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

Somatosensation

Somatosensation

The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.

You might also read

Related Articles

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

Sort by

Same author

ImitateCholec: A Multimodal Dataset for Long-Horizon Imitation Learning in Robotic Cholecystectomy.

Scientific data·2026

Same author

No cancer left behind: a testbed and demonstration of concept for photoacoustic tumor bed inspection.

Computer assisted surgery (Abingdon, England)·2025

Same author

Markerless Tracking of Robotic Surgical Instruments With Head Mounted Display for Augmented Reality Applications.

Healthcare technology letters·2025

Same author

Surgical embodied intelligence for generalized task autonomy in laparoscopic robot-assisted surgery.

Science robotics·2025

Same author

SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning.

Science robotics·2025

Same author

Mixed reality based teleoperation and visualization of surgical robotics.

Healthcare technology letters·2024

Same journal

Strain alignment: toward assessing mechanical plausibility of predicted displacement fields.

International journal of computer assisted radiology and surgery·2026

Same journal

Vascular geometry characterization for AI-based endovascular navigation.

International journal of computer assisted radiology and surgery·2026

Same journal

Nail It! A learning framework for autonomous surgical suturing and teleoperation on the dVRK.

International journal of computer assisted radiology and surgery·2026

Same journal

Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.

International journal of computer assisted radiology and surgery·2026

Same journal

BronchoLumen: analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy.

International journal of computer assisted radiology and surgery·2026

Same journal

Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study).

International journal of computer assisted radiology and surgery·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 3, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

Enhancing robotic telesurgery with sensorless haptic feedback.

Nural Yilmaz^1,2, Brendan Burkhart³, Anton Deguet³

¹Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. nyilmaz2@jhu.edu.

International Journal of Computer Assisted Radiology and Surgery

|April 10, 2024

Summary

This summary is machine-generated.

Sensorless haptic feedback in teleoperated surgery significantly improves tumor detection accuracy and reduces interaction forces. Dynamic compensation further enhances performance, offering benefits without hardware changes.

Keywords:

Deep learning Force sensing Haptics Teleoperation

More Related Videos

Adaptation of a Haptic Robot in a 3T fMRI

Adaptation of a Haptic Robot in a 3T fMRI

Published on: October 4, 2011

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

Related Experiment Videos

Last Updated: May 3, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

Adaptation of a Haptic Robot in a 3T fMRI

Adaptation of a Haptic Robot in a 3T fMRI

Published on: October 4, 2011

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

Area of Science:

Robotics
Surgical Technology
Human-Computer Interaction

Background:

Teleoperation systems in surgery aim to enhance surgeon capabilities remotely.
Haptic feedback is crucial for improving surgical precision and safety.
Existing systems often rely on complex hardware for force sensing.

Purpose of the Study:

To evaluate user performance in telesurgical tasks using the da Vinci Research Kit (dVRK).
To compare unilateral teleoperation, bilateral teleoperation with force sensors, and sensorless force estimation.
To assess the impact of sensorless haptic feedback with dynamic compensation.

Main Methods:

Developed a four-channel teleoperation system with disturbance observers and sensorless force estimation.
Conducted palpation experiments with 12 users on tissue phantoms using various feedback modalities.
Performed peg transfer experiments with 10 users to assess sensorless haptic feedback with and without dynamic compensation.

Main Results:

Sensorless haptic feedback increased tumor detection accuracy by 30% compared to visual feedback alone.
Accuracy with sensorless feedback was comparable to sensor feedback or direct contact.
Sensorless feedback reduced incidental contact forces by threefold but increased free motion forces.

Conclusions:

Sensorless haptic feedback offers significant benefits for teleoperated surgery systems.
Dynamic compensation can mitigate drawbacks and improve overall surgical performance.
This technology enhances surgical outcomes without requiring hardware modifications.