Learning the signatures of the human grasp using a scalable tactile glove

By S. Sundaram, Petr Kellnhofer, Yunzhu Li, Jun-Yan Zhu, Antonio Torralba, Wojciech Matusik. Nature, 2019

1,066 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/f1fa776dcd4720103837da7591cd021d7d81595e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Chengkuo Lee
2. Xinge Guo
3. Long Liu
4. Tianyiyi He
5. Feng Wen
6. Zixuan Zhang
7. Zhongda Sun
8. Qiongfeng Shi

In Cognitive Neuroscience

1. Chengkuo Lee
2. Xinge Guo
3. Long Liu
4. Tianyiyi He
5. Feng Wen
6. Zixuan Zhang
7. Qiongfeng Shi
8. Jianxiong Zhu

Citations in the corpus

• Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “1b, there are a large number of off-the-shelf smart devices or prototypes under development today, including smartphones, smartwatches, smart glasses, smart rings [208], smart gloves [266], smart armbands [151], smart necklaces [64, 142, 328], smart shoes [106], and Etattoos [249].”
• DexPilot: Vision-Based Teleoperation of Dexterous Robotic Hand-Arm System (2019, background): “The approach herein prioritizes fingertip task-space metrics because distal regions are of highest priority in grasping and manipulation tasks as measured by their contact prevalence [28], degree of innervation [12], and heightened controllability for fine, in-hand manipulation skill [29].”
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “…[121] aims at delivering the skill set (e.g., the manipulation skills representation learned from the multisensory tactile and visual data [318], and the signatures of the human grasp learned using a tactile glove [319]) over the Tactile Internet in an ultra-reliable and low-latency manner.”
• Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses (2023, methodology): “Similar work was demonstrated by Matusik’s group, who designed a tactile glove with an array sensor and used a CNN to analyze the array data to recognize individual objects, evaluate the weight, and investigate the classic tactile patterns that occur when grasping objects [173].”