Sensor-based System to Algorithmically Recognize Gait Disturbances Common to Vascular Dementia Onset

Abstract

Early detection of Vascular Dementia onset is challenging in a clinical setting, as the earliest signs often present in physiological aspects. 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 pattern. 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. The proposed system provides a tool for which gait can be analyzed and compared over a long period of time. Such a system opens opportunity to increased personalization in health monitoring and disease diagnosis and provides an avenue to increase patient-centricity of medical care.