Construction method of state space EMG model based on forward dynamics and Hill model

Abstract

The present invention relates to a method for constructing a state space EMG model based on forward dynamics and Hill model. Firstly, the EMG signals of relevant muscles in a joint in a state of continuous motion are collected, and band-pass filtering is performed on them, and then the EMG signals are collected by Nerve activation calculates relevant muscle activation and substitutes it into the Hill muscle model, then simplifies the Hill muscle model and replaces parameters, and then combines the substituted simplified model with joint forward dynamics to obtain a discrete-time state Finally, through the feature extraction of the root mean square and wavelet coefficients of the collected relevant EMG signals, the measurement equation is composed as the state feedback, and the final state space muscle is obtained by fitting the equation with the joint motion. electric model. Compared with traditional angle estimation methods, this model has obvious improvements in prediction accuracy and real-time performance.

Description

technical field [0001] The invention belongs to the field of myoelectric signal processing, and relates to the construction of a state space myoelectric model, in particular to a state space myoelectric model based on forward dynamics and a Hill muscle model. Background technique [0002] Surface Electromyography (sEMG) is a popular input signal source for human-computer interaction in cutting-edge science and technology. Action potential sequence, which has obvious characteristics, rich information, and simple and non-invasive acquisition, has become a hot research field in human-computer interaction technology. The research on surface EMG mainly focuses on the two processes of feature extraction and pattern recognition. The corresponding research results are relatively mature and can identify multiple discrete action categories. However, in the fields of rehabilitation medical robots and other fields, it is more often necessary to predict the continuous motion variables ...

AI Technical Summary

Problems solved by technology

The model involves multiple physiological parameters, which is difficult to calculate and limited in practical application

HMM is the most commonly used muscle model for estimating continuous joint motion, but there are two problems: one is that HMM involves many complex physiological parameters that are difficult to identify, and the calculation burden is relatively large; the other is that HMM can directly calculate joint torque from sEMG signals, But if you need continuous joint motion estimation, you also need to calculate the motion state from the torque

This often introduces cumulative errors, reducing forecast accuracy

Images