Objective: Upper limb (UL) impairment impacts quality of life, but is common after stroke. UL function evaluated in the clinic may not reflect use in activities of daily living (ADLs) after stroke, and current approaches for assessment at home rely on self-report and lack details about hand function. Wrist-worn accelerometers have been applied to capture UL use, but also fail to reveal details of hand function. In response, a wearable system is proposed consisting of egocentric cameras combined with computer vision approaches, in order to identify hand use (hand-object interactions) and the role of the more-affected hand (as stabilizer or manipulator) in unconstrained environments. Methods: Nine stroke survivors recorded their performance of ADLs in a home simulation laboratory using an egocentric camera. Motion, hand shape, colour, and hand size change features were generated and fed into random forest classifiers to detect hand use and classify hand roles. Leave-one-subject-out cross-validation (LOSOCV) and leave-one-task-out cross-validation (LOTOCV) were used to evaluate the robustness of the algorithms. Results: LOSOCV and LOTOCV F1-scores for more-affected hand use were 0.64 ± 0.24 and 0.76 ± 0.23, respectively. For less-affected hands, LOSOCV and LOTOCV F1-scores were 0.72 ± 0.20 and 0.82 ± 0.22. F1-scores for hand role classification were 0.70 ± 0.19 and 0.68 ± 0.23 in the more-affected hand for LOSOCV and LOTOCV, respectively, and 0.59 ± 0.23 and 0.65 ± 0.28 in the less-affected hand. Conclusion: The results demonstrate the feasibility of predicting hand use and the hand roles of stroke survivors from egocentric videos.

中文翻译：

---

使用以自我为中心的视频识别中风后的手部使用和手部角色


目的：上肢（UL）损伤会影响生活质量，但在中风后很常见。在诊所评估的 UL 功能可能无法反映中风后日常生活活动 (ADL) 的使用情况，目前的家庭评估方法依赖于自我报告，缺乏有关手功能的详细信息。腕戴式加速度计已用于捕获 UL 使用情况，但也无法揭示手部功能的细节。为此，提出了一种由以自我为中心的相机与计算机视觉方法相结合的可穿戴系统，以识别手的使用（手与物体的交互）以及受影响较大的手（作为稳定器或操纵器）在不受约束的环境中的作用。方法：九名中风幸存者在家庭模拟实验室中使用自我中心相机记录了他们的 ADL 表现。生成运动、手部形状、颜色和手部大小变化特征并将其输入随机森林分类器以检测手部使用并对手部角色进行分类。使用留一主题交叉验证（LOSOCV）和留一任务交叉验证（LOTOCV）来评估算法的鲁棒性。结果：受影响较大的手部使用的 LOSOCV 和 LOTOCV F1 分数分别为 0.64 ± 0.24 和 0.76 ± 0.23。对于受影响较小的手，LOSOCV 和 LOTOCV F1 分数分别为 0.72 ± 0.20 和 0.82 ± 0.22。对于 LOSOCV 和 LOTOCV，手部角色分类的 F1 分数在受影响较大的手部分别为 0.70 ± 0.19 和 0.68 ± 0.23，在受影响较小的手部分别为 0.59 ± 0.23 和 0.65 ± 0.28。结论：结果证明了从以自我为中心的视频中预测中风幸存者的手部使用和手部角色的可行性。