The effects of limb position and grasped load on hand gesture classification using electromyography, force myography, and their combination.

ABSTRACT: Hand gesture classification is crucial for the control of many modern technologies, ranging from virtual and augmented reality systems to assistive mechatronic devices. A prominent control technique employs surface electromyography (EMG) and pattern recognition algorithms to identify specific patterns in muscle electrical activity and translate these to device commands. While being well established in consumer, clinical, and research applications, this technique suffers from misclassification errors caused by limb movements and the weight of manipulated objects, both vital aspects of how we use our hands in daily life. An emerging alternative control technique is force myography (FMG) which uses pattern recognition algorithms to predict hand gestures from the axial forces present at the sk

SUBMITTER: Young PR 

PROVIDER: S-EPMC11984976 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature