Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

Functional Classification of Joints

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

CircuitBot: Learning to survive with robotic circuit drawing.

PloS one·2022

Same author

The trade-off between morphology and control in the co-optimized design of robots.

PloS one·2017

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 30, 2025

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Published on: October 27, 2023

Variable Stiffness Object Recognition with a CNN-Bayes Classifier on a Soft Gripper.

Jingyi Huang¹, Andre Rosendo¹

¹School of Information Science and Technology, ShanghaiTech University, Shanghai, China.

|March 11, 2022

Summary

This summary is machine-generated.

This study introduces a soft gripper system using tactile sensing and a novel convolutional neural network (CNN) for identifying hard objects within soft materials. This technology enhances robotic palpation for early cancer detection.

Keywords:

object recognition soft gripper variable stiffness

More Related Videos

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Sep 30, 2025

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Published on: October 27, 2023

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Area of Science:

Robotics
Artificial Intelligence
Biomedical Engineering

Background:

Robotic palpation offers a non-invasive method for detecting internal structures.
Current limitations exist in differentiating hard inclusions within soft tissues.
Early cancer detection relies on accurate identification of abnormalities.

Purpose of the Study:

To develop a soft gripper system for recognizing variable-stiffness objects using tactile information.
To enhance the capabilities of robotic palpation for medical diagnostics.
To improve the early detection of cancers through improved shape and size recognition of internal hard objects.

Main Methods:

A three-finger soft gripper equipped with force sensitive resistors was utilized.
Spatiotemporal tactile images (15x50) were generated during 3D palpation.
A custom convolutional neural network (CNN) architecture, SoftTactNet, was developed and trained.
Performance was compared against state-of-the-art CNNs and enhanced with a Naive Bayes classifier.

Main Results:

The SoftTactNet demonstrated superior performance in distinguishing 3D shapes and sizes of enclosed objects.
The system achieved a 97% recognition accuracy when combined with a Naive Bayes classifier.
The framework successfully identified hard inclusions within thick soft foam, simulating biological tissues.

Conclusions:

The proposed soft gripper framework with tactile sensing and CNN analysis significantly improves object recognition capabilities.
This technology holds promise for non-invasive, early cancer detection (e.g., breast, testicular) using robots.
Soft robots equipped with such systems can provide inexpensive and accessible diagnostic tools.