Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets

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.

Can We Target Close Therapeutic Goals in the Gait Re-Education Algorithm for Stroke Patients at the Beginning of the Rehabilitation Process?

“…The 3D motion capture systems are widely used for this purpose, but as Di Raimondo noted, the locomotor function assessment needs to be moved from the laboratory to the patient environment to assess the true impact of specific clinical therapies on the patient’s functioning [ 48 ]. Advances in sensor technology have made it possible to automate the processes of assessment and rehabilitation of stroke patients [ 49 ]. Wireless sensors allow researchers to quickly obtain general data and data which are unavailable from a traditional clinical examination.…”

Telehealth-Enabled In-Home Elbow Rehabilitation for Brachial Plexus Injuries Using Deep-Reinforcement-Learning-Assisted Telepresence Robots

“…The study highlighted that robotic devices could deliver repetitive training tasks, which are often required for neuromuscular rehabilitation. A study [ 32 ] by Patel et al showcased the use of kinematic sensors and smart textiles to remotely monitor patients’ movements during physical therapy. These real-time data were critical for providing feedback to both the patient and the therapist, allowing for more targeted and effective therapy.…”