Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study
In the last few decades, major progress has been made in the medical field; in particular, new treatments and advanced health technologies allow for considerable improvements in life expectancy and, more broadly, in quality of life. As a consequence, the number of elderly people is expected to incre...
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MDPI AG
2024-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/19/6208 |
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| author | Davide De Vittorio Antonio Barili Giovanni Danese Elisa Marenzi |
| author_facet | Davide De Vittorio Antonio Barili Giovanni Danese Elisa Marenzi |
| author_sort | Davide De Vittorio |
| collection | DOAJ |
| description | In the last few decades, major progress has been made in the medical field; in particular, new treatments and advanced health technologies allow for considerable improvements in life expectancy and, more broadly, in quality of life. As a consequence, the number of elderly people is expected to increase in the following years. This trend, along with the need to improve the independence of frail people, has led to the development of unobtrusive solutions to monitor daily activities and provide feedback in case of risky situations and falls. Monitoring devices based on radar sensors represent a possible approach to tackle postural analysis while preserving the person’s privacy and are especially useful in domestic environments. This work presents an innovative solution that combines millimeter-wave radar technology with artificial intelligence (AI) to detect different types of postures: a series of algorithms and neural network methodologies are evaluated using experimental acquisitions with healthy subjects. All methods produce very good results according to the main parameters evaluating performance; the long short-term memory (LSTM) and GRU show the most consistent results while, at the same time, maintaining reduced computational complexity, thus providing a very good candidate to be implemented in a dedicated embedded system designed to monitor postures. |
| format | Article |
| id | doaj-art-e608e794a4e0413591e4ec8d8e90979a |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e608e794a4e0413591e4ec8d8e90979a2025-08-20T01:47:34ZengMDPI AGSensors1424-82202024-09-012419620810.3390/s24196208Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case StudyDavide De Vittorio0Antonio Barili1Giovanni Danese2Elisa Marenzi3Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, ItalyDepartment of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, ItalyIn the last few decades, major progress has been made in the medical field; in particular, new treatments and advanced health technologies allow for considerable improvements in life expectancy and, more broadly, in quality of life. As a consequence, the number of elderly people is expected to increase in the following years. This trend, along with the need to improve the independence of frail people, has led to the development of unobtrusive solutions to monitor daily activities and provide feedback in case of risky situations and falls. Monitoring devices based on radar sensors represent a possible approach to tackle postural analysis while preserving the person’s privacy and are especially useful in domestic environments. This work presents an innovative solution that combines millimeter-wave radar technology with artificial intelligence (AI) to detect different types of postures: a series of algorithms and neural network methodologies are evaluated using experimental acquisitions with healthy subjects. All methods produce very good results according to the main parameters evaluating performance; the long short-term memory (LSTM) and GRU show the most consistent results while, at the same time, maintaining reduced computational complexity, thus providing a very good candidate to be implemented in a dedicated embedded system designed to monitor postures.https://www.mdpi.com/1424-8220/24/19/6208artificial intelligenceLSTMposture analysisradar technologyembedded systemsfall detection |
| spellingShingle | Davide De Vittorio Antonio Barili Giovanni Danese Elisa Marenzi Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study Sensors artificial intelligence LSTM posture analysis radar technology embedded systems fall detection |
| title | Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study |
| title_full | Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study |
| title_fullStr | Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study |
| title_full_unstemmed | Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study |
| title_short | Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study |
| title_sort | artificial intelligence for the evaluation of postures using radar technology a case study |
| topic | artificial intelligence LSTM posture analysis radar technology embedded systems fall detection |
| url | https://www.mdpi.com/1424-8220/24/19/6208 |
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