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

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Absolute Motion Analysis- General Plane Motion

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the drone...

Relative Motion Analysis - Velocity

Relative Motion Analysis - Velocity

A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Relative Motion Analysis using Rotating Axes

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

You might also read

Related Articles

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

Sort by

Same author

Author Correction: Age-group determination of living individuals using first molar images based on artificial intelligence.

Scientific reports·2022

Same author

Age-group determination of living individuals using first molar images based on artificial intelligence.

Scientific reports·2021

Same author

Gaussian Process Trajectory Learning and Synthesis of Individualized Gait Motions.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2019

Same author

Optimizing Tube Precurvature to Enhance Elastic Stability of Concentric Tube Robots.

IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society·2017

Same author

Elastic Stability of Concentric Tube Robots Subject to External Loads.

IEEE transactions on bio-medical engineering·2015

Same author

A Geometric Particle Filter for Template-Based Visual Tracking.

IEEE transactions on pattern analysis and machine intelligence·2015

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Natural movement generation using hidden Markov models and principal components.

Junghyun Kwon¹, Frank C Park

¹School of Mechanical and Aerospace Engineering,Seoul National University, Seoul 151-742, Korea. jhkwon@robotics.snu.ac.kr

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|September 12, 2008

Summary

This study introduces a novel framework for generating humanlike movements by combining principal components and hidden Markov models (HMMs). This approach ensures natural motion generation by considering both joint and task space characteristics.

Related Experiment Videos

Last Updated: Jul 1, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Area of Science:

Robotics
Computer Vision
Human-Computer Interaction

Background:

Perception of natural, humanlike movement relies on joint and task space characteristics.
Existing methods for motion generation often focus on one aspect, leading to suboptimal results.

Purpose of the Study:

To propose a unified framework for generating natural movements by integrating principal components and hidden Markov models (HMMs).
To ensure the generated movements uniformly consider both joint and task space characteristics.

Main Methods:

Extracted principal components from joint trajectories as basis elements for each movement category.
Trained Hidden Markov Models (HMMs) for each movement class using human task space motion data.
Generated natural movements via an optimal linear combination of principal components, maximizing HMM probability.

Main Results:

The proposed framework successfully generates natural movements by merging joint and task space considerations.
Experimental validation with a humanoid robot demonstrated the framework's effectiveness and advantages.

Conclusions:

The integrated approach of principal components and HMMs provides an efficient and reliable method for generating humanlike movements.
This framework offers a significant advancement in robotic motion generation and human-computer interaction.