Non-intrusive RF sensing for early diagnosis of spinal curvature syndrome disorders

• Published in: Computers in biology and medicine Vol. 155; p. 106614
• Main Authors: Mustafa, Ali, Ullah, Farman, Rehman, Mobeen Ur, Khan, Muhammad Bilal, Tanoli, Shujaat Ali Khan, Ullah, Muhammad Kaleem, Umar, Hamza, Chong, Kil To
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2023; Elsevier Limited
• Subjects: Algorithms; Classification; Contactless RF sensing; Disorders; Early Diagnosis; Feature extraction; Humans; Internal Medicine; Kyphosis; Learning algorithms; Lordosis; Machine learning; Medical diagnosis; Monitoring; OFDM; Other; Scoliosis; Software Defined Radio; Software radio; Spinal curvature; Spinal Curvatures; Spine curvature disorders; Statistics; Telemedicine; USRP; Wireless channel state information; Wireless channel state information; Spine curvature disorders; OFDM; Machine learning; Contactless RF sensing; USRP; Software Defined Radio
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.106614

The recent developments in communication and information ease people’s lives to sit in one place and access any information from anywhere. However, the longevity of sitting and sitting in different postures raises the issues of spinal curvature. It necessitates a physical examination to identify the spinal illness in its early stages. This article aims to develop an intelligent monitoring framework for detecting and monitoring spinal curvature syndrome problems based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing actual patients. The proposed SDRF-based system identifies irregular spinal curvature syndrome and offers feedback signals when an incorrect posture is identified. We design the system using wireless university software-defined radio peripheral (USRP) kits to transmit and receive RF signals and record the wireless channel state information (WCSI) for kyphosis, Lordosis, and scoliosis spinal disorders. The statistical measures are extracted from the WCSI and apply machine learning algorithms to identify and classify the type of disorders. We record and test the system using 11 subjects with the spinal disorders kyphosis, Lordosis, and scoliosis. We acquire the WCSI, extract various statistical measures in terms of time and frequency domain features, and evaluate machine learning classifiers to identify and classify the spinal disorder. The performance comparison of the machine learning algorithms showed overall and each spinal curvature disorder recognition accuracy of more than 99%. •Contactless RF sensing approach for monitoring and detecting human spinal curvature syndrome disorders.•Software defined radio (SDR) based platform development for early diagnostics of spinal disorders.•Spinal disorder identification by the minute variations in orthogonal frequency division multiplexing (OFDM) sub-carriers through wireless radio frequency channel using channel state information (CSI).•Analysis of various machine learning algorithms for accurate performance comparison to classify the spine disorder.