Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems
Posture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging and sup...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/11/410 |
| _version_ | 1827647552806191104 |
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| author | Roseline Oluwaseun Ogundokun Rytis Maskeliūnas Sanjay Misra Robertas Damasevicius |
| author_facet | Roseline Oluwaseun Ogundokun Rytis Maskeliūnas Sanjay Misra Robertas Damasevicius |
| author_sort | Roseline Oluwaseun Ogundokun |
| collection | DOAJ |
| description | Posture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging and support for elderly people in their daily activities in the field of health care. Computer vision and pattern recognition communities are particularly interested in fall automated recognition. Human sensing and artificial intelligence have both paid great attention to human posture detection (HPD). The health status of elderly people can be remotely monitored using human posture detection, which can distinguish between positions such as standing, sitting, and walking. The most recent research identified posture using both deep learning (DL) and conventional machine learning (ML) classifiers. However, these techniques do not effectively identify the postures and overfits of the model overfits. Therefore, this study suggested a deep convolutional neural network (DCNN) framework to examine and classify human posture in health monitoring systems. This study proposes a feature selection technique, DCNN, and a machine learning technique to assess the previously mentioned problems. The InceptionV3 DCNN model is hybridized with SVM ML and its performance is compared. Furthermore, the performance of the proposed system is validated with other transfer learning (TL) techniques such as InceptionV3, DenseNet121, and ResNet50. This study uses the least absolute shrinkage and selection operator (LASSO)-based feature selection to enhance the feature vector. The study also used various techniques, such as data augmentation, dropout, and early stop, to overcome the problem of model overfitting. The performance of this DCNN framework is tested using benchmark Silhouettes of human posture and classification accuracy, loss, and AUC value of 95.42%, 0.01, and 99.35% are attained, respectively. Furthermore, the results of the proposed technology offer the most promising solution for indoor monitoring systems. |
| first_indexed | 2024-03-09T19:20:38Z |
| format | Article |
| id | doaj.art-082f7643c1c8461fac5a22d2c6dd8706 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T19:20:38Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-082f7643c1c8461fac5a22d2c6dd87062023-11-24T03:23:04ZengMDPI AGAlgorithms1999-48932022-11-01151141010.3390/a15110410Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring SystemsRoseline Oluwaseun Ogundokun0Rytis Maskeliūnas1Sanjay Misra2Robertas Damasevicius3Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, LithuaniaFaculty of Informatics, Kaunas University of Technology, 51368 Kaunas, LithuaniaDepartment of Computer Science and Communication, Østfold University College, 1757 Halden, NorwayFaculty of Informatics, Kaunas University of Technology, 51368 Kaunas, LithuaniaPosture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging and support for elderly people in their daily activities in the field of health care. Computer vision and pattern recognition communities are particularly interested in fall automated recognition. Human sensing and artificial intelligence have both paid great attention to human posture detection (HPD). The health status of elderly people can be remotely monitored using human posture detection, which can distinguish between positions such as standing, sitting, and walking. The most recent research identified posture using both deep learning (DL) and conventional machine learning (ML) classifiers. However, these techniques do not effectively identify the postures and overfits of the model overfits. Therefore, this study suggested a deep convolutional neural network (DCNN) framework to examine and classify human posture in health monitoring systems. This study proposes a feature selection technique, DCNN, and a machine learning technique to assess the previously mentioned problems. The InceptionV3 DCNN model is hybridized with SVM ML and its performance is compared. Furthermore, the performance of the proposed system is validated with other transfer learning (TL) techniques such as InceptionV3, DenseNet121, and ResNet50. This study uses the least absolute shrinkage and selection operator (LASSO)-based feature selection to enhance the feature vector. The study also used various techniques, such as data augmentation, dropout, and early stop, to overcome the problem of model overfitting. The performance of this DCNN framework is tested using benchmark Silhouettes of human posture and classification accuracy, loss, and AUC value of 95.42%, 0.01, and 99.35% are attained, respectively. Furthermore, the results of the proposed technology offer the most promising solution for indoor monitoring systems.https://www.mdpi.com/1999-4893/15/11/410human posture detectiondeep convolutional neural networkdeep learningmachine learningtransfer learning |
| spellingShingle | Roseline Oluwaseun Ogundokun Rytis Maskeliūnas Sanjay Misra Robertas Damasevicius Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems Algorithms human posture detection deep convolutional neural network deep learning machine learning transfer learning |
| title | Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems |
| title_full | Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems |
| title_fullStr | Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems |
| title_full_unstemmed | Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems |
| title_short | Hybrid InceptionV3-SVM-Based Approach for Human Posture Detection in Health Monitoring Systems |
| title_sort | hybrid inceptionv3 svm based approach for human posture detection in health monitoring systems |
| topic | human posture detection deep convolutional neural network deep learning machine learning transfer learning |
| url | https://www.mdpi.com/1999-4893/15/11/410 |
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