Abstract
Cervical spinal cord injury (cSCI) can cause paralysis and impair hand function. Existing assessments in clinical settings do not reflect an individual's performance in their daily environment. Videos from wearable cameras (egocentric video) provide a novel avenue to analyze hand function in non-clinical settings. Due to the large amounts of video data generated by this approach, automated analysis methods are necessary. We propose to employ an unsupervised learning process to produce a summary of the grasping strategies used in an egocentric video. To this end, an approach was developed consisting of hand detection, pose estimation, and clustering algorithms. The performance of the method was examined with external evaluation indicators and internal evaluation indicators for an uninjured and injured participant, respectively. The results demonstrated that a Gaussian mixture model obtained the highest accuracy in terms of the maximum match, 0.63, and the Rand index, 0.26, for the uninjured participant, and a silhouette score of 0.13 for the injured participant.
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