, AL, 35487, USA

    Todd J. Freeborn

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Freeborn, T.J. Performance evaluation of raspberry Pi platform for bioimpedance analysis using least squares optimization. Pers Ubiquit Comput 23, 279–285 (2019). https://doi.org/10.1007/s00779-019-01203-6

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  • DOI: https://doi.org/10.1007/s00779-019-01203-6

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