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Related Experiment Video

Updated: Jul 24, 2025

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
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Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based

Najmeh Razfar1, Rasha Kashef1, Farah Mohammadi1

  • 1Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a smart, AI-driven approach for assessing stroke severity using unsupervised learning and trunk displacement features. The novel consensus clustering algorithm, PSA-NMF, enhances accuracy in post-stroke assessments, improving rehabilitation outcomes.

Keywords:
Wearable Sensor (Xsens)automated assessmentcamera-based systemclusteringconsensus clusteringlevel of severitystroketrunk displacement

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Area of Science:

  • Rehabilitation Medicine
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Stroke survivors frequently experience motor impairments impacting daily life.
  • Advancements in sensor technology and the Internet of Things (IoT) offer potential for automated stroke assessment and rehabilitation.
  • A research gap exists in virtual assessment for unlabeled data, particularly in unsupervised stroke severity evaluation.

Purpose of the Study:

  • To develop a smart post-stroke severity assessment system utilizing AI-driven models.
  • To address the challenge of assessing stroke severity with unlabeled data through unsupervised learning.
  • To investigate the efficacy of trunk displacement features in the frequency domain for unsupervised stroke assessment.

Main Methods:

  • Proposed a consensus clustering algorithm, PSA-NMF, combining multiple clusterings for robust results.
  • Utilized trunk displacement features in the frequency domain, analyzing position and acceleration data.
  • Employed two data collection methods: camera-based (Vicon) and wearable sensors (Xsens) from the U-limb dataset.
  • Labeled clusters based on compensatory movements observed in stroke survivors during daily activities.

Main Results:

  • The PSA-NMF algorithm demonstrated improved evaluation metrics, including accuracy and F-score, for post-stroke assessment.
  • The study is the first to use unsupervised learning and frequency-domain trunk displacement for stroke severity assessment.
  • The proposed method effectively categorizes stroke severity based on compensatory movement patterns.

Conclusions:

  • The developed AI-driven consensus clustering approach offers a stable and robust method for smart post-stroke assessment.
  • This automated assessment can significantly enhance the stroke rehabilitation process in clinical settings.
  • Improved stroke assessment accuracy and automation have the potential to enhance the quality of life for stroke survivors.