Determining User Intent of Partly Dynamic Shoulder Tasks in Individuals with Chronic Stroke Using Pattern Recognition

Joseph V. Kopke, Michael D. Ellis, Levi J. Hargrove*

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Stroke remains the leading cause of long-term disability in the US. Although therapy can achieve limited improvement of paretic arm use and performance, weakness and abnormal muscle synergies - which cause unintentional elbow, wrist, and finger flexion during shoulder abduction - contribute significantly to limb disuse and compound rehabilitation efforts. Emerging wearable exoskeleton technology could provide powered abduction support for the paretic arm, but requires a clinically feasible, robust control scheme capable of differentiating multiple shoulder degrees-of-freedom. This study examines whether pattern recognition of sensor data can accurately identify user intent for 9 combinations of 1- and 2- degree-of-freedom shoulder tasks. Participants with stroke (n = 12) used their paretic and non-paretic arms, and healthy controls (n = 12) used their dominant arm to complete tasks on a lab-based robot involving combinations of abduction, adduction, and internal and external rotation of the shoulder. We examined the effect of arm (paretic, non-paretic), load level (25% vs 50% maximal voluntary torque), and dataset (electromyography, load cell, or combined) on classifier performance. Results suggest that paretic arm, lower load levels, and using load cell or EMG data alone reduced classifier accuracy. However, this method still shows promise. Further work will examine classifier-user interaction during active control of a robotic device and optimization/minimization of sensors.

Original languageEnglish (US)
Article number8908762
Pages (from-to)350-358
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume28
Issue number1
DOIs
StatePublished - Jan 2020

Keywords

  • Linear discriminant analysis
  • pattern recognition
  • robotic therapy
  • stroke

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering

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