Fall Detection Method based on a Human Electrostatic Field and VMD-ECANet Architecture.

Falls are one of the most serious health risks faced by older adults worldwide, and they can have a significant impact on their physical and mental well-being as well as their quality of life. Detecting falls promptly and accurately and providing assistance can effectively reduce the harm caused by falls to older adults. This paper proposed a noncontact fall detection method based on the human electrostatic field and a VMD-ECANet framework. An electrostatic measurement system was used to measure the electrostatic signals of four types of falling postures and five types of daily actions. The signals were randomly divided in proportion and by individuals to construct a training set and test set. A fall detection model based on the VMD-ECA network was proposed that decomposes electrostatic signals into modal component signals using the variational mode decomposition (VMD) technique. These signals were then fed into a multichannel convolutional neural network for feature extraction. Information fusion was achieved through the efficient channel attention network (ECANet) module. Finally, the extracted features were input into a classifier to obtain the output results. The constructed model achieved an accuracy of 96.44%. The proposed fall detection solution has several advantages, including being noncontact, cost-effective, and privacy friendly. It is suitable for detecting indoor falls by older individuals living alone and helps to reduce the harm caused by falls.