Student Competition (Technology Innovation) ID 1970388

IF 2.4 Q1 REHABILITATION Topics in Spinal Cord Injury Rehabilitation Pub Date : 2023-09-01 DOI:10.46292/sci23-1970388s
Mehdy Dousty, David J. Fleet, J. Zariffa
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Abstract

The evaluation of hand function after spinal cord injury (SCI) is conducted in clinical settings, which may not accurately reflect hand function in the real world, thereby limiting the efficacy assessment of new treatments. Wearable cameras, also known as egocentric video, are a novel method to evaluate hand function in non-clinical environments. Nonetheless, manual processing of vast quantities of complex video data is difficult, highlighting the need for automated data analysis. The objective of this study was to automatically identify distinct hand postures in egocentric video using unsupervised machine learning. Seventeen participants with cervical SCI recorded activities of daily living in a home simulation laboratory. A hand pose estimation algorithm was applied on detected hands to determine 2D joint locations, which were lifted to 3D coordinates. The resulting hand posture information was subjected to a number of clustering techniques. Hand grasps were manually labelled into four categories for evaluation purposes: power, precision, intermediate, and non-prehensile. K-Means clustering consistently exhibited the highest Silhouette score, which reflects the presence of discrete clusters in the data. When comparing with manual annotations, Spectral Clustering applied to a feature space consisting of 2D pose estimation with confidence scores yield the best performance as quantified by maximum match (0.48), Fowlkes-Mallows score (0.46), and normalized mutual information (0.22). This is the first attempt to develop an unsupervised, data-driven hand taxonomy for individuals with SCI using wearable technology. The findings suggest that the method is capable of grouping similar hand grasps.
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学生竞赛(科技创新) ID 1970388
脊髓损伤(SCI)后的手部功能评估是在临床环境中进行的,这可能无法准确反映真实世界中的手部功能,从而限制了新疗法的疗效评估。可穿戴式摄像机(也称为 "自我中心视频")是在非临床环境中评估手部功能的一种新方法。然而,人工处理大量复杂的视频数据十分困难,这凸显了自动数据分析的必要性。本研究的目的是利用无监督机器学习自动识别自我中心视频中不同的手部姿势。 17 名患有颈椎 SCI 的参与者在家庭模拟实验室中记录了日常生活活动。在检测到的双手上应用了手部姿势估计算法,以确定二维关节位置,并将其提升到三维坐标。由此得到的手部姿势信息采用了多种聚类技术。出于评估目的,人工将手部抓握分为四类:力量型、精确型、中间型和非理解型。 K-Means 聚类始终显示出最高的 Silhouette 分数,这反映出数据中存在离散的聚类。与人工标注相比,应用于由二维姿态估计和置信度分数组成的特征空间的光谱聚类产生了最佳性能,其量化指标包括最大匹配度(0.48)、Fowlkes-Mallows 分数(0.46)和归一化互信息(0.22)。 这是利用可穿戴技术为 SCI 患者开发无监督、数据驱动的手部分类法的首次尝试。研究结果表明,该方法能够对相似的手部抓握动作进行分组。
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来源期刊
CiteScore
3.20
自引率
3.40%
发文量
33
期刊介绍: Now in our 22nd year as the leading interdisciplinary journal of SCI rehabilitation techniques and care. TSCIR is peer-reviewed, practical, and features one key topic per issue. Published topics include: mobility, sexuality, genitourinary, functional assessment, skin care, psychosocial, high tetraplegia, physical activity, pediatric, FES, sci/tbi, electronic medicine, orthotics, secondary conditions, research, aging, legal issues, women & sci, pain, environmental effects, life care planning
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