Abstract
Classic studies in human sensorimotor control use simplified tasks to uncover fundamental control strategies employed by the nervous system. Such simple tasks are critical for isolating specific features of motor, sensory, or cognitive processes, and for inferring causality between these features and observed behavioral changes. However, it remains unclear how these theories translate to complex sensorimotor tasks or to natural behaviors. Part of the difficulty in performing such experiments has been the lack of appropriate tools for measuring complex motor skills in real-world contexts. Robot-assisted surgery (RAS) provides an opportunity to overcome these challenges by enabling unobtrusive measurements of user behavior. In addition, a continuum of tasks with varying complexity—from simple tasks such as those in classic studies to highly complex tasks such as a surgical procedure—can be studied using RAS platforms. Finally, RAS includes a diverse participant population of inexperienced users all the way to expert surgeons. In this perspective, we illustrate how the characteristics of RAS systems make them compelling platforms to extend many theories in human neuroscience, as well as, to develop new theories altogether.
Highlights
IntroductionHumans generate motor commands, sense their actions and the environment, and estimate their internal state when trying to achieve a desired task (Figure 1A)
The surgeon’s motor system generates commands which cause hand movements. Her hands interact with master manipulators which serve as the input to the teleoperation system that controls the instruments or an endoscope held by a robotic arm
We emphasize that Robot-assisted surgery (RAS) research platforms can be used for human neuroscience research during tasks of various complexities, excluding only live human surgery. The realism of these systems depends on the fidelity of the teleoperation controllers, but the behavior of the system may be configured to mimic the clinical system or entirely different experimental designs
Summary
Humans generate motor commands, sense their actions and the environment, and estimate their internal state when trying to achieve a desired task (Figure 1A) Understanding such human behavior is essential to combating disease and injury and may be beneficial for designing systems with physical human-robot interactions. Despite revealing many characteristics of the human sensorimotor system, these studies remain distant from representing natural behaviors during complex tasks (Wolpert et al, 2011) The movements in these studies are simple or abstract ( they may be building blocks of more complex movements Mussa-Ivaldi et al, 1994; Mussa-Ivaldi and Bizzi, 2000; Tresch and Jarc, 2009). Human neuroscience research would benefit from an experimental platform that: (1) spans basic to complex tasks; (2) extends to real-world applications; and (3) includes users of different levels of expertize. RAS meets the three main objectives to serve as a useful experimental platform—it encompasses many levels of task complexity, system realism, and user expertize
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