Toward experimental validation of a model for human sensorimotor learning and control in teleoperation

Roth, Eatai; Howell, Darrin; Beckwith, Cydney; Burden, Samuel A.

doi:10.1117/12.2263844

Cited by 3 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where e = r − y is tracking error. Using a combination of feedback and feedforward control to model human trajectorytracking has a long history in the field [1]- [3], [5]- [7], [9], [23]- [25], and is a well-known strategy to improve performance over error feedback alone [20,Ch 8]. We emphasize, however, that certain neurologic conditions like cerebellar ataxia could impair people's ability to perform feedforward control.…”

Section: B Combined Feedback and Feedforward Improves Predictionmentioning

confidence: 99%

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami

Peterson

Howell

et al. 2023

IEEE Trans. Cybern.

Self Cite

View full text Add to dashboard Cite

In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbancerejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise) explains observed performance improvements.

show abstract

Section: B Combined Feedback and Feedforward Improves Predictionmentioning

confidence: 99%

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami

Peterson

Howell

et al. 2023

IEEE Trans. Cybern.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This feature is one prominent difference between HCPS and traditional CPS and is the source of the large performance promotion [1]. In [102], Roth Eatai et al proposed an accurate model of the system's dynamics with AI control techniques in HCPS teleoperation. In [103], Yamagami M. used a combination of reactive (feedback) and predictive (feedforward) AI control algorithms in joint human-cyber-physical systems for manual trajectory tracking, the performance of which can be clearly improved.…”

Section: Aimentioning

confidence: 99%

Human-Cyber-Physical Systems for Energy Internet—A Review

et al. 2023

View full text Add to dashboard Cite

As the research of HCPS in EI is ongoing, an increasing number of problems should be addressed, such as the leading role or aid function definition of humans, the coordination of humans with cyber and physical equipment, the state awareness of information handling, etc. Therefore, an increasing number of advanced techniques should be considered and adopted, such as IOT, AI and big data, etc., the main target of which is to improve the timely, digitalized, informationized and intelligent characters of the related tasks. This paper first introduces the applications of CPS in every industry, especially in the power grid and EI (Energy Internet), the introduction of which can be classified as a framework design, support technologies and algorithms, system function, power ancillary service, Energy Internet realization, etc. Then, this paper summarizes the related technologies of Human-Cyber-Physical Systems, which mainly include the areas of system design, theoretical analysis, technique development and application forecasting, which emphasizes its applications in EI. For the development of HCPS, it uses the human-in-the-loop concept and digital transformation as the future research and development directions, the performance of which can be largely promoted through these means. With the aid of HCPS, the optimal system performance of EI can be achieved more robustly, which finally evolves into an integrated and comprehensive modern power grid.

show abstract

“…If a nonzero reference r ≠ 0 is known to the human, we assume that it evokes an additive feedforward F transformation of r , so that the overall human response can be written as where e = r − y is tracking error. Using a combination of feedback and feedforward control to model human trajectorytracking has a long history in the field [1]–[3], [5]–[7], [9], [23]–[25], and is a well-known strategy to improve performance over error feedback alone [20, Ch 8]. We emphasize, however, that certain neurologic conditions like cerebellar ataxia could impair people’s ability to perform feedforward control.…”

Section: Introductionmentioning

confidence: 99%

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami

Peterson

Howell

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a continuous trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward elements of subjects' controllers. We find that handedness does not affect the learned controller and that controllers transfer between hands. Observed improvements in time-domain tracking performance may be attributed to adaptation of feedback to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise)

show abstract

Toward experimental validation of a model for human sensorimotor learning and control in teleoperation

Cited by 3 publications

References 14 publications

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Human-Cyber-Physical Systems for Energy Internet—A Review

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Contact Info

Product

Resources

About