Human arm motion target prediction method based on GMM-Naive Bayes in contact human–robot collaboration

Purpose The purpose of this paper is to tackle the prediction accuracy degradation in physical contact human–robot collaborative motion due to frequent motion target changes and redundant historical data interference, by proposing a Gaussian mixture model (GMM)-Naive Bayes based motion prediction method. Design/methodology/approach The proposed method consists of a Gaussian mixture prior model and Bayesian online inference. In the prior modeling stage, a GMM is used to estimate the parameters of Gaussian components corresponding to each potential target point, thereby establishing a probabilistic mapping relationship between “target-observation.” During the online inference stage, the posterior probability of the current observation data belonging to each target is calculated using the Naive Bayes method, and the target point with the maximum posterior probability is output as the prediction result. To further enhance real-time performance and prediction accuracy, a velocity-based dynamic prediction horizon adjustment strategy is introduced, which adaptively adjusts the prediction horizon according to the collaborator’s real-time motion velocity. Finally, motion target prediction experiments in contact human–robot collaboration scenarios were conducted to validate the proposed method. Findings The experimental results demonstrate that the proposed prediction method exhibits strong performance in terms of changing-target adaptability, prediction accuracy and real-time responsiveness. It effectively accomplishes motion target prediction in collaborative scenarios. Originality/value A GMM-Naive Bayes hybrid prediction framework is proposed to address the issues of decreased prediction accuracy caused by target change and historical data redundancy. Furthermore, a velocity-based dynamic prediction horizon adjustment mechanism is introduced to enhance the real-time capability and accuracy of predictions in contact collaborative tasks.