This sensor-packed glove could capture signatures of human grasp

This sensor-packed glove could capture signatures of human grasp
Credit: MIT researchers.

MIT researchers have developed a low-cost, sensor-packed glove that captures pressure signals as humans interact with objects.

















Prosthetics manufacturers can potentially use information to, say, choose optimal spots for placing pressure sensors and help customize prosthetics to the tasks and objects people regularly interact with.

Joining Sundaram on the paper are: CSAIL postdocs Petr Kellnhofer and Jun-Yan Zhu; CSAIL graduate student Yunzhu Li; Antonio Torralba, a professor in EECS and director of the MIT-IBM Watson AI Lab; and Wojciech Matusik, an associate professor in electrical engineering and computer science and head of the Computational Fabrication group.



The threads connect from the glove to an external circuit that translates the pressure data into “tactile maps,” which are essentially brief videos of dots growing and shrinking across a graphic of a hand. The dots represent the location of pressure points, and their size represents the force — the bigger the dot, the greater the pressure.

From those maps, the researchers compiled a dataset of about 135,000 video frames from interactions with 26 objects. Those frames can be used by a neural network to predict the identity and weight of objects, and provide insights about the human grasp.

To identify objects, the researchers designed a convolutional neural network (CNN), which is usually used to classify images, to associate specific pressure patterns with specific objects. But the trick was choosing frames from different types of grasps to get a full picture of the object.

The idea was to mimic the way humans can hold an object in a few different ways in order to recognize it, without using their eyesight. Similarly, the researchers’ CNN chooses up to eight semirandom frames from the video that represent the most dissimilar grasps — say, holding a mug from the bottom, top, and handle.

But the CNN can’t just choose random frames from the thousands in each video, or it probably won’t choose distinct grips. Instead, it groups similar frames together, resulting in distinct clusters corresponding to unique grasps.

Then, it pulls one frame from each of those clusters, ensuring it has a representative sample. Then the CNN uses the contact patterns it learned in training to predict an object classification from the chosen frames.

“We want to maximize the variation between the frames to give the best possible input to our network,” Kellnhofer says.

“All frames inside a single cluster should have a similar signature that represent the similar ways of grasping the object. Sampling from multiple clusters simulates a human interactively trying to find different grasps while exploring an object.”


Notably, the CNN wasn’t trained on any frames it was tested on, meaning it couldn’t learn to just associate weight with an object.

In testing, a single frame was inputted into the CNN. Essentially, the CNN picks out the pressure around the hand caused by the object’s weight, and ignores pressure caused by other factors, such as hand positioning to prevent the object from slipping. Then it calculates the weight based on the appropriate pressures.


“Joints are important for predicting weight, but there are also important components of weight from fingertips and the palm that we capture,” Sundaram says.

Written by Rob Matheson.