Abstract
The human-in-the-loop technology requires studies on sensory-motor characteristics of each hand for an effective human–robot collaboration. This study aims to investigate the differences in visuomotor control between the dominant (DH) and non-dominant hands in tracking a target in the three-dimensional space. We compared the circular tracking performances of the hands on the frontal plane of the virtual reality space in terms of radial position error (ΔR), phase error (Δθ), acceleration error (Δa), and dimensionless squared jerk (DSJ) at four different speeds for 30 subjects. ΔR and Δθ significantly differed at relatively high speeds (ΔR: 0.5 Hz; Δθ: 0.5, 0.75 Hz), with maximum values of ≤1% compared to the target trajectory radius. DSJ significantly differed only at low speeds (0.125, 0.25 Hz), whereas Δa significantly differed at all speeds. In summary, the feedback-control mechanism of the DH has a wider range of speed control capability and is efficient according to an energy saving model. The central nervous system (CNS) uses different models for the two hands, which react dissimilarly. Despite the precise control of the DH, both hands exhibited dependences on limb kinematic properties at high speeds (0.75 Hz). Thus, the CNS uses a different strategy according to the model for optimal results.
Highlights
Human–robot collaboration has been emerging with the recent advances in smart manufacturing [1,2,3,4]
A robot controls an end effector with a position control mechanism based on absolute coordinates, whereas a human controls limbs with a predictive-control mechanism and feedback-control mechanism based on body coordinates [5,6,7,8]
We investigated differences between visuomotor control outcomes and mechanisms of the dominant hand (DH) and non-dominant hand (NDH) according to the target periodicity
Summary
Human–robot collaboration has been emerging with the recent advances in smart manufacturing [1,2,3,4]. The human–robot collaboration is challenging owing to the different mechanisms used by humans and current robots. Humans use a combination of feedback and predictive-control mechanisms. The dominant control mechanism depends on the periodicity of the target [9,10,11,12,13,14,15]. The difference in the mechanism leads to the human control of the upper limb with low precision and accuracy compared to the robot. To achieve an effective collaboration in smart manufacturing, the characteristics of human control mechanisms need to be analyzed for robots’ planning, identification, and classification of the human movement (how people react to spatio-temporal changes of the target)
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