Wearable device based on neural network adaptive health monitoring

Abstract

The invention discloses a wearable device based on neural network adaptive health monitoring. The wearable device comprises an exercise state solving module, an exercise link judgment module, a dimension reduction solving module, a physiological parameter setting module and a physiological parameter collection module; the exercise state solving module collects acceleration data output by an acceleration sensor, high-frequency noise is filtered through wavelet transformation, and the exercise state of a user is divided; the exercise link judgment module subdivides the exercise state in combination with an angular velocity to obtain an exercise subdivision link; and according to the dimension reduction solving module, an optimized SVM is used to perform further fine division on partial pathological features hierarchically in combination with data at different levels. According to the wearable device, different feature dimension reduction samples are integrated in an analysis link, the amount of exercise is greatly reduced, correctness is guaranteed by emphatically calculating a verification link, the medical reference value of obtained user physiological parameters is increased, and power consumption standby capability is improved under the same condition.

Description

technical field [0001] The invention relates to the field of smart wear, in particular to a wearable device based on neural network adaptive health monitoring. Background technique [0002] As one of the most threatening diseases to human health, cardiovascular disease is caused by tissues that transport blood in the human body and the circulatory system including the heart, arteries, and veins. The human pulse contains a wealth of physiological and pathological information related to the cardiovascular system, and its waveform amplitude, period, and shape can be used as an important basis for judging the health of the human cardiovascular system. [0003] For a long time, people obtain electrocardiogram by electrocardiograph machine and distinguish. With the development of wearable technology and telemedicine, people are increasingly dissatisfied with the use of large-scale equipment such as electrocardiogram machines to obtain their own health information under the condit...

AI Technical Summary

Problems solved by technology

[0011] 1. Accuracy problem: Existing wearable devices do not consider that different physiological parameters should be collected in different exercise states, so as to obtain physiological parameters with medical reference value

[0014] 2. Power problem: Due to the constraints of volume, weight and system performance, power capacity is one of the most important factors restricting the wide use

[0015] In summary, when the existing technology collects physiological parameters, it does not take into account the user's motion state; at the same time, the existing technology can only collect simple motion state such as step counting, and does not collect the differences in motion links and subtle features of motion.

In addition, when the existing technology collects the detailed features of the motion state, the amount of calculation is large and the power consumption is high

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