Research on fall risk prediction method based on electrostatic gait signals

Falls are one of the most serious health risk issues facing the elderly worldwide, which have a serious impact on the physical and mental health and quality of life of the elderly. Fall risk prediction can help develop targeted fall prevention programs that can help reduce the incidence of falls in the elderly. This paper proposed a fall risk prediction method based on electrostatic gait signals, measured the electrostatic gait signals of three types of people with different fall risks, and extracted gait time parameters, gait symmetry features, and gait variability features. The dimensionality of the dataset was reduced through the hybrid feature dimensionality reduction method based on the particle swarm optimization algorithm, and a fall risk prediction model was constructed based on the SVM algorithm, with the model accuracy reaching 96.77%. Methods of this paper have the advantages of simple equipment layout and non-invasive measurement, and can effectively predict the risk of falls, reduce the incidence of falls in the elderly, and improve the survival rate and quality of life of the elderly.