IDENTIFYING FALL RISK USING MACHINE LEARNING ALGORITHMS

Title:

IDENTIFYING FALL RISK USING MACHINE LEARNING ALGORITHMS

Document Type and Number:

WIPO Patent Application WO/2017/004240

Kind Code:

A8

Abstract:

A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.

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Inventors:

FORTH KATHARINE (US)
AIDEN EREZ LIEBERMAN (US)

Application Number:

PCT/US2016/040153

Publication Date:

August 03, 2017

Filing Date:

June 29, 2016

Export Citation:

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Assignee:

ISHOE INC (US)

International Classes:

G01M1/00; G06N20/00

Attorney, Agent or Firm:

SMITH, Darren et al. (US)

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