# Egocentric Video: A New Tool for Capturing Hand Use of Individuals with Spinal Cord Injury at Home

arXiv: https://arxiv.org/abs/1809.00928 · HTML: https://mlsanity.com/paper/1809.00928/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [Domain Adaptive Hand Keypoint and Pixel Localization in the Wild](https://mlsanity.com/paper/2203.08344/) (2022)
- [Measuring hand use in the home after cervical spinal cord injury using egocentric video](https://mlsanity.com/paper/2203.16996/) (2022)
- [Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction](https://mlsanity.com/paper/2110.10174/) (2021)
- [The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments](https://mlsanity.com/paper/2407.14649/) (2024)
- [A Personalized Video-Based Hand Taxonomy: Application for Individuals with Spinal Cord Injury](https://mlsanity.com/paper/2403.18094/) (2024)
- [REST-HANDS: Rehabilitation with Egocentric Vision Using Smartglasses for Treatment of Hands after Surviving Stroke](https://mlsanity.com/paper/2409.20116/) (2024)

## Abstract (arXiv)

Current upper extremity outcome measures for persons with cervical spinal cord injury (cSCI) lack the ability to directly collect quantitative information in home and community environments. A wearable first-person (egocentric) camera system is presented that can monitor functional hand use outside of clinical settings. The system is based on computer vision algorithms that detect the hand, segment the hand outline, distinguish the user's left or right hand, and detect functional interactions of the hand with objects during activities of daily living. The algorithm was evaluated using egocentric video recordings from 9 participants with cSCI, obtained in a home simulation laboratory. The system produces a binary hand-object interaction decision for each video frame, based on features reflecting motion cues of the hand, hand shape and colour characteristics of the scene. This output was compared with a manual labelling of the video, yielding F1-scores of 0.74 ± 0.15 for the left hand and 0.73 ± 0.15 for the right hand. From the resulting frame-by-frame binary data, functional hand use measures were extracted: the amount of total interaction as a percentage of testing time, the average duration of interactions in seconds, and the number of interactions per hour. Moderate and significant correlations were found when comparing these output measures to the results of the manual labelling, with ρ = 0.40, 0.54 and 0.55 respectively. These results demonstrate the potential of a wearable egocentric camera for capturing quantitative measures of hand use at home.