Constant position of position 1 with varying grasped loads.

<p>The gesture classification accuracies from training and testing at various grasped loads under a constant position of position 1.</p> <p>(TIF)</p>

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items