Individuals with spinal cord injury (SCI) report upper limb function as their top recovery priority. To accurately represent the true impact of new interventions on patient function, evaluation should occur in a natural setting. Wearable cameras can be used to monitor hand function at home, using computer vision to automatically analyze the resulting egocentric videos. A key step in this process, hand detection, is difficult to accomplish robustly and reliably, hindering the deployment of a complete monitoring system in the home and community. We propose an accurate and efficient hand detection method that uses a simple combination of existing detection and tracking algorithms, evaluated on a new hand detection dataset, consisting of 167,622 frames of egocentric videos collected from 17 individuals with SCI in a home simulation laboratory. The F1-scores for the best detector and tracker alone (SSD and Median Flow) were 0.90±0.07 and 0.42±0.18, respectively. The best combination method, in which a detector was used to initialize and reset a tracker, resulted in an F1-score of 0.87±0.07 while being two times faster than the fastest detector. The method proposed here, in combination with wearable cameras, will help clinicians directly measure hand function in a patient's daily life at home.

中文翻译：

---

一种在康复应用中检测以自我为中心的视频中的手的有效方法。

脊髓损伤（SCI）的患者报告上肢功能是他们的首要恢复任务。为了准确反映新干预措施对患者功能的真正影响，应在自然环境中进行评估。可穿戴式摄像机可用于在家中监控手部功能，使用计算机视觉自动分析生成的以自我为中心的视频。该过程中的关键步骤，即手部检测，很难可靠，可靠地完成，这妨碍了在家庭和社区中部署完整的监视系统。我们提出一种准确有效的手部检测方法，该方法使用现有检测和跟踪算法的简单组合，并在一个新的手部检测数据集上进行评估，该数据集由在家庭模拟实验室中从17名SCI个体中收集的167,622帧以自我为中心的视频组成。仅最佳检测器和跟踪器（SSD和中值流量）的F1分数分别为0.90±0.07和0.42±0.18。最佳组合方法（其中使用检测器来初始化和重置跟踪器）导致F1分数为0.87±0.07，但比最快的检测器快两倍。此处提出的方法与可穿戴式相机相结合，将帮助临床医生直接测量患者在家中日常生活中的手部功能。