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Related Experiment Video

Updated: Dec 13, 2025

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Published on: December 23, 2020

Tenodesis Grasp Detection in Egocentric Video.

Mehdy Dousty, Jose Zariffa

IEEE Journal of Biomedical and Health Informatics

|August 6, 2020

Summary

Researchers developed a new machine learning method to automatically detect the tenodesis grasp in individuals with cervical spinal cord injury (cSCI) using wearable camera video. This enables remote monitoring of rehabilitation outcomes and therapeutic guidance.

Area of Science:

Biomedical Engineering
Rehabilitation Technology
Machine Learning in Healthcare

Background:

Cervical spinal cord injury (cSCI) significantly impacts upper limb motor function.
Monitoring rehabilitation progress, particularly grasping strategies like the tenodesis grasp, is crucial for individuals with cSCI.
Existing methods lack automated analysis of egocentric video for evaluating wrist movements essential for grasping.

Purpose of the Study:

To develop and validate a machine learning approach for estimating wrist angle from egocentric video.
To enable automated detection of the tenodesis grasp in individuals with cSCI.
To facilitate remote monitoring of rehabilitation outcomes in a home setting.

Main Methods:

A three-step machine learning pipeline: hand detection, pose estimation, and arm orientation estimation.

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Last Updated: Dec 13, 2025

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Published on: December 23, 2020

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Published on: January 15, 2018

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Utilized egocentric videos of individuals with cSCI performing daily activities.

Algorithm performance evaluated on annotated frames for accuracy and error rates.

Main Results:

Hand detection and pose estimation achieved 63% and 76% accuracy in locating key wrist and finger coordinates.
Arm orientation estimation demonstrated a low mean absolute error of 2.76 +/- 0.39 degrees.
The tenodesis grasp was detected with 72% +/- 11% accuracy across various activities.

Conclusions:

The developed method accurately estimates wrist movements and detects the tenodesis grasp from egocentric video.
This approach provides a reliable, non-invasive tool for clinicians and researchers to monitor cSCI rehabilitation at home.
Enables objective assessment of compensatory grasping strategies and supports remote therapeutic guidance.