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.

通过语义聚类自动识别晚期视频中的主要抓握类型，并利用深度学习模型创建个性化手部分类系统，为研究和临床人员提供了一种灵活和有效的手部功能分析策略。