Abstract The present invention discloses a method for recognizing human knee motion postures based on an extreme learning machine (ELM). The method comprises: collecting output data of a human body under different postures with inertial sensors; segmenting sample data based on a sliding window mechanism and performing feature extraction on the output data in each sliding window; performing dimension reduction and normalization on the output data by using a principal component analysis method to obtain the sample data; constructing an ELM network model and training the ELM network model by utilizing the sample data to obtain a final recognition model; and performing online recognition on real-time measurement data collected by the inertial sensors by utilizing the final recognition model to obtain recognition results. The present invention can accurately and rapidly recognize human motion postures by utilizing the generalization performance and the characteristic of fast learning of the ELM. Drawings of Description Collect output data of a human body under different postures with S1 inertial sensors Segment sample data based on a sliding window mechanism and S2 perform feature extraction on the output data in each sliding window Perform dimension reduction and normalization on output data by using S3 a principal component analysis method to obtain the sample data Construct an ELM network model and train the ELM network model by S4 utilizing the sample data to obtain a final recognition model Perform online recognition on real-time measurement data collected by S5 the inertial sensors by utilizing the final recognition model to obtain recognition results Fig. 1
METHOD FOR RECOGNIZING HUMAN KNEE MOTION POSTURES BASED ON EXTREME LEARNING MACHINE