Sensors, Vol. 23, Pages 5513: Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets

Sensors, Vol. 23, Pages 5513: Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets Sensors doi: 10.3390/s23125513 Authors: Najmeh Razfar Rasha Kashef Farah Mohammadi 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 employ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research