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ATHENA: automatically tracking hands expertly with no annotations.

Daanish M Mulla1, Mario Costantino2, Erez Freud2,3,4

  • 1School of Kinesiology & Health Science, York University, Toronto, Ontario, Canada.

Journal of Neurophysiology
|November 21, 2025
PubMed
Summary
This summary is machine-generated.

ATHENA, a new Python toolbox, provides accurate 3-D markerless hand tracking for naturalistic behaviors. This automated solution reduces costs and participant encumbrance, enabling more ecologically valid motor control studies.

Keywords:
behaviordexteritykinematicsmotion capturepose estimation

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Area of Science:

  • Biomechanics and Motor Control
  • Human-Computer Interaction
  • Robotics

Background:

  • Marker-based motion capture for studying hand behaviors is costly, time-consuming, and restricts participant movement.
  • Existing markerless pose estimation solutions lack validation for precise hand-object manipulation tasks.
  • There is a need for accurate, accessible tools for naturalistic hand behavior analysis.

Purpose of the Study:

  • To introduce Automatically Tracking Hands Expertly with No Annotations (ATHENA), an open-source Python toolbox for 3-D markerless hand tracking.
  • To validate ATHENA's accuracy and reliability against an industry-standard marker-based system (OptiTrack).
  • To demonstrate ATHENA's utility in facilitating ecologically valid motor control and learning studies.

Main Methods:

  • Developed ATHENA, a Python-based toolbox for markerless 3-D hand tracking.
  • Concurrently recorded hand kinematics using ATHENA and OptiTrack systems.
  • Compared kinematic variables (grip aperture, wrist velocity, etc.) during unimanual, bimanual, and object manipulation tasks.

Main Results:

  • ATHENA demonstrated high spatiotemporal agreement with OptiTrack (R² > 0.90).
  • Low root mean square differences were observed for key kinematic variables (<1 cm, <4 cm/s, <5°-10°).
  • ATHENA preserved trial-to-trial kinematic variability, yielding identical scientific conclusions to marker-based methods.

Conclusions:

  • ATHENA is an accurate, automated, and user-friendly platform for 3-D markerless hand tracking.
  • The toolbox significantly reduces financial and time costs associated with motion capture.
  • ATHENA enables more ecologically valid studies of naturalistic hand behaviors and human dexterity.