- author = "James Alexander Hughes and Joseph Alexander Brown and Adil Mehmood Khan and Asad Masood Khattak and Mark Daley",
- booktitle = "2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)",
- title = "Analysis of symbolic models of biometrie data and their use for action and user identification",
- year = "2018",
- month = "30 " # may # "-2 " # jun,
- address = "St. Louis, MO, USA",
- keywords = "genetic algorithms, genetic programming, Biomechanics, Gait Recognition, Kinetics, Symbolic Regression, Smartwatch, Identification",
-
DOI = "
doi:10.1109/CIBCB.2018.8404969",
- size = "8 pages",
- abstract = "Smart devices are becoming an extension of ourselves that contain sensitive information and are often targeted for theft. The development of an intelligent and reliable means of user identification and authentication is critical. Not only can the development of user models performing tasks be used for user and task identification, but systems can also notify individuals if there is a potential health concern. The construction of an idealized model of human locomotion may give medical care providers a better understanding of individual differences and guide therapy and treatment. Data was gathered from a smart watch worn by six subjects performing five different tasks and Genetic Programming was used to perform symbolic regression - a model free, nonlinear type of regression analysis. Symbolic regression was applied to smartwatch data and a collection of nonlinear closed form symbolic mathematical models were generated. Not only did these models fit the data well, but they provided insight into the underlying system. With only 5 seconds of unseen data, the models could classify which subjects were performing which task with 83.9percent accuracy when chance was only 3.33percent.",
-
notes = "INSPEC Accession Number: 17898966
University of Western Ontario, London, Ontario, Canada
Also known as \cite{8404969}",
