Long-Short Term Neural Network Analysis of Center of Pressure of Gait

Abstract

Detection of vascular dementia in early stages of cognitive impairment is difficult to do in a clinical setting since the earliest changes are often discrete and physiological in nature. One major aspect of this is gait patterns. This project utilizes force-sensing platforms, motion capture, and EMG sensors to unobtrusively collect biometric data from an individual's walking gait patterns. The data parameters gathered were center of pressure, gait phase and end of unloading/toe-ff events. By quantifying and analyzing machine learning algorithms, specifically deep learning time-series based models, onset patterns of vascular dementia are explored with an overarching goal of creating a system that will assist in understanding and diagnosing cases of vascular dementia. The proposed system provides a tool for which gait can be analyzed and compared over a long period of time and opens opportunity to increased personalization in health monitoring and disease diagnosis and provides an avenue to increase patient-centricity of medical care. Since gait is one of the early predictors of vascular dementia, we developed a long short-term neural network to predict the gait variations from which we can predict the onset of vascular dementia.