公开(公告)号：US12036021B2

公开(公告)日：2024-07-16

申请号：US18511919

申请日：2023-11-16

Applicant: CENTRAL CHINA NORMAL UNIVERSITY

Inventor： Liang Zhao ,  Sannyuya Liu ,  Zongkai Yang ,  Xiaoliang Zhu ,  Jianwen Sun ,  Qing Li ,  Zhicheng Dai

IPC: A61B8/14 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/1171 ,  A61B5/16 ,  G06N3/0464 ,  G06N3/08 ,  G06V10/30 ,  G06V40/16 ,  A61B5/024 ,  A61B5/08

CPC classification number: A61B5/16 ,  A61B5/0205 ,  A61B5/1176 ,  A61B5/725 ,  A61B5/726 ,  A61B5/7264 ,  G06N3/0464 ,  G06N3/08 ,  G06V10/30 ,  G06V40/161 ,  A61B5/02427 ,  A61B5/0816 ,  G06V2201/03

Abstract: The present disclosure provides a non-contact fatigue detection system and method based on rPPG. The system and method adopt multi-thread synchronous communication for real-time acquisition and processing of rPPG signal, enabling fatigue status detection. In this setup, the first thread handles real-time rPPG data capture, storage and concatenation, while the second thread conducts real-time analysis and fatigue detection of rPPG data. Through a combination of skin detection and LUV color space conversion, rPPG raw signal extraction is achieved, effectively eliminating interference from internal and external environmental facial noise; Subsequently, an adaptive multi-stage filtering process enhances the signal-to-noise ratio, and a multi-dimensional fusion CNN model ensures accurate detection of respiration and heart rate. The final step involves multi-channel data fusion of respiration and heartbeats, succeeding in not only learning person-independent features for fatigue detection but also detecting early fatigue with very high accuracy.

公开(公告)号：US11568012B2

公开(公告)日：2023-01-31

申请号：US16605806

申请日：2018-05-08

Applicant: CENTRAL CHINA NORMAL UNIVERSITY

Inventor： Zongkai Yang ,  Sannyuya Liu ,  Dongbo Zhou ,  Jianwen Sun ,  Jiangbo Shu ,  Hao Li

IPC: G06F16/00 ,  G06F16/9537 ,  G06F16/9535 ,  G09B5/12

Abstract: The disclosure discloses a method for analyzing educational big data on the basis of maps. The method includes acquiring educational resource data and storing the educational resource data into databases according to certain data structures; constructing theme map layers for each analysis theme, classifying and indexing data according to the analysis themes, and superimposing the theme map layers onto base maps to form data maps; analyzing data of the theme map layers according to the analysis themes and acquiring theme analysis results; extracting the data of the multiple theme map layers in target regions, fusing the data and acquiring region analysis results; acquiring learning preference of users; combining the learning preference of the users according to content of user requests and searching the region analysis results in response to the user requests. The disclosure further discloses a system for analyzing the educational big data on the basis of the maps.