Pattern Recognition of Human Activity Based on Smartphone Data Sensors Using SVM Multiclass

Alman, Alman and Lawi, Armin and Tahir, Zulkifli (2019) Pattern Recognition of Human Activity Based on Smartphone Data Sensors Using SVM Multiclass. In: ICOST 2019, 2-3 May 2019, Makassar, Indonesia.

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Abstract

Mobile devices are increasingly sophisticated while smartphones continue to make the latest generation that immerses the supporting tools needed in everyday life such as cameras, GPS, Microphones, and various sensors such as light sensors, a direction sensor, acceleration sensor (i.e., accelerometer) and the gyroscope sensor. This study aims to classify human activities from the accelerometer and gyroscope sensors on a Sony z3+ smartphone. To implement our system, we collect labeled accelerometer and gyroscope data from eight users when they carry out daily activity. Every activity was recorded for 22 seconds, total data that we use every activity is 2000 data with the total amount of data is 16000 data. This data we classify using the Multiclass Support Vector Machine (SVM) method reaches 97.40% accuracy using a 70% ratio as training data and 30% as test data, the classification process takes 5 seconds to classify the data.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: activity recognition accelerometer sensor support vector machine
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 01 Oct 2021 13:44
Last Modified: 01 Oct 2021 13:44
URI: https://eprints.eudl.eu/id/eprint/7310

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