Dousty Mehdy, Zariffa Jose
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2151-2154. doi: 10.1109/EMBC44109.2020.9175918.
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.
颈脊髓损伤（cSCI）可导致瘫痪并损害手部功能。临床环境中的现有评估无法反映个体在日常环境中的表现。可穿戴摄像头拍摄的视频（以自我为中心的视频）为分析非临床环境中的手部功能提供了一条新途径。由于这种方法会生成大量视频数据，因此需要自动化分析方法。我们建议采用无监督学习过程来总结以自我为中心的视频中使用的抓握策略。为此，开发了一种由手部检测、姿势估计和聚类算法组成的方法。分别使用外部评估指标和内部评估指标对一名未受伤参与者和一名受伤参与者检验了该方法的性能。结果表明，对于未受伤参与者，高斯混合模型在最大匹配度（0.63）和兰德指数（0.26）方面获得了最高准确率，对于受伤参与者，轮廓系数为0.13。
