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Published, Version of Record (VoR)Open Access Discount via Drexel Libraries Read and Publish Program 2024CC BY V4.0, Open
Journal article
Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning
Sensors (Basel, Switzerland), v 24(23), 7779
05 Dec 2024
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging to detect in real time, as they often occur suddenly and with little warning, highlighting the need for innovative detection methods. This study aimed to assist in the advancement of an accurate and efficient fall detection system using electroencephalogram (EEG) data to recognize the reaction to a postural disturbance. We employed a state-space-based system identification approach to extract features from EEG signals indicative of reactions to postural perturbations and compared its performance with those of traditional autoregressive (AR) and Shannon entropy (SE) methods. Using EEG epochs starting from 80 ms after the onset of the event yielded improved performance compared with epochs that started from the onset. The classifier trained on the EEG data achieved promising results, with a sensitivity of up to 90.9%, a specificity of up to 97.3%, and an accuracy of up to 95.2%. Additionally, a real-time algorithm was developed to integrate the EEG and accelerometer data, which enabled accurate fall detection in under 400 ms and achieved an over 99% accuracy in detecting unexpected falls. This research highlights the potential of using EEG data in conjunction with other sensors for developing more accurate and efficient fall detection systems, which can improve the safety and quality of life for elderly adults and other vulnerable individuals.
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1 citations in Scopus
Details
- Title
- Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning
- Creators
- Zhuo Wang (Corresponding Author) - Drexel UniversityAvia Noah - GraceValentina Graci - Children's Hospital of PhiladelphiaEmily Keshner - GraceMadeline Griffith - Children's Hospital of PhiladelphiaThomas Seacrist - Children's Hospital of PhiladelphiaJohn BurnsIIIOhad GalAllon Guez - Drexel University
- Publication Details
- Sensors (Basel, Switzerland), v 24(23), 7779
- Publisher
- MDPI
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Graduate School of Biomedical Sciences and Professional Studies
- Scopus ID
- 2-s2.0-85212673297
- Other Identifier
- 991022004204304721