Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model
Obesity and laziness are some of the common issues in the majority of the youth today. This has led to the development of a proposed exergaming solution where users can play first-person physical games. This research study not only proposes a solution for physical fitness in the form of a game using...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10025735/ |
| _version_ | 1811163516130820096 |
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| author | Mir Mushhood Afsar Shizza Saqib Mohammad Aladfaj Mohammed Hamad Alatiyyah Khaled Alnowaiser Hanan Aljuaid Ahmad Jalal Jeongmin Park |
| author_facet | Mir Mushhood Afsar Shizza Saqib Mohammad Aladfaj Mohammed Hamad Alatiyyah Khaled Alnowaiser Hanan Aljuaid Ahmad Jalal Jeongmin Park |
| author_sort | Mir Mushhood Afsar |
| collection | DOAJ |
| description | Obesity and laziness are some of the common issues in the majority of the youth today. This has led to the development of a proposed exergaming solution where users can play first-person physical games. This research study not only proposes a solution for physical fitness in the form of a game using wearable sensors but also proposes a multi-purpose system that provides different applications when trained for the domain-specific dataset. Critical tasks of gesture recognition and depiction in virtual reality can be applied to many applications in the domains of crime detection, fitness, healthcare, online learning, and sports. In particular, the proposed system enables a user to perform, detect, and depict different gestures in the virtual reality game. First, the system pre-processes input data by applying a median filter to overcome the anomalies. Then, features are extracted through a convolutional neural network, power spectral density, skewness, and kurtosis methods. Further, the system optimizes different features by using the grey wolf optimization. Lastly, the feature set which is optimized is fed to a recurrent neural network for classification. When Compared to the traditional methods, the suggested system gives better results while being easier to use. The IMSporting behaviors (IMSB) dataset includes badminton and other physical activities, the WISDM dataset includes common locomotor motions, and the ERICA dataset which includes a variety of exercises, were used in the experimentation. According to experimental findings, the suggested approach outperformed current methods, which showed detection accuracies of 85.01%, 88.46%, and 93.18% over the IMSB, WISDM, and ERICA datasets, respectively. |
| first_indexed | 2024-04-10T15:07:32Z |
| format | Article |
| id | doaj.art-7a458ca19b80499aa050438ba890ed58 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T15:07:32Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7a458ca19b80499aa050438ba890ed582023-02-15T00:00:12ZengIEEEIEEE Access2169-35362023-01-0111124601247310.1109/ACCESS.2023.323969210025735Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning ModelMir Mushhood Afsar0https://orcid.org/0000-0001-9256-7597Shizza Saqib1Mohammad Aladfaj2Mohammed Hamad Alatiyyah3Khaled Alnowaiser4https://orcid.org/0000-0003-3980-843XHanan Aljuaid5https://orcid.org/0000-0001-6042-0283Ahmad Jalal6Jeongmin Park7https://orcid.org/0000-0001-8027-0876Department of Computer Science, Air University, Islamabad, PakistanDepartment of Computer Science, Air University, Islamabad, PakistanDepartment of Natural Engineering, College of Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Sciences and Humanities in Aflaj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, Air University, Islamabad, PakistanDepartment of Computer Engineering, Tech University of Korea, Siheung-si, South KoreaObesity and laziness are some of the common issues in the majority of the youth today. This has led to the development of a proposed exergaming solution where users can play first-person physical games. This research study not only proposes a solution for physical fitness in the form of a game using wearable sensors but also proposes a multi-purpose system that provides different applications when trained for the domain-specific dataset. Critical tasks of gesture recognition and depiction in virtual reality can be applied to many applications in the domains of crime detection, fitness, healthcare, online learning, and sports. In particular, the proposed system enables a user to perform, detect, and depict different gestures in the virtual reality game. First, the system pre-processes input data by applying a median filter to overcome the anomalies. Then, features are extracted through a convolutional neural network, power spectral density, skewness, and kurtosis methods. Further, the system optimizes different features by using the grey wolf optimization. Lastly, the feature set which is optimized is fed to a recurrent neural network for classification. When Compared to the traditional methods, the suggested system gives better results while being easier to use. The IMSporting behaviors (IMSB) dataset includes badminton and other physical activities, the WISDM dataset includes common locomotor motions, and the ERICA dataset which includes a variety of exercises, were used in the experimentation. According to experimental findings, the suggested approach outperformed current methods, which showed detection accuracies of 85.01%, 88.46%, and 93.18% over the IMSB, WISDM, and ERICA datasets, respectively.https://ieeexplore.ieee.org/document/10025735/Convolution neural networkexergaminggrey wolf optimizationrecurrent neural networkvirtual realitywearable sensors |
| spellingShingle | Mir Mushhood Afsar Shizza Saqib Mohammad Aladfaj Mohammed Hamad Alatiyyah Khaled Alnowaiser Hanan Aljuaid Ahmad Jalal Jeongmin Park Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model IEEE Access Convolution neural network exergaming grey wolf optimization recurrent neural network virtual reality wearable sensors |
| title | Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model |
| title_full | Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model |
| title_fullStr | Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model |
| title_full_unstemmed | Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model |
| title_short | Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model |
| title_sort | body worn sensors for recognizing physical sports activities in exergaming via deep learning model |
| topic | Convolution neural network exergaming grey wolf optimization recurrent neural network virtual reality wearable sensors |
| url | https://ieeexplore.ieee.org/document/10025735/ |
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