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Related Experiment Video

Updated: Jul 4, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Motor ability-aware adaptive pose estimation with hierarchical uncertainty modeling and cross-ability learning.

Qian Wang¹, Quan Zhou², Jinshan Yang³

¹College of Physical Education, Hunan University of Science and Technology, Changsha, Hunan, China.

Scientific Reports

|July 2, 2026

Summary

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Hierarchy of Motor Control

Hierarchy of Motor Control

The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.

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This study introduces a novel human pose estimation framework that adapts to diverse motor abilities, significantly improving accuracy and reducing performance gaps for individuals with motor impairments. The system enhances inclusivity in assistive technologies.

Area of Science:

Computer Vision
Machine Learning
Biomedical Engineering

Background:

Current human pose estimation (HPE) systems exhibit reduced accuracy with varying motor abilities.
Existing HPE methods lack uncertainty quantification due to reliance on standardized anatomy.

Purpose of the Study:

To develop an inclusive HPE framework addressing diverse motor abilities.
To quantify uncertainty in pose estimation across different body parts and user groups.

Main Methods:

MADSRNet: Dynamically adapts skeleton structures using gated fusion and dynamic graph construction.
HUGPose: Employs heteroscedastic regression for hierarchical uncertainty estimation.
CADCL: Leverages contrastive learning and domain adversarial training for cross-ability knowledge transfer.

Keywords:

Assistive technology Contrastive learning Domain adaptation Human pose estimation Inclusive AI Motor ability adaptation Uncertainty quantification

Related Experiment Videos

Last Updated: Jul 4, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Main Results:

Achieved state-of-the-art results with 41.2 mm MPJPE on the DiverseMotor-PE dataset, a 14.7% improvement over ViTPose.
Reduced the mean per-level MPJPE difference between typical and severely impaired individuals from 12.1 mm to 10.3 mm.
Demonstrated low expected calibration error (1.58%) for uncertainty estimates.

Conclusions:

The proposed framework provides accurate human pose estimation irrespective of motor ability.
This work advances inclusive HPE systems, promoting equitable access to healthcare and assistive technologies.