Human Motion Posture Recognition Based on Optimized Hidden Markov Model
REN Yuanbo
CAO Yao
SUN Anping
Abstract：To improve the accuracy with which human motion postures are recognized,this paper proposes a method based on an optimized Hidden Markov Model(HMM).Standards for recognizing these postures are set based on the positional relationship between joints and bones in different postures.Optical imaging is used to capture human motion.The initial images are pre-processed using steps such as grayscale conversion and noise reduction.Motion targets are then detected and tracked,and posture features are extracted using the optimized HMM.Human motion posture recognition is then achieved based on state probability and feature matching results.Performance tests conclude that the optimized design method achieves an average recognition error rate of 0.13％.making it superior to traditional methods.
Keywords：optimized HMMhuman motionmotion postureposture recognition method
Publication Date：2025-12-30
Online Publishing Date：2026-01-17(First online date of this platform, not the publication date of the document)
Pages：7( 50-56 )