BP neural network-based analysis of the applicability of NMF in side-step cutting

• Published in: Heliyon Vol. 10; no. 8; p. e29673
• Main Authors: Pan, Zhengye, Liu, Lushuai, Li, Xingman, Ma, Yunchao
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 30.04.2024; Elsevier
• Subjects: BP neural networks; Factorisation method; Fractal analysis; ide-step cutting; Muscle synergy; Factorisation method; BP neural networks; Muscle synergy; ide-step cutting; Fractal analysis
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e29673

Although the spatio-temporal structure of muscle activation in cutting have been studied extensively, including time-varying motor primitives and time-invariant motor modules under various conditions, the factorisation methods suitable for cutting are unclear, and the extent to which each factorisation method loses information about movement during dimensionality reduction is uncertain. To clarify the extent to which NMF, PCA and ICA retain information about movement when downscaling, and to explore the factorisation method suitable for cutting. The kinematic data during cutting was captured with a Vicon motion capture system, from which the kinematic features of the pelvic centre of mass were calculated. NMF, PCA and ICA were used to obtain muscle synergies based on sEMG of the cutting at different angles, respectively. A back propagation neural network was constructed using temporal component of synergy as input and the kinematics data of pelvic as output. Calculation of the Hurst index using fractal analysis based on the temporal component of muscle synergy. The quality of sEMG reconstruction is significantly higher with ICA (P < 0.01) than with NMF and PCA for the cutting. The NMF reconstruction has a high degree of preservation of movement, whereas the ICA loses movement information about the most of the swing phase, and the PCA loses information related to the change of direction. Hurst index less than 0.5 for all three angles of cutting. NMF is suitable for extracting muscle synergies in all directions of cutting. Information related to movement may be lost when using PCA and ICA to extract the synergy of cutting. The high individual variability of muscle synergy in cutting may be responsible for the loss of movement information in muscle synergy.