# Comparison of Constrained and Unconstrained Human Grasp Forces Using Fingernail Imaging and Visual Servoing

arXiv: https://arxiv.org/abs/2004.02311 · HTML: https://mlsanity.com/paper/2004.02311/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Force-Aware Interface via Electromyography for Natural VR/AR Interaction](https://mlsanity.com/paper/2210.01225/) (2022)
- [Real-Time Tactile Grasp Force Sensing Using Fingernail Imaging via Deep Neural Networks](https://mlsanity.com/paper/2109.15231/) (2021)

## Abstract (arXiv)

Fingernail imaging has been proven to be effective in prior works [1],[2] for estimating the 3D fingertip forces with a maximum RMS estimation error of 7%. In the current research, fingernail imaging is used to perform unconstrained grasp force measurement on multiple fingers to study human grasping. Moreover, two robotic arms with mounted cameras and a visual tracking system have been devised to keep the human fingers in the camera frame during the experiments. Experimental tests have been conducted for six human subjects under both constrained and unconstrained grasping conditions, and the results indicate a significant difference in force collaboration among the fingers between the two grasping conditions. Another interesting result according to the experiments is that in comparison to constrained grasping, unconstrained grasp forces are more evenly distributed over the fingers and there is less force variation (more steadiness) in each finger force. These results validate the importance of measuring grasp forces in an unconstrained manner in order to study how humans naturally grasp objects.