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Web-based Clinician Guide to Record Compatible Video of Standardized Drinking Task Kinematics for Computer Vision

¹Department of Physical Medicine and Rehabilitation, University of Kentucky; Department of Mechanical and Aerospace Engineering, University of Kentucky; justin.huber@uky.edu.

Journal of Visualized Experiments : Jove

|December 15, 2025

Summary

This study introduces a user-friendly web app for standardized upper limb kinematic assessment during drinking tasks. This tool simplifies motion capture, aiming to improve rehabilitation research and clinical practice for patients with upper limb impairments.

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Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Published on: March 28, 2018

Area of Science:

Rehabilitation Science
Biomedical Engineering
Computer Vision

Background:

Upper limb assessment is crucial for rehabilitation outcomes.
Kinematic analysis of the drinking task is a validated measure for conditions like stroke and musculoskeletal disease.
Current motion capture methods present technical challenges hindering widespread adoption in research and clinical practice.

Purpose of the Study:

To present a web-based application (app) for standardized video capture of the drinking task.
To demonstrate the integration of this video capture with a computer vision pipeline for kinematic metric extraction.
To reduce technical barriers and improve access to upper limb kinematic data in rehabilitation.

Main Methods:

Development of a web-based application guiding clinicians through a standardized drinking task protocol.
Utilizing off-the-shelf stereo cameras and personal computers for video acquisition.
Employing a custom-built computer vision pipeline to process video data and extract kinematic metrics.

Main Results:

The developed app facilitates standardized video capture of the drinking task.
The video capture is compatible with a computer vision pipeline for kinematic analysis.
The approach significantly reduces the technical complexity associated with traditional motion capture.

Conclusions:

The presented tool enhances accessibility to standardized upper limb kinematic metrics.
This advancement is expected to accelerate research and improve clinical care for rehabilitation populations with upper limb impairments.
Ongoing validation and usability studies will further support the clinical translation of this technology.