Visual Concurrent Analysis of Gait Patterns Among Healthy Young, Old Adults, and Patients With Parkinson’s Disease

The ability to detect gait in Parkinson’s disease (PD) is useful for the diagnosis of PD and monitoring its progress. For the first time, based on an artificial intelligence-based notion of graphical temporal recurrence modeling of nonlinear systems, this paper presents a visual concurrent analysis of gait dynamics for detecting visual changes in patterns among young, old, and PD subjects. More specifically, the analysis is based on tensor decomposition of fuzzy recurrence plots of wearable sensor-induced stride signals. After validating the proposed approach using time series of known systems, results obtained for the analysis of gait patterns among the three cohorts suggest the combined methods as a potential computerized health data analysis tool for early recognition, assessment, and management of PD in either clinical or home environments.