Identifying fall risk using machine learning algorithms
AssigneeZibrio Inc.
InventorsKatharine Forth, Erez Lieberman Aiden
Life of this patent
Estimating the term…
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.
Claims20
A method, comprising:
- receiving, by a processor, a plurality of load data points over a period of time from at least two load detecting modules;
- and
- calculating center of pressure (COP) data based, at least in part, on the plurality of load data points;
- determining a plurality of posture states based, at least in part, on the center of pressure (COP) data by applying a machine learning algorithm to the COP data;
- calculating one or more base punctuated equilibrium model (PEM) stability metrics based, at least in part, on the plurality of posture states, wherein the base punctuated equilibrium model (PEM) stability metrics comprise metrics corresponding to a presence of a plurality of postural states;
- calculating one or more advanced punctuated equilibrium model (PEM) stability metrics based, at least in part, on the plurality of posture states, wherein the advanced punctuated equilibrium model (PEM) stability metrics comprise metrics corresponding to a relationship between the plurality of postural states;
- and
- determining a balance score based, at least in part, on the one or more base punctuated equilibrium model (PEM) stability metrics and on the one or more advanced punctuated equilibrium model (PEM) stability metrics.
A system for determining postural stability and fall risk of a person, comprising:
- two or more load detecting modules configured to acquire a plurality of load data points;
- a data analysis module configured to analyze the plurality of load data points received from the two or more load detecting modules, wherein the data analysis module is configured to perform steps comprising:
- calculating center of pressure (COP) data based, at least in part, on the plurality of load data points;
- determining a plurality of posture states based, at least in part, on the center of pressure (COP) data by applying a machine learning algorithm to the COP data;
- calculating one or more base punctuated equilibrium model (PEM) stability metrics based, at least in part, on the plurality of posture states, wherein the base punctuated equilibrium model (PEM) stability metrics comprise metrics corresponding to a presence of a plurality of postural states;
- calculating one or more advanced punctuated equilibrium model (PEM) stability metrics based, at least in part, on the plurality of posture states, wherein the advanced punctuated equilibrium model (PEM) stability metrics comprise metrics corresponding to a relationship between the plurality of postural states;
- and
- determining a balance score based, at least in part, on the one or more base punctuated equilibrium model (PEM) stability metrics and on the one or more advanced punctuated equilibrium model (PEM) stability metrics.
Dependencies are inferred from the claim text.