Open AccessData Analysis of MYO Signals during Upper Limb Movements of Enhanced Exoskeleton
Author(s) -
Lei Sun,
Jian Gao,
Honglei An,
Hongxu Ma
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1576/1/012041
Subject(s) - exoskeleton , principal component analysis , computer science , joint (building) , dimension (graph theory) , artificial intelligence , motion capture , computer vision , motion (physics) , control theory (sociology) , simulation , engineering , control (management) , mathematics , structural engineering , pure mathematics
Equipped with enhanced exoskeleton in the individual combat system can effectively enhance the soldiers’long-term load-bearing capacity and enhance the combat effectiveness of the army. In the exoskeleton control system, accurate recognition of human movement intentions plays an important role as upper-level control. This paper uses MYO armband as a sEMG sensor to collect 8 channels sEMG signals. Neural network method is applied to estimate the joint angles during arm movement. Motion capture system is used to verify the estimation accuracy. PCA (Principle Component Analysis) method is performed on the 8 channels data collected by MYO and processed dimensions were selected by experiments to make the estimation accuracy the highest. The results show that when the principal component dimension is selected as 8, which makes the estimation accuracy of the joint angles the highest.