Memory Pattern Identification for Feedback Tracking Control in Human-Machine Systems

Objective: The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor. Background: Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a one-dimensional tracking task. Specifically, data recorded from human subjects controlling dynamic systems with different fractional order were investigated. Method: A finite impulse response (FIR) controller was fitted to the data, and pattern analysis of the fitted parameters was performed. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closed-loop was conducted. Results: It is shown that the FIR model can be used to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less than or equal to 1. Conclusion: For systems of different fractional order, the proposed control scheme-based on an FIR model-can effectively characterize the linear properties of manual control in humans. Application: This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are the following: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.

Automated summary

This study aimed to identify the characteristics of memory patterns with respect to visual input perceived by the human operator during a manual control task, using FIR controller fitting and pattern analysis. The results showed that the FIR model can effectively identify and represent procedural memory patterns in human manual control tasks, with stability for systems with fractional order less than or equal to 1.