Energy metabolism assessment method based on wearable sensing information fusion and system thereof

Abstract

The invention discloses an energy metabolism assessment method based on wearable sensing information fusion and a system thereof. The method comprises the following steps: acquiring electrocardiosignal information reflecting the physiological state of a human body, acceleration signal information reflecting the exercise intensity level of the human body and physical characteristic information of a human body structure; inputting the electrocardiosignal information into a first convolutional neural network to extract electrocardiosignal features, inputting the acceleration signal information into a second convolutional neural network to extract acceleration features, wherein the first convolutional neural network and the second convolutional neural network are obtained through training, and the first convolutional neural network and the second convolutional neural network are both multi-branch structures so as to extract multi-scale features; and fusing the electrocardio characteristics, the acceleration characteristics and the physical characteristic information of the human body structure, and predicting corresponding energy metabolism information based on the fused characteristics. By means of the method, accurate calculation of energy consumption in the movement process can be achieved, and the generalization ability is high.

Description

technical field [0001] The present invention relates to the technical field of information processing, and more specifically, to an energy metabolism evaluation method and system based on fusion of wearable sensor information. Background technique [0002] With the development of social economy and the improvement of people's living standards, various metabolic diseases (such as fatty liver, diabetes, hyperlipidemia, etc.) caused by the imbalance of human energy metabolism have become high-incidence diseases. Scientific and reasonable control of dietary energy intake and exercise energy consumption is an effective means for the prevention and rehabilitation of metabolic diseases, and it is also an important part of individual active health management. The human body is a complex time-varying and non-linear system. The energy consumption level of exercise is affected by many factors, such as exercise intensity, environmental temperature and humidity, individual physical state...

AI Technical Summary

Problems solved by technology

However, at present, there is still a lack of effective means for using wearable devices to accurately evaluate energy consumption during exercise.

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