Identifying fall risk using machine learning algorithms
US10307084B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 29, 2016 |
| Grant date | Jun 4, 2019 |
| Priority date | — |
| Expiry date | Jun 10, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H20/30
- WIPO fieldMedical technology
- WIPO sectorInstruments
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.