Cao Shengting, Ko Mansoo, Li Chih-Ying, Brown David, Wang Xuefeng, Hu Fei, Gan Yu
Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487 USA.
University of Texas Medical Branch, Mountain Brook, TX 77555-0128 USA.
IEEE Trans Hum Mach Syst. 2023 Dec;53(6):1006-1016. doi: 10.1109/thms.2023.3327661. Epub 2023 Nov 21.
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.
中风是导致长期残疾的主要原因，并给康复带来了巨大的经济负担。在中风后康复中，偏瘫患者对患侧腿和非患侧腿之间的协调运动有特殊需求。分带跑步机可以通过在患者在分带跑步机上行走时减慢患侧腿的皮带速度，有效地促进患侧腿的运动。尽管研究发现分带跑步机比传统的单带跑步机能产生更好的步态恢复效果，但分带跑步机的高成本是临床中风康复的一个重大障碍。在本文中，我们设计了一种基于人工智能的单带跑步机系统，通过根据患者的微步态阶段即时调整皮带速度，使其表现得像分带跑步机。该系统只需要一个低成本的RGB摄像头来捕捉人体步态模式。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，可以将锚定视频与实时视频进行校准。然后我们使用ResNet-LSTM模块来处理时间信息并提高准确性。一种实时滤波算法用于平滑跑步机控制。我们已经用34名健康个体和4名中风患者对开发的系统进行了测试。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阶段，并且在训练中需要的人工标注更少。我们的系统“Splicer”由人工智能模块推动，在及时改变左右脚速度、在健康个体中创造偏瘫步态以及促进中风患者患侧用力对称方面，其性能与分带系统相当。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。
