UbiStroke: Multimodal Sensing of Stroke-Related Deficits

Wed, 01 Jan 2020 00:00:00 +0000

Overview

Stroke assessment rests on human observation. Diagnosing a stroke quickly enough to treat it requires a specialist, and the deficit scales in clinical use record symptoms categorically, which makes outcome prediction crude: existing scales are generally unable to say whether a patient will do well or very poorly.

UbiStroke develops techniques to identify and quantify stroke-related deficits from multiple sensors at once, producing a per-patient stroke signature from body posture, facial droop, pupil tracking, speech patterns, and other symptoms. Machine learning models trained on this data detect stroke-associated hemiparesis and support the clinician rather than replacing the exam.

The work evaluated UbiStroke alongside the clinician-performed NIHSS exam, and studied how clinicians rely on a computational aid during an acute assessment.

Funding and External Collaborations

UbiStroke is a collaboration with the UCSD Stroke Center, the UCSD Bioengineering Department, the Institute of Neural Computation, and HomniHealth. Research leading to UbiStroke has been funded by the National Science Foundation, the NSF I-Corps, the National Library of Medicine at NIH, IBM Research, and UCSD’s Office of Research Affairs.

Holo-Stroke: Assessing for Immersive Stroke Care Through Stroke Hologram Teleportation

Tue, 01 Oct 2024 00:00:00 +0000

Abstract P307: The ALPHA Sign in the Diagnosis of Potential Stroke

Fri, 01 Jan 2021 00:00:00 +0000

Assessing Clinicians' Reliance on Computational Aids for Acute Stroke Diagnosis

Wed, 01 Jan 2020 00:00:00 +0000

Developing aids to assist acute stroke diagnosis

Wed, 01 Jan 2020 00:00:00 +0000

Stroke-associated hemiparesis detection using body joints and support vector machines

Mon, 01 Jan 2018 00:00:00 +0000

Exploring stroke-associated hemiparesis assessment with support vector machines

Sun, 01 Jan 2017 00:00:00 +0000

Vish Ramesh | HXI - Human-centered eXtended Intelligence

UbiStroke: Multimodal Sensing of Stroke-Related Deficits

Overview

Funding and External Collaborations

Holo-Stroke: Assessing for Immersive Stroke Care Through Stroke Hologram Teleportation

Abstract P307: The ALPHA Sign in the Diagnosis of Potential Stroke

Assessing Clinicians' Reliance on Computational Aids for Acute Stroke Diagnosis

Developing aids to assist acute stroke diagnosis

Stroke-associated hemiparesis detection using body joints and support vector machines

Exploring stroke-associated hemiparesis assessment with support vector machines