Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

A Data-Driven Approach to Quantifying Immune States in Sepsis07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

482
This study investigates the immune condition in sepsis by analyzing the quantitative relationships among white blood cells, lymphocytes, and neutrophils in sepsis patients and healthy controls using data visualization analysis and three-dimensional numerical fitting to establish a mathematical model.
482
CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

3.3K
We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.
3.3K
A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion10:31

A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion

13.9K
This article provides an overview of a multi-modal approach to assessing recovery following concussion in youth athletes. The described protocol uses pre- and post-concussion assessment of performance across a wide variety of domains and can inform the development of improved concussion rehabilitation protocols specific to the youth sport...
13.9K
Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation06:32

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

1.8K
The protocol shows a prototype of the at-home multi-modal data collection platform that supports research optimizing adaptive deep brain stimulation (aDBS) for people with neurological movement disorders. We also present key findings from deploying the platform for over a year to the home of an individual with Parkinson's...
1.8K
Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists03:43

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists

650
This study presents an effective approach to performing colonoscopies. Achieving success in this procedure involves ensuring thorough bowel preparation, carefully inserting the scope while minimizing air insufflation, and skillfully managing any looping of the...
650
Religious Chanting and Self-Related Brain Regions: A Multi-Modal Neuroimaging Study05:05

Religious Chanting and Self-Related Brain Regions: A Multi-Modal Neuroimaging Study

1.5K
Here, we present a protocol to investigate the neurophysiological correlates of various meditation forms, including religious chanting. This method uniquely integrates fMRI eigenvector results for region selection in electroencephalogram (EEG) source analysis using k-means clustering. The results provide an in-depth understanding of the neural processes involved in repetitive religious...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Multitraction point strategy for endoscopic submucosal dissection in managing difficult colorectal lesions.

VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy·2026
Same author

Safety and efficacy of extended surveillance intervals following colorectal endoscopic submucosal dissection: an international multicentre study.

Gut·2026
Same author

Enabling Real-Time Shape-Sensing in Soft Robots via a Miniaturized, Single-Signal, Color-Tuned Soft Optical Sensor.

Advanced robotics research·2026
Same author

Accuracy of endoscopic ultrasound for staging esophageal and gastroesophageal junction neoplasia prior to endoscopic submucosal dissection.

Scandinavian journal of gastroenterology·2026
Same author

Outcomes of additional surgery after noncurative endoscopic submucosal dissection of upper malignant gastrointestinal lesions.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract·2026
Same author

Role of topical self-assembling peptide in preventing delayed bleeding following advanced endoscopic resection: a systematic review and meta-analysis.

Surgical endoscopy·2026

Related Experiment Video

Updated: Jan 20, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

482

Multi-modal sensing in colonoscopy: a data-driven approach.

Viola Del Bono1, Emma Capaldi1, Anushka Kelshiker2

  • 1Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA.

IEEE Robotics and Automation Letters
|January 19, 2026
PubMed
Summary

This study presents a machine learning (ML) framework for real-time 3D shape and force estimation in soft robotic sleeves for colonoscopy. The system achieves high accuracy in tracking shape and force, improving minimally invasive procedures.

Keywords:
ControlForce and Tactile SensingLearning for Soft RobotsModelingSoft Sensors and Actuators

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion
10:31

A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion

Published on: September 25, 2014

13.9K

Related Experiment Videos

Last Updated: Jan 20, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

482
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion
10:31

A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion

Published on: September 25, 2014

13.9K

Area of Science:

  • Robotics and Automation
  • Medical Devices
  • Machine Learning Applications

Background:

  • Soft optical sensors offer potential for minimally invasive procedures like colonoscopy.
  • Complex multi-modal sensor responses present significant challenges for real-time data interpretation.
  • Accurate estimation of 3D shape and contact force is crucial for safe and effective colonoscopy.

Purpose of the Study:

  • To develop a machine learning (ML) framework for real-time estimation of 3D shape and contact force in a soft robotic sleeve for colonoscopy.
  • To create an automated data collection platform to overcome manual calibration limitations and generate large datasets for ML.
  • To investigate ML-based contact localization capabilities for enhanced spatial awareness.

Main Methods:

  • Development of an automated platform for collecting sensor data across various orientations, curvatures, and contact forces.
  • Implementation of a cascaded ML architecture for sequential estimation of contact force and 3D shape.
  • Training ML models to estimate contact intensity and location across 16 distributed indenters on the sleeve.

Main Results:

  • The ML framework achieved high accuracy in shape and force estimation, with errors of 4.7% for curvature, 2.37% for orientation, and 5.5% for force tracking.
  • Contact force intensity was estimated with errors ranging from 0.06 N to 0.31 N.
  • ML-based contact localization demonstrated promising spatial resolution, with 8 indenters achieving over 80% accuracy despite close proximity.

Conclusions:

  • The developed ML framework enables accurate real-time estimation of 3D shape and contact force for soft robotic sleeves in colonoscopy.
  • The automated data collection platform facilitates efficient ML model training and validation.
  • The system shows significant potential for improving the safety and efficacy of minimally invasive colonoscopy procedures through enhanced sensing capabilities.