Graduation Semester and Year
2017
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Manfred Huber
Abstract
Gait generally refers to the style of walk and is influenced by a number of parameters and conditions. In particular, chronic and temporary health conditions often influence gait patterns. As such conditions increase with age, changes in gait pattern and gait disorders become more common. Changes in the walking pattern in the elderly can suggest neurological problems or age related problems that influence the walk. For example, individuals with parkinsonian and vascular dementias generally display gait disorders. Similarly, short term changes in muscle tone, strength, and overall condition can reflect in gait parameters. Analysis of the gait for abnormal walk can thus serve as a predictor for such neurological disorders or disorders related to age and potentially be used as a means for early detection of the onset of chronic conditions or to help prevent falls in the elderly. In our research we try to build personalized models for individual gait patterns as well as a framework for anomaly detection in order to distinguish individuals based solely on gait parameters and in order to try to detect deviations in walking based on these parameters. In this thesis we use time series data from pressure monitoring floor sensors to real-time segment walking data and separate it from data representing other activities like standing and turning by using unsupervised and supervised learning. We extract spatio-temporal gait parameters from relevant walking segments. We then model walking of individuals based on these parameters to predict deviation in walking pattern using Support Vector Data Descriptor (SVDD) method and One Class Support Vector Machine (OCSVM) for anomaly detection. We apply these models to real walking data from 30 individuals to attempt person identification to demonstrate the feasibility of building personalized models.
Keywords
Activity recognition, Person identification, Anomaly detection, Support vector machines, Gait, One class support vector machines, Support vector data description
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Rama Krishna Reddy, Suhas Mandikal, "PERSON IDENTIFICATION AND ANOMALY DETECTION USING GAIT PARAMETERS EXTRACTED FROM TIME SERIES DATA" (2017). Computer Science and Engineering Theses. 142.
https://mavmatrix.uta.edu/cse_theses/142
Comments
Degree granted by The University of Texas at Arlington