Resistance strength training is a proven method to improve bone density and muscle strength. A solution capable of automatically detecting the resistance force level exerted by a user from a wrist-based device can offer great convenience to the trainee and hence facilitate a better training outcome. In this short communication, we present our investigation aimed at exploring if force myographic (FMG) signals recorded at the wrist can predict the relative resistance levels that are associated with different weights. Specifically, we investigated the Spearman's correlations between the wrist FMG signal features and the dumbbell weights during a bicep curl exercise. 10 volunteers were recruited to perform a total of 100 curl actions, which included both the hammer and regular curls while the wrist FMG signals were being recorded. Three sets of weights ranging from 0.2 lb to 8 lb were used. For the hammer curls, a median correlation coefficient of 0.92 with an interquartile range (IQR) of 0.03 was obtained. For the regular curls, a 0.94 median correlation with a 0.02 IQR was obtained. We also used the data from the first 36 curls to generate a classifier model and applied it onto the rest of the data. An averaged validation accuracy of 88% was obtained. The results of this study showed the potential use of wrist FMG signal to detect different levels of the load during exercises; such information could potentially be used as feedback in fitness, sports, and rehabilitation activities.
Keywords: FMG; Fitness; Force; Sports.
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