Human Activity Recognition through Smartphone Inertial Sensors with ML Approach
Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be...
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2024-02-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6586 |
| _version_ | 1797319273294594048 |
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| author | Munid Alanazi Raghdah Saem Aldahr Mohammad Ilyas |
| author_facet | Munid Alanazi Raghdah Saem Aldahr Mohammad Ilyas |
| author_sort | Munid Alanazi |
| collection | DOAJ |
| description |
Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be performed in response to the output of these HAR systems. The number of smartphone users increases, whereas the sensors are widely available in different sizes and shapes (internal or external sensors). Recent advances in sensor technology and machine learning have led researchers to conduct studies on sensor technology such as HAR. HAR systems typically use a combination of sensors, such as accelerometers, gyroscopes, and cameras, to collect images or signal data that can be classified by machine learning algorithms. HAR research has focused on several key challenges including dealing with variability in sensor data, handling missing data or noise, and dealing with large amounts of sensor-generated data. In this work, several machine learning algorithms were tested in predefined settings using the KU-HAR dataset in a series of experiments. Subsequently, various performance metrics were calculated to assess the chosen algorithms’ performance. The experimental findings showed that the LightGBM classifier surpassed the other machine learning algorithms in performance metrics, such as accuracy, F1 score, precision, and recall. Although Gradient Boosting has lengthy training time, the other classifiers complete their training in an acceptable time period.
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| first_indexed | 2024-03-08T04:04:31Z |
| format | Article |
| id | doaj.art-407676e14176455796f18daa821d09ea |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-08T04:04:31Z |
| publishDate | 2024-02-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-407676e14176455796f18daa821d09ea2024-02-09T06:06:05ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362024-02-0114110.48084/etasr.6586Human Activity Recognition through Smartphone Inertial Sensors with ML ApproachMunid Alanazi0Raghdah Saem Aldahr1Mohammad Ilyas2Department of Electrical Engineering and Computer Science, Florida Atlantic University, USA | Department of Business Informatics, College of Business, King Khalid University, Saudi ArabiaDepartment of Electrical Engineering and Computer Science, Florida Atlantic University, USA | Department of Computer Science, Taibah University, Saudi ArabiaDepartment of Electrical Engineering and Computer Science, Florida Atlantic University, USA Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be performed in response to the output of these HAR systems. The number of smartphone users increases, whereas the sensors are widely available in different sizes and shapes (internal or external sensors). Recent advances in sensor technology and machine learning have led researchers to conduct studies on sensor technology such as HAR. HAR systems typically use a combination of sensors, such as accelerometers, gyroscopes, and cameras, to collect images or signal data that can be classified by machine learning algorithms. HAR research has focused on several key challenges including dealing with variability in sensor data, handling missing data or noise, and dealing with large amounts of sensor-generated data. In this work, several machine learning algorithms were tested in predefined settings using the KU-HAR dataset in a series of experiments. Subsequently, various performance metrics were calculated to assess the chosen algorithms’ performance. The experimental findings showed that the LightGBM classifier surpassed the other machine learning algorithms in performance metrics, such as accuracy, F1 score, precision, and recall. Although Gradient Boosting has lengthy training time, the other classifiers complete their training in an acceptable time period. https://etasr.com/index.php/ETASR/article/view/6586accelerometergyroscopemachine learningsensorsHuman Activity Recognition (HAR) |
| spellingShingle | Munid Alanazi Raghdah Saem Aldahr Mohammad Ilyas Human Activity Recognition through Smartphone Inertial Sensors with ML Approach Engineering, Technology & Applied Science Research accelerometer gyroscope machine learning sensors Human Activity Recognition (HAR) |
| title | Human Activity Recognition through Smartphone Inertial Sensors with ML Approach |
| title_full | Human Activity Recognition through Smartphone Inertial Sensors with ML Approach |
| title_fullStr | Human Activity Recognition through Smartphone Inertial Sensors with ML Approach |
| title_full_unstemmed | Human Activity Recognition through Smartphone Inertial Sensors with ML Approach |
| title_short | Human Activity Recognition through Smartphone Inertial Sensors with ML Approach |
| title_sort | human activity recognition through smartphone inertial sensors with ml approach |
| topic | accelerometer gyroscope machine learning sensors Human Activity Recognition (HAR) |
| url | https://etasr.com/index.php/ETASR/article/view/6586 |
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