AI Medical Compendium Topic:
Biomechanical Phenomena

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Fundamental understanding of millipede morphology and locomotion dynamics.

Bioinspiration & biomimetics
A detailed model for the locomotory mechanics used by millipedes is provided here through systematic experimentation on the animal and validation of observations through a biomimetic robotic platform. Millipedes possess a powerful gait that is necess...

Artificial Intelligence-Assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture.

Computer methods in biomechanics and biomedical engineering
We aimed to determine whether artificial intelligence (AI)-assisted markerless motion capture software is useful in the clinical medicine and rehabilitation fields. Currently, it is unclear whether the AI-assisted markerless method can be applied to ...

Safety Evaluation and Experimental Study of a New Bionic Muscle Cable-Driven Lower Limb Rehabilitation Robot.

Sensors (Basel, Switzerland)
Safety is a significant evaluation index of rehabilitation medical devices and a significant precondition for practical application. However, the safety evaluation of cable-driven rehabilitation robots has not been reported, so this work aims to stud...

Real-time biomechanics using the finite element method and machine learning: Review and perspective.

Medical physics
PURPOSE: The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real...

An Accelerated Finite-Time Convergent Neural Network for Visual Servoing of a Flexible Surgical Endoscope With Physical and RCM Constraints.

IEEE transactions on neural networks and learning systems
This article designs and analyzes a recurrent neural network (RNN) for the visual servoing of a flexible surgical endoscope. The flexible surgical endoscope is based on a commercially available UR5 robot with a flexible endoscope attached as an end-e...

Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach.

Gait & posture
BACKGROUND: Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial acceler...

A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes.

Sensors (Basel, Switzerland)
BACKGROUND: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running.

Replicating dynamic humerus motion using an industrial robot.

PloS one
Transhumeral percutaneous osseointegrated prostheses provide upper-extremity amputees with increased range of motion, more natural movement patterns, and enhanced proprioception. However, direct skeletal attachment of the endoprosthesis elevates the ...

Analysis of Visuo Motor Control between Dominant Hand and Non-Dominant Hand for Effective Human-Robot Collaboration.

Sensors (Basel, Switzerland)
The human-in-the-loop technology requires studies on sensory-motor characteristics of each hand for an effective human-robot collaboration. This study aims to investigate the differences in visuomotor control between the dominant (DH) and non-dominan...