Impedance-Based Gaussian Processes for Modeling Human Motor Behavior in Physical and Non-Physical Interaction

• Published in: IEEE transactions on biomedical engineering 2019-09, Vol.66 (9), p.2499-2511
• Main Authors: Medina, Jose R., Borner, Hendrik, Endo, Satoshi, Hirche, Sandra
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Arm - physiology; Bayesian analysis; Bayesian modeling; Computer Simulation; Correlation; Electric Impedance; Exploitation; Female; Gaussian process; Gaussian processes; Human behavior; human motor control; human motor intention; human robot interaction; Humans; Impedance; Intention; kernel structure; Learning algorithms; Machine learning; Male; Mechanical impedance; Modelling; Models, Biological; Motor drives; Motor skill learning; Motor task performance; Movement - physiology; Normal Distribution; Parameterization; Performance prediction; Psychomotor Performance - physiology; Robots; Stochastic processes; Stochasticity; Task analysis; Trajectories; Trajectory; Young Adult
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2018.2890710

Objective: Modeling of human motor intention plays an essential role in predictively controlling a robotic system in human-robot interaction tasks. In most machine learning techniques, human motor behavior is modeled as a generic stochastic process. However, the integration of a priori knowledge about underlying system structures can provide insights on otherwise unobservable intrinsic states that yield the superior prediction performance and increased generalization capabilities. Methods: We present a novel method for modeling human motor behavior that explicitly includes a neuroscientifically supported model of human motor control, in which the dynamics of the human arm are modeled by a mechanical impedance that tracks a latent desired trajectory. We adopt a Bayesian setting by defining Gaussian process (GP) priors for the impedance elements and the latent desired trajectory. This enables exploitation of a priori human arm impedance knowledge for regression of interaction forces through inference of a latent desired human trajectory. Results: The method is validated using simulated data, with particular focus on effects of GP prior parameterization and intention estimation capabilities. The superior prediction performance is shown with respect to a naive GP prior. An experiment with human participants evaluates generalization capabilities and effects of training data sparsity. Conclusion: We derive the correlations of an impedance-based GP model of human motor behavior that exploits a priori knowledge. Significance: The model effectively predicts interaction forces by inferring a latent desired human trajectory in previously observed as well as unobserved regions of the input space.