Monitoring Bone Healing: Integrating RF Sensing With AI
This study presents the development of an advanced machine learning model based on a two-dimensional (2D) Radio Frequency (RF) sensing framework for refined monitoring of femoral bone fractures. Utilising MATLAB simulations, we created a comprehensive dataset enhanced with variations in bone diamete...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818430/ |
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| author | Ahmad Aldelemy Ebenezer Adjei Prince O. Siaw Ali Al-Dulaimi Viktor Doychinov Nazar T. Ali Rami Qahwaji John G. Buckley Pete Twigg Raed A. Abd-Alhameed |
| author_facet | Ahmad Aldelemy Ebenezer Adjei Prince O. Siaw Ali Al-Dulaimi Viktor Doychinov Nazar T. Ali Rami Qahwaji John G. Buckley Pete Twigg Raed A. Abd-Alhameed |
| author_sort | Ahmad Aldelemy |
| collection | DOAJ |
| description | This study presents the development of an advanced machine learning model based on a two-dimensional (2D) Radio Frequency (RF) sensing framework for refined monitoring of femoral bone fractures. Utilising MATLAB simulations, we created a comprehensive dataset enhanced with variations in bone diameter, muscle thickness, fat thickness, and hematoma size, augmented with multiple sensor configurations (two, four, six, and eight sensors). The model aims to provide a frequent, non-invasive assessment of the fracture healing process compared to conventional imaging methods. Our approach leverages data from six RF sensors, achieving a high overall accuracy of 99.2% in classifying different fracture stages, including “no fracture” and varying degrees of hematoma sizes. The findings indicate that increasing the number of sensors up to six significantly enhances detection accuracy and sensitivity across all fracture stages. However, the marginal improvement from six to eight sensors was not statistically significant, suggesting that a six-sensor configuration offers an optimal balance between performance and system complexity. The results demonstrate significant potential for this technology to revolutionise orthopaedic treatment and recovery management by offering continuous, real-time monitoring without radiation exposure. The proposed system enhances personalised patient care by integrating RF sensing with artificial intelligence, enabling timely interventions and more informed, data-driven treatment strategies. This research lays a robust foundation for future advancements, including three-dimensional modelling and clinical validations, toward the practical implementation of non-invasive fracture monitoring systems. |
| format | Article |
| id | doaj-art-c4de775fa47f4023b3c4baeb9acdffb9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c4de775fa47f4023b3c4baeb9acdffb92025-01-24T00:02:04ZengIEEEIEEE Access2169-35362025-01-0113111141113510.1109/ACCESS.2024.352417810818430Monitoring Bone Healing: Integrating RF Sensing With AIAhmad Aldelemy0https://orcid.org/0000-0002-3547-7461Ebenezer Adjei1https://orcid.org/0009-0000-3773-3150Prince O. Siaw2https://orcid.org/0009-0003-4930-9906Ali Al-Dulaimi3Viktor Doychinov4https://orcid.org/0000-0001-6730-0057Nazar T. Ali5https://orcid.org/0000-0003-2991-9451Rami Qahwaji6https://orcid.org/0000-0002-8637-1130John G. Buckley7Pete Twigg8Raed A. Abd-Alhameed9https://orcid.org/0000-0003-2972-9965Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.School of Computing and Innovative Technologies, British University Vietnam, Hưng Yên, VietnamFaculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesFaculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.This study presents the development of an advanced machine learning model based on a two-dimensional (2D) Radio Frequency (RF) sensing framework for refined monitoring of femoral bone fractures. Utilising MATLAB simulations, we created a comprehensive dataset enhanced with variations in bone diameter, muscle thickness, fat thickness, and hematoma size, augmented with multiple sensor configurations (two, four, six, and eight sensors). The model aims to provide a frequent, non-invasive assessment of the fracture healing process compared to conventional imaging methods. Our approach leverages data from six RF sensors, achieving a high overall accuracy of 99.2% in classifying different fracture stages, including “no fracture” and varying degrees of hematoma sizes. The findings indicate that increasing the number of sensors up to six significantly enhances detection accuracy and sensitivity across all fracture stages. However, the marginal improvement from six to eight sensors was not statistically significant, suggesting that a six-sensor configuration offers an optimal balance between performance and system complexity. The results demonstrate significant potential for this technology to revolutionise orthopaedic treatment and recovery management by offering continuous, real-time monitoring without radiation exposure. The proposed system enhances personalised patient care by integrating RF sensing with artificial intelligence, enabling timely interventions and more informed, data-driven treatment strategies. This research lays a robust foundation for future advancements, including three-dimensional modelling and clinical validations, toward the practical implementation of non-invasive fracture monitoring systems.https://ieeexplore.ieee.org/document/10818430/RF sensingartificial intelligencebone fracture monitoringmachine learningnon-invasive assessmenthealing process |
| spellingShingle | Ahmad Aldelemy Ebenezer Adjei Prince O. Siaw Ali Al-Dulaimi Viktor Doychinov Nazar T. Ali Rami Qahwaji John G. Buckley Pete Twigg Raed A. Abd-Alhameed Monitoring Bone Healing: Integrating RF Sensing With AI IEEE Access RF sensing artificial intelligence bone fracture monitoring machine learning non-invasive assessment healing process |
| title | Monitoring Bone Healing: Integrating RF Sensing With AI |
| title_full | Monitoring Bone Healing: Integrating RF Sensing With AI |
| title_fullStr | Monitoring Bone Healing: Integrating RF Sensing With AI |
| title_full_unstemmed | Monitoring Bone Healing: Integrating RF Sensing With AI |
| title_short | Monitoring Bone Healing: Integrating RF Sensing With AI |
| title_sort | monitoring bone healing integrating rf sensing with ai |
| topic | RF sensing artificial intelligence bone fracture monitoring machine learning non-invasive assessment healing process |
| url | https://ieeexplore.ieee.org/document/10818430/ |
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