@article{MAKHILLJEAS201712214101,
    title = {Smote and OVO Multiclass Method for Multiple Handheld Placement
Gait Identification on Smartphone’s Accelerometer},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {2},
    pages = {374-382},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.374.382},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.374.382},
    author = {Abdul Rafiez Abdul,Nasir,Norwati and},
    keywords = {SMOTE,OVO,handheld placement,gait identification,smartphone position},
    abstract = {Gait identification has been a well-known type of biometric recognition for many purposes. However,
the usage and its application are still limited due to uncertainty factors that lead to its lack of use. One of the
factors is the position of the smartphone. Current research uses pouch, pocket and other parts of the body but
not handheld. The second factor is the nonstationary data that resemble the person which contains only a few
meaningful dataset for learning purposes. The third factor is the ability of the classifier itself whether is it
efficient enough in tackling the multiclass problem. In this research, investigation on the handheld smartphone
position is proposed. Besides that SMOTE is applied to the dataset to increase its sample data before the
training procedure. For classification, OVO multiclass structure is proposed instead of using a single classifier
algorithm. From the result, it shows that handheld placement of the smartphone is viable for gait recognition.
At the same time, using SMOTE and OVO methods do increase the accuracy of the gait identification.}
    }