Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments
Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–co...
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MDPI AG
2025-04-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/4/235 |
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| author | Yirga Yayeh Munaye Metadel Addis Yenework Belayneh Atinkut Molla Wasyihun Admass |
| author_facet | Yirga Yayeh Munaye Metadel Addis Yenework Belayneh Atinkut Molla Wasyihun Admass |
| author_sort | Yirga Yayeh Munaye |
| collection | DOAJ |
| description | Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–computer interaction. However, several challenges arise in outdoor settings, including varying lighting conditions, occlusions caused by obstacles, environmental noise, and the complexity of differentiating between similar activities. This study presents a hybrid deep learning approach that integrates human activity recognition and localization in outdoor environments using Wi-Fi signal data. The study focuses on applying the hybrid long short-term memory–bi-gated recurrent unit (LSTM-BIGRU) architecture, designed to enhance the accuracy of activity recognition and location estimation. Moreover, experiments were conducted using a real-world dataset collected with the PicoScene Wi-Fi sensing device, which captures both magnitude and phase information. The results demonstrated a significant improvement in accuracy for both activity recognition and localization tasks. To mitigate data scarcity, this study utilized the conditional tabular generative adversarial network (CTGAN) to generate synthetic channel state information (CSI) data. Additionally, carrier frequency offset (CFO) and cyclic shift delay (CSD) preprocessing techniques were implemented to mitigate phase fluctuations. The experiments were conducted in a line-of-sight (LoS) outdoor environment, where CSI data were collected using the PicoScene Wi-Fi sensor platform across four different activities at outdoor locations. Finally, a comparative analysis of the experimental results highlights the superior performance of the proposed hybrid LSTM-BIGRU model, achieving 99.81% and 98.93% accuracy for activity recognition and location prediction, respectively. |
| format | Article |
| id | doaj-art-c126412a4df14a7d9208343bb789a5bf |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-c126412a4df14a7d9208343bb789a5bf2025-08-20T02:17:19ZengMDPI AGAlgorithms1999-48932025-04-0118423510.3390/a18040235Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor EnvironmentsYirga Yayeh Munaye0Metadel Addis1Yenework Belayneh2Atinkut Molla3Wasyihun Admass4Department of Information Technology, Injibara University, Injibara 40, EthiopiaDepartment of Computer Science, Dire Dawa University, Dire Dawa 1362, EthiopiaDepartment of Information Technology, Injibara University, Injibara 40, EthiopiaDepartment of Information Technology, Injibara University, Injibara 40, EthiopiaSchool of Software Engineering, University of Science and Technology of China, Suzhou 215123, ChinaActivity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–computer interaction. However, several challenges arise in outdoor settings, including varying lighting conditions, occlusions caused by obstacles, environmental noise, and the complexity of differentiating between similar activities. This study presents a hybrid deep learning approach that integrates human activity recognition and localization in outdoor environments using Wi-Fi signal data. The study focuses on applying the hybrid long short-term memory–bi-gated recurrent unit (LSTM-BIGRU) architecture, designed to enhance the accuracy of activity recognition and location estimation. Moreover, experiments were conducted using a real-world dataset collected with the PicoScene Wi-Fi sensing device, which captures both magnitude and phase information. The results demonstrated a significant improvement in accuracy for both activity recognition and localization tasks. To mitigate data scarcity, this study utilized the conditional tabular generative adversarial network (CTGAN) to generate synthetic channel state information (CSI) data. Additionally, carrier frequency offset (CFO) and cyclic shift delay (CSD) preprocessing techniques were implemented to mitigate phase fluctuations. The experiments were conducted in a line-of-sight (LoS) outdoor environment, where CSI data were collected using the PicoScene Wi-Fi sensor platform across four different activities at outdoor locations. Finally, a comparative analysis of the experimental results highlights the superior performance of the proposed hybrid LSTM-BIGRU model, achieving 99.81% and 98.93% accuracy for activity recognition and location prediction, respectively.https://www.mdpi.com/1999-4893/18/4/235outdoor environmenthuman activitydeep learningWi-Fi signals |
| spellingShingle | Yirga Yayeh Munaye Metadel Addis Yenework Belayneh Atinkut Molla Wasyihun Admass Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments Algorithms outdoor environment human activity deep learning Wi-Fi signals |
| title | Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments |
| title_full | Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments |
| title_fullStr | Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments |
| title_full_unstemmed | Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments |
| title_short | Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments |
| title_sort | hybrid deep learning methods for human activity recognition and localization in outdoor environments |
| topic | outdoor environment human activity deep learning Wi-Fi signals |
| url | https://www.mdpi.com/1999-4893/18/4/235 |
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