Abstract
Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human–machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of this study is to identify the minimum sampling frequency needed for recording upper-limb FMG signals without sacrificing signal integrity. Twelve healthy volunteers participated in an experiment in which they were instructed to perform rapid hand actions with FMG signals being recorded from the wrist and the bulk region of the forearm. The FMG signals were sampled at 1 kHz with a 16-bit resolution data acquisition device. We downsampled the signals with frequencies ranging from 1 Hz to 500 Hz to examine the discrepancies between the original signals and the downsampled ones. Based on the results, we suggest that FMG signals from the forearm and wrist should be collected with minimum sampling frequencies of 54 Hz and 58 Hz for deciphering isometric actions, and 70 Hz and 84 Hz for deciphering dynamic actions. This fundamental work provides insight into minimum requirements for sampling FMG signals such that the data content of such signals is not compromised.
Highlights
Force myography (FMG) is an emerging technique to register muscle activity of a limb [1].This technique utilizes multiple force sensors to detect the pressure variation on the surface of the limb during movements [2]
We present an example of the FMG signals captured for different actions to provide a visual representation of the FMG patterns, followed by the analysis of the RMSE between the original signals pattern and the ones with different downsample frequencies
We investigate the power spectral density (PSD) of the FMG patterns to gain an understanding of the FMG power characteristics
Summary
Force myography (FMG) is an emerging technique to register muscle activity of a limb [1]. This technique utilizes multiple force sensors to detect the pressure variation on the surface of the limb during movements [2]. Such variations can be correlated to muscle contraction levels and, can be used to estimate different limb movements for physical activity monitoring or human–machine interface applications [1]. Its signal is less expensive to extract as it requires less complicated hardware for signal conditioning [5]
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