Wang Zhuo, Noah Avia, Graci Valentina, Keshner Emily A, Griffith Madeline, Seacrist Thomas, Burns John, Gal Ohad, Guez Allon
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA.
Center for Injury Research and Prevention, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Sensors (Basel). 2024 Dec 5;24(23):7779. doi: 10.3390/s24237779.
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.
全球每年有数百万人受到跌倒的影响，这使其成为一个重大的公共卫生问题。跌倒是特别难以实时检测的，因为它们通常突然发生且几乎没有预警，这凸显了对创新检测方法的需求。本研究旨在通过使用脑电图（EEG）数据来识别对姿势干扰的反应，协助推进一种准确高效的跌倒检测系统。我们采用基于状态空间的系统识别方法从EEG信号中提取表示对姿势扰动反应的特征，并将其性能与传统的自回归（AR）和香农熵（SE）方法的性能进行比较。与从事件开始时起的脑电周期相比，使用从事件开始后80毫秒起的脑电周期可提高性能。在EEG数据上训练的分类器取得了有前景的结果，灵敏度高达90.9%，特异性高达97.3%，准确率高达95.2%。此外，还开发了一种实时算法来整合EEG和加速度计数据，该算法能够在400毫秒内准确检测跌倒，并且在检测意外跌倒时准确率超过99%。这项研究突出了将EEG数据与其他传感器结合使用以开发更准确高效的跌倒检测系统的潜力，这可以提高老年人和其他弱势群体的安全性和生活质量。
