Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient is walking on a split-belt treadmill. Although studies have found that split-belt treadmills can produce better gait recovery outcomes than traditional single-belt treadmills, the high cost of split-belt treadmills is a significant barrier to stroke rehabilitation in clinics. In this article, we design an AI-based system for the single-belt treadmill to make it act like a split-belt by adjusting the belt speed instantaneously according to the patient's microgait phases. This system only requires a low-cost RGB camera to capture human gait patterns. A novel microgait classification pipeline model is used to detect gait phases in real time. The pipeline is based on self-supervised learning that can calibrate the anchor video with the real-time video. We then use a ResNet-LSTM module to handle temporal information and increase accuracy. A real-time filtering algorithm is used to smoothen the treadmill control. We have tested the developed system with 34 healthy individuals and four stroke patients. The results show that our system is able to detect the gait microphase accurately and requires less human annotation in training, compared to the ResNet50 classifier. Our system “Splicer” is boosted by AI modules and performs comparably as a split-belt system, in terms of timely varying left/right foot speed, creating a hemiparetic gait in healthy individuals, and promoting paretic side symmetry in force exertion for stroke patients. This innovative design can potentially provide cost-effective rehabilitation treatment for hemiparetic patients.

中文翻译：

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单皮带与分体皮带：通过微相步态捕捉进行智能跑步机控制，用于中风后康复


中风是主要的长期残疾，并造成与康复相关的重大经济负担。在中风后康复中，偏瘫患者对瘫痪腿和非瘫痪腿之间的协调运动有着特殊的需求。当患者在分体带跑步机上行走时，分体带跑步机可以通过减慢该腿的带速度来有效地促进瘫痪腿。尽管研究发现分体带跑步机比传统单带跑步机能产生更好的步态恢复效果，但分体带跑步机的高成本是临床中风康复的重大障碍。在本文中，我们为单皮带跑步机设计了一个基于人工智能的系统，通过根据患者的微步态阶段即时调整皮带速度，使其像分体皮带一样工作。该系统只需要一个低成本的 RGB 相机即可捕捉人类步态模式。一种新颖的微步态分类管道模型用于实时检测步态阶段。该管道基于自监督学习，可以用实时视频校准主播视频。然后，我们使用 ResNet-LSTM 模块来处理时间信息并提高准确性。使用实时过滤算法来平滑跑步机控制。我们已经对 34 名健康个体和 4 名中风患者测试了开发的系统。结果表明，与 ResNet50 分类器相比，我们的系统能够准确检测步态微相，并且在训练中需要更少的人工注释。 我们的系统“Splicer”由人工智能模块推动，其性能与分体带系统相当，可以及时改变左/右脚速度，为健康个体创造偏瘫步态，并促进中风患者用力时的偏瘫侧对称性。这种创新设计有可能为偏瘫患者提供具有成本效益的康复治疗。