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Symposium

Stroke

Health Services Research

Technology

HOT TOPICS: Artificial Intelligence and Wearable-based Motor Assessment: Future Applications and Clinician Perspectives in Stroke Rehabilitation 9595

Saturday, November 2, 2024
8:15 AM – 9:15 AM  
Location:  ROOM: Obelisk A REGION: Atrium II Mezzanine Level >>> DIRECTIONS: Exit Atrium Elevators. Take a right and proceed down the corridor. Continue past the escalators and the row of rooms on your left. At the end of the row of rooms, turn down the corridor on the right. Obelisk A is the first room on the left. If coming from the Tower Mezzanine, from the Tower Elevators, turn right. Proceed down the stairs and take the first corridor on your left. Obelisk A is the first room on the left.

    Corresponding Presenter(s)

  • Elena V. Donoso Brown, n/a

    Associate Professor
    Duquesne University
    Pittsburgh, Pennsylvania, United States

    • Presenter(s)

  • Mary Ellen Stoykov, PhD, OTR/L

    Research Scientist II / Research Associate Professor
    Shirley Ryan Ability Lab and Northwestern Dept. of Physical Medicine and Rehabilitation
    Chicago, IL, United States

  • Sunghoon Ivan Ivan Lee, PhD

    Associate Professor
    University of Massachusetts Amherst
    Amherst, Massachusetts, United States

  • HC

    Hannah Clark (she/her/hers)

    Occupational Therapy Student
    Duquesne University
    Pittsburgh, Pennsylvania, United States

    • Non-Presenting Contributor(s)

  • ML

    Min Hun Lee

    Assistant Professor
    Singapore Management University
    Singapore, Singapore

  • Kasey Stepansky

    Clinical Assistant Professor
    Duquesne University
    Bethel Park, PA, United States

Current opportunities created by the increase in artificial intelligence have the potential to move the field forward. In this symposium, we report on the use of artificial intelligence in post-stroke rehabilitation as well as clinician perception of current use. The development of sensors to monitor post-stroke upper limb movement has been reported in the literature. Assessing movement with sensors can reduce variability and allow for smaller sample sizes in clinical trials. Wearable sensors provide an opportunity for clinicians to continuously monitor upper limb activity outside of the clinic which will hopefully advance the creation of individually tailored upper limb intervention. We will discuss a sampling of behavioral intervention technologies currently being examined in individuals living with stroke. Finally, we will describe clinicians’ perception of artificial intelligence, which will provide more knowledge regarding its clinical implementation in stroke rehabilitation.

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Learning Objectives:

  • Describe two common artificial intelligence and machine learning applications.
  • Explain clinician perspectives on key factors needed to create usable AI applications.
  • Describe at least one potential application of machine learning and AI for motor rehabilitation post-stroke
  • Describe two common artificial intelligence and machine learning applications.
Disclosure(s):
Mary Ellen Stoykov, PhD, OTR/L: No financial relationships to disclose
Min Hun Lee: No financial relationships to disclose
Kasey Stepansky: No financial relationships to disclose