Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Punjab 43600, Pakistan.
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Punjab 43600, Pakistan; Division of Computer Science and Engineering, Jeonbuk National University, Jeonju, South Korea.
Comput Biol Med. 2023 Mar;155:106614. doi: 10.1016/j.compbiomed.2023.106614. Epub 2023 Feb 8.
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%.
最近通信和信息领域的发展使得人们可以足不出户，坐在一个地方就获取任何地方的信息。然而，长时间保持坐姿和不同的姿势会导致脊柱弯曲问题。因此，有必要进行体检以在早期阶段发现脊柱疾病。本文旨在开发一种基于软件定义无线电频率（SDRF）感应的智能监测框架，用于检测和监测脊柱弯曲综合征问题，并验证其对实际患者进行诊断的可行性。基于 SDRF 的系统可以识别不规则的脊柱弯曲综合征，并在识别出不正确的姿势时提供反馈信号。我们使用无线大学软件定义无线电外围设备（USRP）套件来设计系统，以传输和接收射频信号，并记录脊柱后凸、前凸和脊柱侧凸的无线信道状态信息（WCSI）。从 WCSI 中提取统计量，并应用机器学习算法来识别和分类疾病类型。我们使用 11 名患有脊柱后凸、前凸和脊柱侧凸疾病的受试者来记录和测试系统。我们获取 WCSI，提取时间和频域特征方面的各种统计量，并评估机器学习分类器，以识别和分类脊柱疾病。机器学习算法的性能比较表明，整体和每种脊柱弯曲疾病的识别准确率都超过了 99%。
