Human activity and transition detection
US11271629B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 27, 2018 |
| Grant date | Mar 8, 2022 |
| Priority date | — |
| Expiry date | Feb 27, 2038 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W48/12
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A system that can determine states of human activity and transitions between those states using wireless signal data. A machine learning model such as a Hidden Markov Model (HMM) may be trained to determine transitions between states of human activity (e.g., static, slow movement, fast movement) using information from wireless signal data, such as channel state information gathered from Wi-Fi signal beacons. Depending on the state of the human activity the system may then cause certain commands to be executed corresponding to the human activity such as turning on a certain configuration of lights, playing certain music, or the like.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.