The Markov model of a dynamic system based on experimental data for control problems of bionic prostheses

TL;DR

This paper develops a Markov chain model using experimental gait data to predict kinematics in bionic prostheses, aiding in understanding system dynamics and control.

Contribution

It introduces a novel Markov modeling approach based on delay space analysis for bionic prosthesis gait prediction.

Findings

Markov model effectively predicts gait kinematics.

Model helps estimate attractor dimensions and characteristic frequencies.

Experimental data supports model accuracy and applicability.

Abstract

This article is devoted to a prediction of gait kinematics based on the Markov chains in the delay space. We use hypercubic grid in the delay space of angles in the sagittal plane in the knee and hip joints to construct Markov states. Our experimental signal (obtained from the telemetry of the prosthesis) provides transition probabilities. The resulting Markov model seems to be helpful for estimation of attractor dimension, characteristic frequencies of the system, and so on.