Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition
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 suc...
| Main Authors: | , , , |
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| Format: | Proceedings |
| Language: | English English |
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Institute of Electrical and Electronics Engineers
2018
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| Online Access: | https://eprints.ums.edu.my/id/eprint/33447/1/Smart%20phone%20sensor%20data%2C%20comparative%20analysis%20of%20various%20classification%20methods%20for%20task%20of%20human%20activity%20recognition.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/33447/2/Smart%20Phone%20Sensor%20Data%2C%20Comparative%20Analysis%20of%20Various%20Classification%20Methods%20for%20Task%20of%20Human%20Activity%20Recognition.pdf |
| _version_ | 1796911405605060608 |
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| author | Tanveer Abbas Gadehi Faheem Yar Khuhawar Ahmed Memon Kashif Nisar |
| author_facet | Tanveer Abbas Gadehi Faheem Yar Khuhawar Ahmed Memon Kashif Nisar |
| author_sort | Tanveer Abbas Gadehi |
| collection | UMS |
| description | 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. |
| first_indexed | 2024-03-06T03:18:19Z |
| format | Proceedings |
| id | ums.eprints-33447 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:18:19Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | ums.eprints-334472022-08-03T23:18:07Z https://eprints.ums.edu.my/id/eprint/33447/ Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition Tanveer Abbas Gadehi Faheem Yar Khuhawar Ahmed Memon Kashif Nisar QA76.75-76.765 Computer software 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. Institute of Electrical and Electronics Engineers 2018 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/33447/1/Smart%20phone%20sensor%20data%2C%20comparative%20analysis%20of%20various%20classification%20methods%20for%20task%20of%20human%20activity%20recognition.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/33447/2/Smart%20Phone%20Sensor%20Data%2C%20Comparative%20Analysis%20of%20Various%20Classification%20Methods%20for%20Task%20of%20Human%20Activity%20Recognition.pdf 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. https://ieeexplore.ieee.org/document/9772905 |
| spellingShingle | QA76.75-76.765 Computer software Tanveer Abbas Gadehi Faheem Yar Khuhawar Ahmed Memon Kashif Nisar Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition |
| title | Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition |
| title_full | Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition |
| title_fullStr | Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition |
| title_full_unstemmed | Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition |
| title_short | Smart phone sensor data: Comparative analysis of various classification methods for task of human activity recognition |
| title_sort | smart phone sensor data comparative analysis of various classification methods for task of human activity recognition |
| topic | QA76.75-76.765 Computer software |
| url | https://eprints.ums.edu.my/id/eprint/33447/1/Smart%20phone%20sensor%20data%2C%20comparative%20analysis%20of%20various%20classification%20methods%20for%20task%20of%20human%20activity%20recognition.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/33447/2/Smart%20Phone%20Sensor%20Data%2C%20Comparative%20Analysis%20of%20Various%20Classification%20Methods%20for%20Task%20of%20Human%20Activity%20Recognition.pdf |
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