2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
Elham Rastegari , College of Information Science and Technology, Omaha, NE 68182, USA
Vivien Marmelat , Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE 68182, USA
Lotfollah Najjar , College of Information Science and Technology, Omaha, NE 68182, USA
Dhundy Bastola , College of Information Science and Technology, Omaha, NE 68182, USA
Hesham H. Ali , College of Information Science and Technology, Omaha, NE 68182, USA
Monitoring gait patterns seamlessly and continuously over time provides valuable information that could help physicians diagnose diseases in the early stages. Currently, traditional gait measurement approaches do not support continuous monitoring of gait and focus on collecting limited data points in controlled lab environments. However, with advancements in wireless technology, movement patterns can be recorded using small portable wearable devices. Parkinson's disease (PD) is a progressively disabling neurodegenerative disorder that is affecting gait and posture and consequently leads to higher risk of falling. Several research studies have looked into changes in the gait parameters of PD patients compared to healthy adults. However, there are only few studies with the focus on gait assessment of PD patients in the early stages as compared to patterns associated with patients at advanced stages. In addition, the number of gait-related studies in this domain using accelerometers on ankle is very limited. Knowing which body location could serve as a target place for accelerometers to provide accurate information is a necessary step toward the health assessment of PD patients. The purpose of this study was to evaluate the gait parameters of patients with mild or moderate PD using accelerometers on ankles. A number of gait parameters, including average stride time, stride time variability, stride time symmetry, and oscillation of acceleration in the mediolateral (ML) direction were calculated and compared between PD patients and healthy elderlies. Preliminary results indicate that features extracted from accelerometers on ankles can be effective in differentiating between healthy elderlies and PD patients at mid-stages of disease but less so at earlier stages of disease.
Accelerometers, Protocols, Diseases, Senior citizens, Acceleration, Monitoring, Legged locomotion
E. Rastegari, V. Marmelat, L. Najjar, D. Bastola and H. H. Ali, "Using gait parameters to recognize various stages of Parkinson's disease," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 1647-1651.