Invariant properties of upper limb movement trajectory control

OBJECTIVE: Human movements often follow smooth trajectories predicted by the minimum-jerk trajectory (MJT) model, suggesting that the motor system optimizes motion smoothness during planning. While MJT-like behavior has been extensively studied in reaching movements, it remains unclear whether these trajectory properties are preserved across different control modalities, visual feedback conditions, and limb status.
Approach: We examined the effects of control modality (reaching versus muscle-based control), visual feedback (present versus absent), and limb status (intact limb versus limb difference) on movement trajectory generation. Thirteen participants, including three individuals with upper-limb difference, performed virtual cursor movements to varying target distances. Peak velocity, movement time, position error, and adherence to MJT-like trajectory structure were analyzed.
Main results: Across all experimental conditions, movements exhibited bell-shaped velocity profiles and amplitude-dependent scaling of peak velocity and movement time. These features persisted in the absence of visual feedback and in participants with limb difference. Position error increased when visual feedback was removed, but the overall smoothness and structure of the trajectories remained consistent with MJT predictions.
Significance: These findings suggest that MJT-like movement structure is robust across variations in control modality, visual feedback availability, and limb status. The results support the idea that smooth movement planning reflects a generalized property of motor control and provide insight for the development of biologically inspired assistive and human-machine interface systems.

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Publication Details

Journal
Journal of Neural Engineering
Published
2026-07-16
DOI
https://doi.org/10.1088/1741-2552/ae8bd8
Primary Topic
Motor Control and Adaptation
Type
article
Field-Weighted Citation Impact
0.00

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article

Invariant properties of upper limb movement trajectory control

Wilsaan M. Joiner, Shriniwas Patwardhan, Siddhartha Sikdar, Jonathon S. Schofield
Journal of Neural Engineering
Motor Control and Adaptation
article

Invariant properties of upper limb movement trajectory control

Wilsaan M. Joiner, Shriniwas Patwardhan, Siddhartha Sikdar, Jonathon S. Schofield
article en

Abstract

OBJECTIVE: Human movements often follow smooth trajectories predicted by the minimum-jerk trajectory (MJT) model, suggesting that the motor system optimizes motion smoothness during planning. While MJT-like behavior has been extensively studied in reaching movements, it remains unclear whether these trajectory properties are preserved across different control modalities, visual feedback conditions, and limb status.
Approach: We examined the effects of control modality (reaching versus muscle-based control), visual feedback (present versus absent), and limb status (intact limb versus limb difference) on movement trajectory generation. Thirteen participants, including three individuals with upper-limb difference, performed virtual cursor movements to varying target distances. Peak velocity, movement time, position error, and adherence to MJT-like trajectory structure were analyzed.
Main results: Across all experimental conditions, movements exhibited bell-shaped velocity profiles and amplitude-dependent scaling of peak velocity and movement time. These features persisted in the absence of visual feedback and in participants with limb difference. Position error increased when visual feedback was removed, but the overall smoothness and structure of the trajectories remained consistent with MJT predictions.
Significance: These findings suggest that MJT-like movement structure is robust across variations in control modality, visual feedback availability, and limb status. The results support the idea that smooth movement planning reflects a generalized property of motor control and provide insight for the development of biologically inspired assistive and human-machine interface systems.

Journal of Neural Engineering
George Mason University (US), University of California, Davis (US)
National Science Foundation, U.S. Department of Defense, National Institutes of Health
Sustainable cities and communities
Openalex Percentile: Top 7%
Motor Control and Adaptation
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