Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model
Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscope...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/1/317 |
| _version_ | 1828151279793209344 |
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| author | Nadeem Ahmed Jahir Ibna Rafiq Md Rashedul Islam |
| author_facet | Nadeem Ahmed Jahir Ibna Rafiq Md Rashedul Islam |
| author_sort | Nadeem Ahmed |
| collection | DOAJ |
| description | Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification. |
| first_indexed | 2024-04-11T21:54:31Z |
| format | Article |
| id | doaj.art-e01b60b3e5b242ddba98a5466eae676d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:54:31Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e01b60b3e5b242ddba98a5466eae676d2022-12-22T04:01:09ZengMDPI AGSensors1424-82202020-01-0120131710.3390/s20010317s20010317Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection ModelNadeem Ahmed0Jahir Ibna Rafiq1Md Rashedul Islam2Centre for Higher Studies and Research, Bangladesh University of Professionals, Mirpur Cantonment, Dhaka-1216, BangladeshDepartment of Computer Science and Engineering, University of Asia Pacific, 74/A, Green Road, Dhaka-1205, BangladeshSchool of Computer Science and Engineering, University of Aizu, Fukushima 965-8580, JapanHuman activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification.https://www.mdpi.com/1424-8220/20/1/317human activity recognition (har)feature selectionmachine learningsvmsensoraccelerometergyroscope |
| spellingShingle | Nadeem Ahmed Jahir Ibna Rafiq Md Rashedul Islam Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model Sensors human activity recognition (har) feature selection machine learning svm sensor accelerometer gyroscope |
| title | Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model |
| title_full | Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model |
| title_fullStr | Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model |
| title_full_unstemmed | Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model |
| title_short | Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model |
| title_sort | enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model |
| topic | human activity recognition (har) feature selection machine learning svm sensor accelerometer gyroscope |
| url | https://www.mdpi.com/1424-8220/20/1/317 |
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