Algorithmically Recognizing Gait Variance from a Sensor-Based System

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. Following data collection, a series of algorithms computes statistics off the gait cycles. In addition to previously validated biometric indicators of vascular dementia, including stride length, time in stride and swing phases of gait, time in dual leg vs single leg support, this system also examines metrics surrounding balance, lateral movement, and fine-grained gait analysis during critical transition periods of gait, when weight is transferred from one leg to the other. Secondly, 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.