| 摘要 |
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. |
| 主权项 |
1. 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 estimating, by the processor, a fall risk by applying a machine learning algorithm to the plurality of load data points, wherein the step of estimating the fall risk comprises:
calculating center of pressure (COP) data based, at least in part, on the plurality of load data points;determining a plurality of posture states identified with Hidden Markov Model techniques based, at least in part, on the center of pressure (COP) data;calculating one or more base punctuated equilibrium model (PEM) stability metrics based, at least in part, on the plurality of posture states;calculating one or more advanced punctuated equilibrium model (PEM) stability metrics based, at least in part, on the plurality of posture states; anddetermining the fall risk 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. |
