Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope

نویسندگان

چکیده

Human Activity Recognition is technology introduced to recognize human activities. Several technologies that have been applied are Accelerometer sensors, Gyroscope Cameras, and GPS. The selection of the Support Vector Machine algorithm due its capabilities minimize errors in training data sets Curse dimensionality which can estimate parameters as well ability find best hyperplane separates two classes. SVM was originally developed for classification Problem raised if there more than In addition, performance will not optimal large-scale data. Therefore, modification current design needed. An ensemble technique be used combine with bagging algorithm. This study proposes application an classify activities based on accelerometers gyroscope sensors smartphones. total 13725 records 4575 representatives each class. From results overall partition carried out calcification process using algorithm, generated when comparing datasets 80% 20% test from a because it succeeded increasing accuracy, precision, sensitivity.

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ژورنال

عنوان ژورنال: Ilkom Jurnal Ilmiah

سال: 2023

ISSN: ['2087-1716', '2548-7779']

DOI: https://doi.org/10.33096/ilkom.v15i1.1270.107-117