Search

Labanotation is a symbolic recording system for human movements, and also a powerful tool for protecting and spreading folk dances and other performing arts. State-of-the-art automatic Labanotation uses end-to-end methods with sequence-based skeleton representation, which cannot capture the relationship between joints and bones in the skeleton for accurate descriptions of continuous lower limb movements such as dance steps. In this paper, we propose a novel double-stream fusion method of directed graph neural networks (DGNN), combined with connectionist temporal classification (CTC), namely DFGNN-CTC, for sequential fine-grained motion recognition, such as the Labanotation generation of unsegmented dance movement. First, we extract double-stream directed graph feature, employing an orientation-normalized directed acyclic graph (ON-DAG) and an orientation-normalized temporal directed acyclic graph (ON-TDAG), to jointly model spatiotemporal properties of movement recorded in motion capture data. Then, we design a CTC-based fusion-pooling module to fuse the spatial and temporal streams encoded by two DGNNs. It concatenates and fuses the two streams to generate discriminative descriptions of each time step, and concentrates them to make per-time-step predictions of Laban gesture type, from which the CTC searches the optimal Laban symbol sequence, corresponding to elemental motions composing the movement. In this way, the new method enables much finer discrimination for similar Laban gestures with subtle differences in spatial and temporal properties through joint contextual spatiotemporal modeling so that it achieves much superior performance in continuous Labanotation generation to existing methods, which only have single-stream analysis either spatially or temporally. The experiments on two Labanotation-labelled motion capture datasets demonstrate the effectiveness of the components in the proposed method and its superiority comparing with the state-of-the-art methods, especially for lower limb movements. [ABSTRACT FROM AUTHOR]

Herein, ZnO nanoparticles (NPs) were synthesized using zinc acetate and an amino hyperbranched polymer. The methods of transmission electron microscope (TEM) and X-ray Diffraction (XRD) were applied to the characterization of ZnO NPs. Polypropylene (PP)/ZnO fiber was prepared using 1–5 wt% ZnO NPs via melt spinning. The ultraviolet light (UV)-degradation behavior, antibacterial activity and mechanical properties of PP fibers were characterized. The PP fiber filled with ZnO NPs presents better mechanical properties and the resistance to UV light degradation. For the best effect, the contents of ZnO NPs were set 4 wt% in PP fiber. FTIR analysis shows significant photo-degradation of PP induced by UV irradiation and a remarkable reduction in the UV degradation of the fiber filled with ZnO NPs. It is also observed that the ZnO NPs-filled fiber has good antibacterial actives against Escherichia coli and staphylococcus aureus. [ABSTRACT FROM AUTHOR]