The average classification accuracy for each sensing modality at the neutral and unloaded position compared to varying grasped loads and limb positions.

<p>Averages were calculated by aggregating and then averaging all participant’s classification accuracies by sensing modality. For the neutral and unloaded position, EMG was found to be statistically different from EMG+FMG. For the varying weight and position condition, all sensing modalities...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Peyton R. Young (14021643) (author)
مؤلفون آخرون:	Kihun Hong (21072471) (author), Eden J. Winslow (21072474) (author), Giancarlo K. Sagastume (21072477) (author), Marcus A. Battraw (14021640) (author), Richard S. Whittle (21072480) (author), Jonathon S. Schofield (4062958) (author)
منشور في:	2025
الموضوعات:	Physiology Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified skin &# 8217 pattern recognition algorithms misclassification errors caused many modern technologies identify specific patterns bodied participants performed axial forces present augmented reality systems assistive mechatronic devices predict hand gestures 4 hand gestures muscle electrical activity 34 %) followed 3 sensing modalities statistically significant effects object loading effects highest classification accuracy grasped load combinations effective device control combination </ p grasped load hand gesture sensing modality muscle contraction device commands 27 %) gesture classification classification performance well established vital aspects underlying muscles technique suffers surface created research applications release task manipulated objects limb movements fused combination force myography daily life 84 %). 8 positions 27 able
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The average classification accuracy for each sensing modality at the neutral and unloaded position compared to varying grasped loads and limb positions.

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