When humans proactively manipulate objects, the applied fingertip forces primarily depend on feedforward, predictive neural control mechanisms that depend on internal representations of the physical properties of the objects. Here we investigate whether predictions of object properties also control fingertip forces that subjects generate reactively. We analyzed fingertip forces reactively supporting grasp stability in a restraining task that engaged two fingers. Each finger contacted a plate mounted on a separate torque motor, and, at unpredictable times, both plates were loaded simultaneously with forces tangential to the plates or just one of the plates was loaded. Thus, the apparatus acted as though the plates were mechanically linked or as though they were two independent objects. In different test series, each with a predominant behavior of the apparatus and with interspersed catch trials, we showed that the reactive responses clearly reflected the predominant behavior of the apparatus. Whether subject performed the task with one hand or bimanually, appropriate reactive fingertip forces developed when predominantly both contact plates were loaded or just one of the plates was loaded. When a finger was unexpectedly loaded during a catch trial, a weak initial reactive response was triggered, but the effective force development was delayed by approximately 100 msec. We conclude that the predicted physical properties of an object not only control fingertip forces during proactive but also in reactive manipulative tasks. Specifically, the automatic reactive responses reflect predictions at the level of individual digits as to the mechanical linkage of items contacted by the fingertips in manipulation.
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http://dx.doi.org/10.1523/JNEUROSCI.22-02-00600.2002 | DOI Listing |
J Neurophysiol
January 2025
Indiana University Indianapolis, School of Health and Human Sciences.
How humans perceive the texture of a surface can inform and guide how their interaction takes place. From grasping a glass to walking on icy steps, the information we gather from the surfaces we interact with is instrumental to the success of our movements. However, the hands and feet differ in their ability to explore and identify textures.
View Article and Find Full Text PDFMicromachines (Basel)
December 2024
School of Aerospace Science and Technology, Xidian University, Xi'an 710071, China.
Robotic devices with integrated tactile sensors can accurately perceive the contact force, pressure, sliding, and other tactile information, and they have been widely used in various fields, including human-robot interaction, dexterous manipulation, and object recognition. To address the challenges associated with the initial value drift, and to improve the durability and accuracy of the tactile detection for a robotic dexterous hand, in this study, a flexible tactile sensor is designed with high repeatability by introducing a supporting layer for pre-separation. The proposed tactile sensor has a detection range of 0-5 N with a resolution of 0.
View Article and Find Full Text PDFBrain Sci
November 2024
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 3-1-1 Tsushima-Naka, Kita-ku, Okayama 700-8530, Japan.
Background: Transferring learned manipulations to new manipulation tasks has enabled humans to realize thousands of dexterous object manipulations in daily life. Two-digit grasp and three-digit grasp manipulations require different fingertip forces, and our brain can switch grasp types to ensure good performance according to motor memory. We hypothesized that several brain areas contribute to the execution of the new type of motor according to the motor memory.
View Article and Find Full Text PDFSoft Robot
December 2024
Department of Mechanical Engineering, Institute of Advanced Machines and Design, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea.
Soft sensors integrated or attached to robots or human bodies enable rapid and accurate estimation of the physical states of the target systems, including position, orientation, and force. While the use of a number of sensors enhances precision and reliability in estimation, it may constrain the movement of the target system or make the entire system complex and bulky. This article proposes a rapid, efficient framework for determining where to place the sensors on the system given the limited number of available sensors.
View Article and Find Full Text PDFmedRxiv
December 2024
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Introduction: Dynamic modulation of grip occurs mainly within the major structures of the brain stem, in parallel with cortical control. This basic, but fundamental level of the brain, is robust to ill-formed feedback and to be useful, it may not require all the perceptual information of feedback we are consciously aware. This makes it viable candidate for using peripheral nerve stimulation (PNS), a form of tactile feedback that conveys intensity and location information of touch well but does not currently reproduce other qualities of natural touch.
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