Quantifying feed forward control: A linear scaling model for fingertip forces and object weight

Ying Lu, Seda Bilaloglu, Viswanath Aluru, Preeti Raghavan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The ability to predict the optimal fingertip forces according to object properties before the object is lifted is known as feed forward control, and it is thought to occur due to the formation of internal representations of the object’s properties. The control of fingertip forces to objects of different weights has been studied extensively by using a custom-made grip device instrumented with force sensors. Feed forward control is measured by the rate of change of the vertical (load) force before the object is lifted. However, the precise relationship between the rate of change of load force and object weight and how it varies across healthy individuals in a population is not clearly understood. Using sets of 10 different weights, we have shown that there is a log-linear relationship between the fingertip load force rates and weight among neurologically intact individuals. We found that after one practice lift, as the weight increased, the peak load force rate (PLFR) increased by a fixed percentage, and this proportionality was common among the healthy subjects. However, at any given weight, the level of PLFR varied across individuals and was related to the efficiency of the muscles involved in lifting the object, in this case the wrist and finger extensor muscles. These results quantify feed forward control during grasp and lift among healthy individuals and provide new benchmarks to interpret data from neurologically impaired populations as well as a means to assess the effect of interventions on restoration of feed forward control and its relationship to muscular control.

Original languageEnglish (US)
Article numberA30
Pages (from-to)411-418
Number of pages8
JournalJournal of neurophysiology
Volume114
Issue number1
DOIs
StatePublished - Jul 1 2015
Externally publishedYes

Keywords

  • Electromyography
  • Feed forward and feedback control
  • Motor control and learning
  • Precision grasp
  • Sensorimotor adaptation

ASJC Scopus subject areas

  • General Neuroscience
  • Physiology

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