文献类型： 会议论文

第一作者： Mehdy Dousty

作者： Mehdy Dousty ¹ ; Jose Zariffa ¹ ;

作者机构： 1.Institute of Biomaterials and Biomedical Engineering, University of Toronto

会议名称： Annual International Conference of the IEEE Engineering in Medicine and Biology Society

主办单位：

页码： 2151-2154

摘要： 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.

分类号： Q81-53