Overview of the 3 tests conducted in the experiment.

<p>Test 1 was split into 2 analyses, as shown by the dashed line. The first analysis examined how accurately each of the sensing modalities could classify combinations of limb position, grasped load, and hand gesture. The second analysis compared classifying hand gestures in the neutral and un...

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Bibliographic Details
Main Author:	Peyton R. Young (14021643) (author)
Other Authors:	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)
Published:	2025
Subjects:	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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Overview of the 3 tests conducted in the experiment.

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