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

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

Paretic limb biomechanical response to hip exoskeleton limb assistance strategies during walking for individuals post-stroke.

Journal of biomechanics·2026

Same author

Wearable technologies for assisted mobility in the real world.

Nature communications·2025

Same author

Deep domain adaptation eliminates costly data required for task-agnostic wearable robotic control.

Science robotics·2025

Same author

Robotic Ankle Exoskeleton and Limb Angle Biofeedback for Assisting Stroke Gait: A Feasibility Study.

IEEE robotics and automation letters·2025

Same author

Online Adaptation Framework Enables Personalization of Exoskeleton Assistance During Locomotion in Patients Affected by Stroke.

IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society·2025

Same author

The Second Skin: A Wearable Sensor Suite That Enables Real-Time Human Biomechanics Tracking Through Deep Learning.

IEEE transactions on bio-medical engineering·2025

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

Search research articles

Related Experiment Video

Updated: May 3, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

Machine Learning Enables Rapid Detection of Slips Using a Robotic Hip Exoskeleton.

Reese R Peterson¹, Jennifer K Leestma², Inseung Kang³

¹Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 USA. He is now with the Florida Institute for Human and Machine Cognition, Pensacola, FL, 32502 USA.

IEEE Transactions on Medical Robotics and Bionics

|August 21, 2025

Summary

This summary is machine-generated.

Researchers developed slip detection algorithms for hip exoskeletons to aid balance recovery. Extreme Gradient Boosting (XGBoost) showed the best performance, achieving high accuracy and rapid detection times for industrial workers and older adults.

Keywords:

Fall Prevention Locomotion Machine Learning Robotic Hip Exoskeleton Slip Detection

More Related Videos

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Published on: October 25, 2024

Related Experiment Videos

Last Updated: May 3, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Published on: October 25, 2024

Area of Science:

Robotics
Biomechanics
Machine Learning

Background:

Slips are a leading cause of injury for industrial workers and older adults.
Effective balance recovery systems require rapid slip detection.
Current assistive devices lack effective real-time slip detection capabilities.

Purpose of the Study:

To compare the efficacy of different machine learning models for detecting slips using hip exoskeleton sensors.
To identify the optimal algorithm for rapid and accurate slip detection to enhance balance recovery.

Main Methods:

Trained and evaluated user-independent models using Linear Discriminant Analysis (LDA), Extreme Gradient Boosting (XGBoost), and Convolutional Neural Networks (CNN).
Utilized native sensor data from a hip exoskeleton during treadmill-based slip perturbations.
Tested models on early-stance (ES) and late-stance (LS) slips of varying magnitudes.

Main Results:

All tested models, except LDA for LS slips, achieved over 90% accuracy in slip detection.
XGBoost demonstrated superior performance with rapid detection times (155.06 ms for ES, 228.88 ms for LS) and high median accuracies (96.25% for ES, 93.75% for LS).
XGBoost achieved 100% sensitivity at 195.64 ms (ES) and 266.24 ms (LS).

Conclusions:

Machine learning models, particularly XGBoost, show significant promise for real-time slip detection in hip exoskeletons.
The developed models can aid in designing effective balance recovery strategies for slip perturbations.
Further research is warranted to create generalizable models for diverse populations and slip scenarios.