M. Maashi, M. Al-Hagery, Mohammed Rizwanullah, A. Osman
{"title":"基于感官模态数据的视障人士使用非洲秃鹫优化和深度学习的自动手势识别","authors":"M. Maashi, M. Al-Hagery, Mohammed Rizwanullah, A. Osman","doi":"10.57197/jdr-2023-0019","DOIUrl":null,"url":null,"abstract":"Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.","PeriodicalId":46073,"journal":{"name":"Scandinavian Journal of Disability Research","volume":"4 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data\",\"authors\":\"M. Maashi, M. Al-Hagery, Mohammed Rizwanullah, A. Osman\",\"doi\":\"10.57197/jdr-2023-0019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.\",\"PeriodicalId\":46073,\"journal\":{\"name\":\"Scandinavian Journal of Disability Research\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scandinavian Journal of Disability Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.57197/jdr-2023-0019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"REHABILITATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scandinavian Journal of Disability Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.57197/jdr-2023-0019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REHABILITATION","Score":null,"Total":0}
Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data
Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.