Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning
Zhuo Wang, Avia Noah, Valentina Graci, Emily A Keshner, Madeline Griffith, Thomas Seacrist, John Burns, Ohad Gal, Allon Guez, Paolo Benna · 2024
Read Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning by Zhuo Wang, Avia Noah, Valentina Graci, Emily A Keshner, Madeline Griffith, Thomas Seacrist, John Burns, Ohad Gal, Allon Guez, Paolo Benna (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11645098.txt.
Description
Zhuo Wang and co-authors present, in twenty twenty-four, a real-time system to detect postural disturbances by fusing brain and motion data. Falls affect millions of people each year and often strike with no warning. Brain signals, recorded as EEG, show the reaction to a balance disturbance. The team extracted features from EEG with a state-space identification method. They compared it with two classic methods, an autoregressive model and Shannon entropy. Windows that started slightly after the event worked better than windows from the onset.
Classifiers trained on EEG reached high sensitivity, specificity, and accuracy. A real-time algorithm then fused EEG with accelerometer data. It detected unexpected falls in under four hundred milliseconds with accuracy above ninety-nine percent.
Catalog record
| CREATOR | Zhuo Wang, Avia Noah, Valentina Graci, Emily A Keshner, Madeline Griffith, Thomas Seacrist, John Burns, Ohad Gal, Allon Guez, Paolo Benna |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |