The invention discloses a
blood pressure monitoring method based on a long short-
term memory network model. According to the method, the problem of how to provide a
blood pressure monitoring method which can be deeply fused with a safety helmet, adapts to the
working environment and is high in accuracy is solved, and efficient and accurate
blood pressure monitoring is achieved by combining data collection at a specific part with
deep learning. The method specifically comprises the following steps: acquiring
pulse wave signals of a testee, wherein the acquisition parts comprise intersection positions of arteries on two side surfaces of the head and mandible,
temporal regions on two sides of the head and the middle part of a
frontal region; filtering and
feature extraction are carried out on original signals, data are integrated to construct a reference
data set, the
data set is trained by using a long-short-
term memory network model to obtain an optimal model, and finally blood
pressure analysis is carried out on the collected data by using the optimal model. By optimizing the acquisition strategy and using the long-short-
term memory network model, the physiological information of the testee is acquired and monitored on the head, and the
system is suitable for scenes such as intelligent safety helmets and the like.