Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network
Abstract The correct sitting posture in a wheelchair is crucial for paralyzed people. This helps prevent problems such as pressure ulcers, muscle contractures, and respiratory problems. A paralyzed person with poor sitting posture is highly likely to slip out of their wheelchair. To prevent this fro...
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-04381-x |
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| author | Vijaya Gunturu J. Kavitha Swapna Thouti N. K. Senthil Kumar Kamal Poon Ayman A. Alharbi Amar Y. Jaffar V. Saravanan |
| author_facet | Vijaya Gunturu J. Kavitha Swapna Thouti N. K. Senthil Kumar Kamal Poon Ayman A. Alharbi Amar Y. Jaffar V. Saravanan |
| author_sort | Vijaya Gunturu |
| collection | DOAJ |
| description | Abstract The correct sitting posture in a wheelchair is crucial for paralyzed people. This helps prevent problems such as pressure ulcers, muscle contractures, and respiratory problems. A paralyzed person with poor sitting posture is highly likely to slip out of their wheelchair. To prevent this from happening and consistently maintain paralyzed individuals under observation, a new model, the Emperor Penguin Optimized Sensor-Infused Wheelchair (EPIC), has been designed to monitor the position and health of the individual in the wheelchair in real-time. A Force Sensitive Resistor (FSR) sensor and an ultrasonic sensor continuously transmit information to the Arduino UNO R4 Wi-Fi board. The Emperor Penguin Optimizer Algorithm (EPOA) was used to select the features sent from the Arduino board to the ESP8266-Wi-Fi module. A Deep Maxout Network (DMN) was used to predict the posture of a wheelchair-using patient following the feature selection phase. A mobile application for Android collects data from the ESP32 module and estimates posture to inform the caretaker about the user’s current posture and health status. Evaluation metrics such as precision, accuracy, sensitivity, and specificity have been used to determine the efficiency in the EPIC framework, which improves overall accuracy by 10.1%, 7.73%, and 2.84% for better posture recognition. |
| format | Article |
| id | doaj-art-76010e2dc38741a98dd27201a4405eb4 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-76010e2dc38741a98dd27201a4405eb42025-08-20T02:05:46ZengNature PortfolioScientific Reports2045-23222025-06-0115111910.1038/s41598-025-04381-xMulti sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout NetworkVijaya Gunturu0J. Kavitha1Swapna Thouti2N. K. Senthil Kumar3Kamal Poon4Ayman A. Alharbi5Amar Y. Jaffar6V. Saravanan7Department of ECE, Indian Institute of Information Technology - Design and Manufacturing (IIITDM)Department of Computer Science & Engineering, Koneru Lakshmaiah Education FoundationDepartment of Electronics and Communication Engineering, CVR College of EngineeringDepartment of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyDepartment of College of Science and Engineering, Southern Arkansas UniversityComputer and Network Engineering Department, College of Computing, Umm Al-Qura UniversityComputer and Network Engineering Department, College of Computing, Umm Al-Qura UniversityDepartment of Computer Science, Dambi Dollo UniversityAbstract The correct sitting posture in a wheelchair is crucial for paralyzed people. This helps prevent problems such as pressure ulcers, muscle contractures, and respiratory problems. A paralyzed person with poor sitting posture is highly likely to slip out of their wheelchair. To prevent this from happening and consistently maintain paralyzed individuals under observation, a new model, the Emperor Penguin Optimized Sensor-Infused Wheelchair (EPIC), has been designed to monitor the position and health of the individual in the wheelchair in real-time. A Force Sensitive Resistor (FSR) sensor and an ultrasonic sensor continuously transmit information to the Arduino UNO R4 Wi-Fi board. The Emperor Penguin Optimizer Algorithm (EPOA) was used to select the features sent from the Arduino board to the ESP8266-Wi-Fi module. A Deep Maxout Network (DMN) was used to predict the posture of a wheelchair-using patient following the feature selection phase. A mobile application for Android collects data from the ESP32 module and estimates posture to inform the caretaker about the user’s current posture and health status. Evaluation metrics such as precision, accuracy, sensitivity, and specificity have been used to determine the efficiency in the EPIC framework, which improves overall accuracy by 10.1%, 7.73%, and 2.84% for better posture recognition.https://doi.org/10.1038/s41598-025-04381-xHealthcare monitoring systemDeep Maxout NetworkDeep learningEmperor Penguin OptimizerSensor networksFeature selection |
| spellingShingle | Vijaya Gunturu J. Kavitha Swapna Thouti N. K. Senthil Kumar Kamal Poon Ayman A. Alharbi Amar Y. Jaffar V. Saravanan Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network Scientific Reports Healthcare monitoring system Deep Maxout Network Deep learning Emperor Penguin Optimizer Sensor networks Feature selection |
| title | Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network |
| title_full | Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network |
| title_fullStr | Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network |
| title_full_unstemmed | Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network |
| title_short | Multi sensor based monitoring of paralyzed using Emperor Penguin Optimizer and Deep Maxout Network |
| title_sort | multi sensor based monitoring of paralyzed using emperor penguin optimizer and deep maxout network |
| topic | Healthcare monitoring system Deep Maxout Network Deep learning Emperor Penguin Optimizer Sensor networks Feature selection |
| url | https://doi.org/10.1038/s41598-025-04381-x |
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