A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network
Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video proces...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4716 |
| _version_ | 1827740149886222336 |
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| author | Madiha Javeed Naif Al Mudawi Bayan Ibrahimm Alabduallah Ahmad Jalal Wooseong Kim |
| author_facet | Madiha Javeed Naif Al Mudawi Bayan Ibrahimm Alabduallah Ahmad Jalal Wooseong Kim |
| author_sort | Madiha Javeed |
| collection | DOAJ |
| description | Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature. |
| first_indexed | 2024-03-11T03:20:54Z |
| format | Article |
| id | doaj.art-e61f9ba23e7d4a83a7ef64df465c568d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:54Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e61f9ba23e7d4a83a7ef64df465c568d2023-11-18T03:11:29ZengMDPI AGSensors1424-82202023-05-012310471610.3390/s23104716A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural NetworkMadiha Javeed0Naif Al Mudawi1Bayan Ibrahimm Alabduallah2Ahmad Jalal3Wooseong Kim4Department of Computer Science, Air University, Islamabad 44000, PakistanDepartment of Computer Science, College of Computer Science and Information System, Najran University, Najran 55461, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science, Air University, Islamabad 44000, PakistanDepartment of Computer Engineering, Gachon University, Seongnam 13120, Republic of KoreaLocomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.https://www.mdpi.com/1424-8220/23/10/4716activities of daily living classificationambient sensorinertial filtermultimodal locomotionlocomotion predictionvisual sensors |
| spellingShingle | Madiha Javeed Naif Al Mudawi Bayan Ibrahimm Alabduallah Ahmad Jalal Wooseong Kim A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network Sensors activities of daily living classification ambient sensor inertial filter multimodal locomotion locomotion prediction visual sensors |
| title | A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network |
| title_full | A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network |
| title_fullStr | A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network |
| title_full_unstemmed | A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network |
| title_short | A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network |
| title_sort | multimodal iot based locomotion classification system using features engineering and recursive neural network |
| topic | activities of daily living classification ambient sensor inertial filter multimodal locomotion locomotion prediction visual sensors |
| url | https://www.mdpi.com/1424-8220/23/10/4716 |
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