ANN-based performance analysis on human activity recognition
In the Big Data era, where various devices can connect to each other through network and cloud services, a smartphone has numerous sensors that can detect data about everything around it. This makes the identifying-process activity (AR) applications and behavior aware of the context. In this paper,...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/28646/ http://umpir.ump.edu.my/id/eprint/28646/1/ANN-based%20Performance%20Analysis%20on%20Human%20Activity%20Recognition1.pdf |
| _version_ | 1848823104278626304 |
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| author | Elzein, Nahla Mohammed Fakherldin, Mohammed Abaker, Ibrahim Mazlina, Abdul Majid |
| author_facet | Elzein, Nahla Mohammed Fakherldin, Mohammed Abaker, Ibrahim Mazlina, Abdul Majid |
| author_sort | Elzein, Nahla Mohammed |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | In the Big Data era, where various devices can connect to each other through network and cloud services, a smartphone has numerous sensors that can detect data about everything around it. This makes the identifying-process activity (AR) applications and behavior aware of the context. In this paper, we used an algorithm to predict a person's activity based on the collected sensor data. Also, Principal Component Analysis(PCA) is applied to the 561 features of the dataset. PCA reduced the dimensions of the dataset from 561 to 50, decreasing the complexity of the data. Therefore, a number of important features are identified out of the 561 features. Consequently, the neural network outperforms the HF-SVM. The HF-SVM was chosen because it requires less memory, computational power, and battery consumption. This study suggests minimizing the resources used by the neural network or exploring another classification algorithm to achieve comparable results with fewer resources. |
| first_indexed | 2025-11-15T02:51:50Z |
| format | Conference or Workshop Item |
| id | ump-28646 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:51:50Z |
| publishDate | 2020 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-286462025-05-30T09:05:00Z http://umpir.ump.edu.my/id/eprint/28646/ ANN-based performance analysis on human activity recognition Elzein, Nahla Mohammed Fakherldin, Mohammed Abaker, Ibrahim Mazlina, Abdul Majid QA75 Electronic computers. Computer science In the Big Data era, where various devices can connect to each other through network and cloud services, a smartphone has numerous sensors that can detect data about everything around it. This makes the identifying-process activity (AR) applications and behavior aware of the context. In this paper, we used an algorithm to predict a person's activity based on the collected sensor data. Also, Principal Component Analysis(PCA) is applied to the 561 features of the dataset. PCA reduced the dimensions of the dataset from 561 to 50, decreasing the complexity of the data. Therefore, a number of important features are identified out of the 561 features. Consequently, the neural network outperforms the HF-SVM. The HF-SVM was chosen because it requires less memory, computational power, and battery consumption. This study suggests minimizing the resources used by the neural network or exploring another classification algorithm to achieve comparable results with fewer resources. IEEE 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28646/1/ANN-based%20Performance%20Analysis%20on%20Human%20Activity%20Recognition1.pdf Elzein, Nahla Mohammed and Fakherldin, Mohammed and Abaker, Ibrahim and Mazlina, Abdul Majid (2020) ANN-based performance analysis on human activity recognition. In: IEEE 4th International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE2020) , 27-29 November 2019 , Kedah, Malaysia. pp. 1-6.. ISBN 978-1-7281-2610-4 (Published) https://doi.org/10.1109/ICRAIE47735.2019.9037749 |
| spellingShingle | QA75 Electronic computers. Computer science Elzein, Nahla Mohammed Fakherldin, Mohammed Abaker, Ibrahim Mazlina, Abdul Majid ANN-based performance analysis on human activity recognition |
| title | ANN-based performance analysis on human activity recognition |
| title_full | ANN-based performance analysis on human activity recognition |
| title_fullStr | ANN-based performance analysis on human activity recognition |
| title_full_unstemmed | ANN-based performance analysis on human activity recognition |
| title_short | ANN-based performance analysis on human activity recognition |
| title_sort | ann-based performance analysis on human activity recognition |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/28646/ http://umpir.ump.edu.my/id/eprint/28646/ http://umpir.ump.edu.my/id/eprint/28646/1/ANN-based%20Performance%20Analysis%20on%20Human%20Activity%20Recognition1.pdf |