A Personalized Video-Based Hand Taxonomy: Application for Individuals with Spinal Cord Injury
It addresses the need for sensitive assessments and tailored interventions for individuals with spinal cord injury, though it is incremental as it builds on existing methods for grasp analysis.
This study tackled the problem of evaluating hand function in individuals with spinal cord injury by developing a personalized hand grasp taxonomy from egocentric video, achieving a cluster purity of 67.6% ± 24.2% with 18.0% ± 21.8% redundancy.
Hand function is critical for our interactions and quality of life. Spinal cord injuries (SCI) can impair hand function, reducing independence. A comprehensive evaluation of function in home and community settings requires a hand grasp taxonomy for individuals with impaired hand function. Developing such a taxonomy is challenging due to unrepresented grasp types in standard taxonomies, uneven data distribution across injury levels, and limited data. This study aims to automatically identify the dominant distinct hand grasps in egocentric video using semantic clustering. Egocentric video recordings collected in the homes of 19 individual with cervical SCI were used to cluster grasping actions with semantic significance. A deep learning model integrating posture and appearance data was employed to create a personalized hand taxonomy. Quantitative analysis reveals a cluster purity of 67.6% +- 24.2% with with 18.0% +- 21.8% redundancy. Qualitative assessment revealed meaningful clusters in video content. This methodology provides a flexible and effective strategy to analyze hand function in the wild. It offers researchers and clinicians an efficient tool for evaluating hand function, aiding sensitive assessments and tailored intervention plans.