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Abstract：
Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets-the camera-based method (Vicon) and wearable sensor-based technology (Xsens)-were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.
摘要：
中风幸存者通常患有严重影响其日常活动的运动障碍。传感器技术和物联网的进步为中风幸存者提供了自动化评估和康复过程的机会。本文旨在提供使用AI驱动模型的智能卒中后严重程度评估。由于缺乏标签数据和专家评估，在提供虚拟评估方面存在研究空白，特别是对于未标记的数据。受到共识学习进步的启发，在本文中，我们提出了一种共识聚类算法，PSA-NMF,将各种聚类组合成一个统一的聚类，即，集群共识，与个体聚类相比，产生更稳定和更稳健的结果。本文首次在频域中使用无监督学习和躯干位移特征来研究严重程度，以进行卒中后智能评估。使用了两种不同的从U形肢体数据集收集数据的方法-基于相机的方法（Vicon）和基于可穿戴传感器的技术（Xsens）。躯干位移方法根据中风幸存者用于日常活动的代偿运动来标记每个聚类。所提出的方法使用频域中的位置和加速度数据。实验结果表明，使用中风后评估方法的所提出的聚类方法增加了诸如准确性和F分数之类的评估指标。这些发现可以导致更有效和自动化的中风康复过程，适合临床环境，从而提高卒中幸存者的生活质量。
