Abstract
Quantitatively measuring the difficulty of a manipulation task in human-in-the-loop control systems is ill-defined. Currently, systems are typically evaluated through task-specific performance measures and post-experiment user surveys; however, these methods do not capture the real-time experience of human users. In this study, we propose to analyze and predict the difficulty of a bivariate pointing task, with a haptic device interface, using human-centric measurement data in terms of cognition, physical effort, and motion kinematics. Noninvasive sensors were used to record the multimodal response of human user for 14 subjects performing the task. A data-driven approach for predicting task difficulty was implemented based on several task-independent metrics. We compare four possible models for predicting task difficulty to evaluated the roles of the various types of metrics, including: (I) a movement time model, (II) a fusion model using both physiological and kinematic metrics, (III) a model only with kinematic metrics, and (IV) a model only with physiological metrics. The results show significant correlation between task difficulty and the user sensorimotor response. The fusion model, integrating user physiology and motion kinematics, provided the best estimate of task difficulty (R2 = 0.927), followed by a model using only kinematic metrics (R2 = 0.921). Both models were better predictors of task difficulty than the movement time model (R2 = 0.847), derived from Fitt’s law, a well studied difficulty model for human psychomotor control.
Highlights
Human-in-the-loop robot-assisted systems have substantial freedom in design that enable operators to interact with complex physical systems in a variety of ways
We propose that the difficulty of a reaching task can be objectively quantified from multiple measures of user sensorimotor response, including user physiology and motion kinematic metrics found in both the user and task workspaces
Findings of statistical results during typical reaching tasks confirm the correlations between user sensorimotor response metrics and task difficulty, p < 0.001
Summary
Human-in-the-loop robot-assisted systems have substantial freedom in design that enable operators to interact with complex physical systems in a variety of ways. It is a common practice to evaluate teleoperation difficulty and performance via quantitative tool-based metrics and performance measures, such as completion time and manipulation accuracy [17, 18] These metrics are often coupled with checklists or userresponse surveys, such as NASA Task Load Index (NASA-TLX) [19], where operators report the ease-of-use and acceptance of the system by rating it on predefined scales. These ratings have been widely used to assess task workload and perceived performance in a variety of domains, including robotic surgery [20,21,22,23], and teleoperation [24]. These evaluations are limited because: (1) task-specific performance measures do not always correspond to perceived user acceptance and performance [6, 25,26,27,28]; (2) user ratings can only be measured in a post-hoc manner, and do not capture the real-time response of the human user
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