LSTM network-based treatment and convalesce combined health monitoring method

Abstract

The invention discloses an LSTM network-based treatment and convalesce combined health monitoring method. The method comprises the following steps that by means of health detection equipment, intelligent home and an HIS system, comprehensive health information data of people aged 60 years and above is collected and obtained and then stored in a cloud database; collected and obtained data waveformsare subjected to denoising processing by adopting an adaptive notch filter of LMS; processed and classified data serves as a training sample, a pervasive health model of people aged 60 years and above is generated by adopting LSTM network training and learning; for an existing pervasive health model, after personal data is obtained, continuous training and personalized adjustment are conducted while the model is identified, and abnormal early warning of the corresponding situation is given. Accordingly, a traditional statistical model algorithm and a neural network are organically combined, comprehensive health monitoring is performed on people aged 60 years or above, efficient and accurate personalized health prediction and early warning are achieved, and a solid foundation is laid for building intelligent health pension service.

Description

technical field [0001] The invention belongs to the technical fields of neural network, deep learning, Internet of Things, smart elderly care, etc., and specifically relates to a health monitoring method based on an LSTM network in combination with medical care and elderly care. Background technique [0002] In the process of smart elderly care development, in view of complex and scattered elderly care service resources, separation of medical care and nursing care, and the inability to provide personalized, active, safe, accurate, and continuous full-life cycle services, etc., research on smart health that combines cross-border multi-service medical care and nursing care The pension model is gradually improving. Through the integration of wearable, portable, non-contact health monitoring and smart home devices, establish an all-round health monitoring Internet of Things for chronic disease management, first aid for elderly sudden diseases, disease early warning, and critical...

AI Technical Summary

Problems solved by technology

The analysis of long-period dynamic time series data has long-term regularity characteristics and requires a certain degree of prediction. Common data mining methods cannot meet the requirements, such as cluster analysis. Therefore, a deformation of RNN in neural networks is chosen. LSTM (Long Short-Term Memory) network, that is, long short-term memory network

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