A multi-sensor architecture combining human pose estimation and real-time location systems for workflow monitoring on hybrid operating suites

Rodrigues, Vinicius Facco; Antunes, Rodolfo Stoffel; Seewald, Lucas Adams; Bazo, Rodrigo; Reis, Eduardo Souza dos; Santos, Uelison J.L. dos; Righi, Rodrigo da Rosa; S., Luiz G. da; Costa, Cristiano André da; Bertollo, Felipe L.; Maier, Andreas; Eskofier, Bjoern M.; Horz, Tim; Pfister, Marcus; Fahrig, Rebecca

doi:10.1016/j.future.2022.05.006

Cited by 8 publications

(1 citation statement)

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“…CrossViT ( Chen, Fan & Panda, 2021 ) uses two different branches to process image tags of different sizes in image classification, and then uses cross attention to fuse class tags. Cross attention has also been used in multi-modal tasks ( Lu, Zhou & Ye, 2019 ; Rodrigues et al, 2022 ). In this article, we apply the cross-attention for the interaction between different multi-resolution markers to improve the identifiability of low-resolution markers.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the mixed teaching of college physical education based on the health big data and blockchain technology

Liu

2023

PeerJ Computer Science

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In the era of health big data, with the continuous development of information technology, students’ physical health management also relies more on various information technologies. Blockchain, as an emerging technology in recent years, has the characteristics of high efficiency and intelligence. College physical education is an important part of college students’ health big data. Unlike cultural classes, physical education with its rich movements and activities, leaves teachers no time to monitor students’ real classroom performance. Therefore, we propose a human pose estimation method based on cross-attention-based Transformer multi-scale representation learning to monitor students’ class concentration. Firstly, the feature maps with different resolution are obtained by deep convolutional network and these feature maps are transformed into multi-scale visual markers. Secondly, we propose a cross-attention module with the multi-scales. The module reduces the redundancy of key point markers and the number of cross fusion operations through multiple interactions between feature markers with different resolutions and the strategy of moving key points for key point markers. Finally, the cross-attention fusion module extracts feature information of different scales from feature tags to form key tags. We can confirm the performance of the cross-attention module and the fusion module by the experimental results conducting on MSCOCO datasets, which can effectively promote the Transformer encoder to learn the association relationship between key points. Compared with the completive TokenPose method, our method can reduce the computational cost by 11.8% without reducing the performance.

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Section: Related Workmentioning

confidence: 99%