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

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.9K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.9K
Introduction to Learning01:18

Introduction to Learning

1.6K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.6K
Observational Learning01:12

Observational Learning

1.2K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.2K
Force Classification01:22

Force Classification

2.6K
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,...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Therapist-exoskeleton-patient interaction for gait therapy.

Science robotics·2026
Same author

Design and Validation of a Tripping-Eliciting Platform Based on Compliant Random Obstacles.

IEEE open journal of engineering in medicine and biology·2024
Same author

Human motor augmentation with an extra robotic arm without functional interference.

Science robotics·2023
Same author

Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review.

IEEE transactions on medical robotics and bionics·2023
Same author

Early decoding of walking tasks with minimal set of EMG channels.

Journal of neural engineering·2023
Same author

Myoelectric prosthesis hand grasp control following targeted muscle reinnervation in individuals with transradial amputation.

PloS one·2023
Same journal

Multimodal Cross-Attention Fusion of B-Mode Ultrasound and Strain Elastography for Tumor Segmentation in Robotics-Assisted Surgery.

IEEE transactions on medical robotics and bionics·2026
Same journal

A Pneumatically Actuated Robotic Assistant for MRI-Guided Stereotactic Neurosurgery.

IEEE transactions on medical robotics and bionics·2026
Same journal

Interdisciplinary Dialogues on Surgical Data Science: Revising Its Benefits for Surgical Stakeholders and Patients.

IEEE transactions on medical robotics and bionics·2026
Same journal

Concentric Tube Robot-Assisted Intracerebral Hemorrhage Evacuation: Validation in an Ovine Model.

IEEE transactions on medical robotics and bionics·2026
Same journal

Autonomous Slip-Prevention Grip Force Control and Its Potential in Shared Control of Robotic Prosthetic Hands.

IEEE transactions on medical robotics and bionics·2026
Same journal

Modeling and Control For Minimally Invasive Intracerebral Hemorrhage Evacuation.

IEEE transactions on medical robotics and bionics·2026
See all related articles

Related Experiment Video

Updated: Apr 3, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

7.4K

A Deep Learning Framework With Domain Generalization and Few-Shot Learning for Locomotion Mode Classification Across

Eugenio Anselmino1, Ann M Simon2, Levi J Hargrove3

  • 1Department of Excellence in Robotics and AI, and The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa.

IEEE Transactions on Medical Robotics and Bionics
|April 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework for classifying prosthetic leg movements across different sessions and users. The novel approach achieves high accuracy, improving prosthetic control for transfemoral amputees.

Keywords:
Locomotion mode classificationdeep learningintention decodinglower limb prostheses

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

874

Related Experiment Videos

Last Updated: Apr 3, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

7.4K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

874

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Prosthetics

Background:

  • Transfemoral amputees require reliable prosthetic control for daily activities.
  • Accurate classification of locomotion modes is crucial for advanced prosthetic function.
  • Inter-session and inter-subject variability pose challenges for current prosthetic algorithms.

Purpose of the Study:

  • To develop and validate a deep learning framework for robust locomotion mode classification in transfemoral amputees.
  • To address the challenge of classifying movements across different sessions, subjects, and prosthesis models.
  • To improve the clinical applicability of prosthetic control systems.

Main Methods:

  • A deep-learning framework utilizing domain-adversarial training and few-shot learning fine-tuning was employed.
  • The approach was validated using a leave-one-session-out cross-validation strategy on a dataset from 11 subjects.
  • Data from two different prosthesis models (Vanderbilt University Gen 2 and Gen 3) were merged for analysis.
  • Locomotion modes included level walking, incline/decline walking, and stair ascent/descent, analyzed at heel-strike (HS) and toe-off (TO) events.

Main Results:

  • The proposed framework achieved high median f1-scores: 99.12% (HS, VU Gen 2), 92.41% (HS, VU Gen 3), 96.83% (TO, VU Gen 2), and 94.36% (TO, VU Gen 3).
  • The method demonstrated strong performance on unseen sessions and subjects across different prosthesis models.
  • Comparisons showed superior performance over prosthesis-specific classifiers.

Conclusions:

  • The developed deep learning framework offers a promising solution for reliable locomotion mode classification in unseen data.
  • This approach enhances the potential for seamless integration and improved functionality of prosthetic devices.
  • The framework's ability to generalize across sessions, subjects, and prosthesis models is key for clinical implementation.