Bandini Andrea, Dousty Mehdy, Zariffa Jose
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2159-2162. doi: 10.1109/EMBC44109.2020.9176274.
Cervical spinal cord injury (cSCI) causes the paralysis of upper and lower limbs and trunk, significantly reducing quality of life and community participation of the affected individuals. The functional use of the upper limbs is the top recovery priority of people with cSCI and wearable vision-based systems have recently been proposed to extract objective outcome measures that reflect hand function in a natural context. However, previous studies were conducted in a controlled environment and may not be indicative of the actual hand use of people with cSCI living in the community. Thus, we propose a deep learning algorithm for automatically detecting hand-object interactions in egocentric videos recorded by participants with cSCI during their daily activities at home. The proposed approach is able to detect hand-object interactions with good accuracy (F1-score up to 0.82), demonstrating the feasibility of this system in uncontrolled situations (e.g., unscripted activities and variable illumination). This result paves the way for the development of an automated tool for measuring hand function in people with cSCI living in the community.
颈脊髓损伤（cSCI）会导致上下肢及躯干瘫痪，严重降低受影响个体的生活质量和社区参与度。上肢的功能使用是cSCI患者康复的首要任务，最近有人提出使用基于视觉的可穿戴系统来提取反映自然环境下手功能的客观结果指标。然而，以往的研究是在受控环境中进行的，可能无法反映社区中cSCI患者的实际手部使用情况。因此，我们提出了一种深度学习算法，用于自动检测cSCI患者在家中日常活动期间录制的以自我为中心视频中的手-物体交互。所提出的方法能够以较高的准确率检测手-物体交互（F1分数高达0.82），证明了该系统在不受控制的情况下（如无脚本活动和可变光照）的可行性。这一结果为开发一种用于测量社区中cSCI患者手功能的自动化工具铺平了道路。
