Physiological status monitoring equipment based on electroencephalogram signals and bioimpedance data

Abstract

The invention relates to emotion and physiological status monitoring equipment based on electroencephalogram signals and bioimpedance data. The equipment is characterized in that portable acquisitionof electroencephalogram and bioimpedance data of a human body is realized by using embedded equipment, and the acquired data are uploaded to an Internet of Things platform for management. The human health condition can be accurately reflected; and the real-time monitoring and management of the health data of the data are realized through the cloud platform. Different from the traditional equipmentwhich is expensive in use, low in portability and low in real-time performance, the provided equipment is capable of realizing convenient collection and analysis of the data.

Description

technical field [0001] The invention relates to an intelligent medical system of the Internet of Things, in particular to a physiological condition monitoring device based on electroencephalogram signals and bio-impedance data, which belongs to the technical field of health management. Background technique [0002] As one of the most delicate and complex organs in the human body, the brain contains rich physiological and pathological information. The information transmitted by the EEG signal can be identified through the identification of the characteristic signal, and the method of combining the fundamental component and the harmonic component can be used to extract the best band in the EEG signal, and then through the Kolmogorov-Smirnov Check out the EEG channels. Finally, this classifier is trained and tested through a validation process, and then used to distinguish between different types of emotions. [0003] Bioelectrical impedance measurement, or impedance technolo...

AI Technical Summary

Problems solved by technology

[0005] However, although traditional EEG and bioimpedance measurement equipment are of high quality and reliable in medical diagnosis, they are more complicated to use and need to be used in fixed locations (such as hospitals, etc.), and most of the time, after data collection, Still need to wait for analysis results

The cost of use is relatively expensive, the portability is low, and the real-time performance is low. In addition, since the data is only collected for a period of time in most cases, it is difficult to make judgments and predictions on the long-term condition of the human body.

And in terms of emotion classification, although many related studies at home and abroad have proved the feasibility of emotion recognition through EEG, there has been no model with high accuracy.

Therefore, at present, it is difficult to realize real-time monitoring and management of the user's health status, let alone the use of EEG and bioimpedance, which are highly accurate but rely on large-scale equipment to collect data

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