The invention provides a low-rank personalized
blood pressure estimation method, which comprises the following steps of: performing pre-training on a large-scale PPG-
blood pressure data pair on a
group level based on a UniTS model of a Transform
backbone network, and learning a mapping relation from a PPG
signal to
blood pressure; carrying out personalized
fine tuning on the pre-trained model by adopting a low-rank adaptive technology to realize model
adaptation under the condition of few samples; in the personalized
fine tuning process, a
pulse pressure segmented penalty
loss function is introduced, total training loss is formed by combining
mean square error loss, and prediction results of the systolic pressure and the diastolic pressure are restrained to conform to the physiological law; the problem of sampling
rate difference is solved by adopting a low-rank self-
adaptive method with a stable sampling rate, and
stable blood pressure estimation across equipment is realized; the invention aims to realize high-precision and cross-device robust blood pressure
estimation according with physiological rules under the condition of few-sample calibration by introducing a physiological constraint
loss function and sampling rate robust
adaptation mechanism only depending on PPG signals and through a framework combining group pre-training and low-rank personalized
fine tuning.