The invention discloses a tumble
risk assessment method and
system based on individualized information, and relates to the technical field of
medical assessment and
artificial intelligence, and the method comprises the following steps: a
data acquisition step, a support period detection step, an individualized
feature fusion step, a
gait period segmentation step, a
feature extraction step, and a
time sequence coding step. A
risk assessment step; firstly, multi-channel
plantar pressure time sequence data of a subject and individualized information of the subject are obtained, and then through five-layer progressive
processing, the problems of supporting period self-adaptive detection,
gait cycle precise segmentation, multi-dimensional
feature extraction,
time sequence dynamic modeling and self-adaptive model training are solved respectively. And finally, accurate individual
fall risk assessment is realized. Thresholds are automatically adapted for subjects with different physiological features, the
gait deterioration trend caused by fatigue is captured through periodic-level double-
flow time sequence modeling, and self-adaptive personalized
feature learning is achieved through cascaded personalized
information layer-by-layer fusion.