Memory Pattern Identification for Feedback Tracking Control in Human–Machine Systems. (March 2021)

• Record Type: Journal Article
• Authors: Martínez-García, Miguel; Zhang, Yu; Gordon, Timothy
• Is Part Of: Human factors. Volume 63:Number 2(2021)
• Page Start: 210
• Page End: 226
• Publication Date: 2021-03
• Subjects: human–machine interaction -- information processing -- memory -- autonomous agents -- adaptive automation -- fractional-order systems; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720819881008
• Languages: English
• ISSNs: 0018-7208

Abstract

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.