Tanveer Abbas Gadehi and Faheem Yar Khuhawar and Ahmed Memon and Kashif Nisar (2018) Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition.
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Smart phone sensor data, comparative analysis of various classification methods for task of human activity recognition.ABSTRACT.pdf Download (53kB) |
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Smart Phone Sensor Data, Comparative Analysis of Various Classification Methods for Task of Human Activity Recognition.pdf Restricted to Registered users only Download (1MB) | Request a copy |
Abstract
Human Activity Recognition has a long history of research and requires further exploration to produce useful and optimal outcomes. Areas such as medicine, daily routine, and security are some benefits that smartphone enables via embedded sensors. Our work has chosen sensor data of six activities such as standing, walking, laying from pre-recorded dataset gathered via smartphone to evaluate the performance of various supervised machine learning algorithms. The results suggest that logistic regression has been an optimal choice based on experiments. Whereas, the Support Vector Machine (SVM) has shown to perform well with ninety-five percentage accuracy.
Item Type: | Proceedings |
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Keyword: | HAR , Classification , RNN , Ensemble , SVM , Machine , Learning , Activity recognition |
Subjects: | Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software |
Department: | FACULTY > Faculty of Computing and Informatics |
Depositing User: | DG MASNIAH AHMAD - |
Date Deposited: | 04 Aug 2022 07:18 |
Last Modified: | 04 Aug 2022 07:18 |
URI: | https://eprints.ums.edu.my/id/eprint/33447 |
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