Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

ABSTRACT: Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement

SUBMITTER: Reichert C 

PROVIDER: S-EPMC9412700 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature