Evaluation system of students' classroom learning status based on wearable physiological signal monitoring

Abstract

The invention discloses a student classroom learning state evaluation system based on wearable physiological signal monitoring, which includes a wearable multi-physiological signal acquisition module, a physiological signal transmission and synchronization module, a server-side data analysis module and a learning state evaluation module. The wearable multi-physiological signal acquisition module collects students' EEG signals, ECG signals and pulse wave signals; the physiological signal transmission and synchronization module synchronizes the time stamp of each physiological signal, and transmits the data to the server-side data analysis module through wireless means; The server-side data analysis module performs preprocessing and feature extraction on each physiological signal, and inputs the feature vector into the classification model to obtain the student learning status index, and uploads it to the learning status evaluation module; the learning status evaluation module uses the data uploaded by the server-side data analysis module Shown to educators. Compared with the existing evaluation methods, the present invention is more intelligent, objective and accurate, and is conducive to promoting the improvement of teaching level and teaching quality.

Description

technical field [0001] The invention belongs to the field of educational informatization, and in particular relates to a system for evaluating students' classroom learning status. Background technique [0002] The effective evaluation of students' classroom learning status is an important means to promote students' progress and improve the quality of classroom teaching. The rapid development of signal processing, big data, artificial intelligence and other technologies will become endogenous variables that support and lead the modernization of education and promote the upgrading of education informatization. [0003] With the development of wearable device technology and signal processing algorithms, physiological signals are widely used to monitor mental states such as emotion, attention, stress, and fatigue. The multi-physiological signals collected based on wearable devices can comprehensively and accurately reflect the state of the measured object and improve the overal...

AI Technical Summary

Problems solved by technology

Teachers make adjustments and improvements after observing abnormalities. Teachers often cannot take into account the learning status of all students, and the evaluation methods are highly subjective.

Although the method of video surveillance is simple, it has great defects, including low robustness, large external influence factors, inability to recognize false facial expressions, and high requirements for real-time computing. More importantly, the collection of facial videos involves violations of student privacy. problem, use on campus has been strictly limited and managed

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