Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7779 |
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| author | Zhuo Wang Avia Noah Valentina Graci Emily A. Keshner Madeline Griffith Thomas Seacrist John Burns Ohad Gal Allon Guez |
| author_facet | Zhuo Wang Avia Noah Valentina Graci Emily A. Keshner Madeline Griffith Thomas Seacrist John Burns Ohad Gal Allon Guez |
| author_sort | Zhuo Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b069397304e84cf5bcca413c67907929 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b069397304e84cf5bcca413c679079292025-08-20T02:50:37ZengMDPI AGSensors1424-82202024-12-012423777910.3390/s24237779Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine LearningZhuo Wang0Avia Noah1Valentina Graci2Emily A. Keshner3Madeline Griffith4Thomas Seacrist5John Burns6Ohad Gal7Allon Guez8Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USAGraceFall, Inc., Penn Valley, PA 19702, USACenter for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USAGraceFall, Inc., Penn Valley, PA 19702, USACenter for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USACenter for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USACenter for Injury Research and Prevention, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USAGraceFall, Inc., Penn Valley, PA 19702, USADepartment of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USAMillions 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.https://www.mdpi.com/1424-8220/24/23/7779fall detectionEEGsystem identificationelderly adultssensor fusion |
| spellingShingle | Zhuo Wang Avia Noah Valentina Graci Emily A. Keshner Madeline Griffith Thomas Seacrist John Burns Ohad Gal Allon Guez Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning Sensors fall detection EEG system identification elderly adults sensor fusion |
| title | Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning |
| title_full | Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning |
| title_fullStr | Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning |
| title_full_unstemmed | Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning |
| title_short | Real-Time Postural Disturbance Detection Through Sensor Fusion of EEG and Motion Data Using Machine Learning |
| title_sort | real time postural disturbance detection through sensor fusion of eeg and motion data using machine learning |
| topic | fall detection EEG system identification elderly adults sensor fusion |
| url | https://www.mdpi.com/1424-8220/24/23/7779 |
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