WLSCMS: Wearable Lumbar Spine Curve Monitoring System based on Integrated Sensors

• Published in: IEEE transactions on instrumentation and measurement Vol. 73; p. 1
• Main Authors: Kim, Jungyoon, Hwang, Ja-Young, Kang, Misun, Cheon, Songhee, Park, So Hyun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Back; Biomedical monitoring; Deep Neural Network; Feature extraction; Flexible components; Inertial platforms; Inertial sensing devices; Internet of Thing; Machine Learning; Monitoring; Principal Component Analysis; Sensor systems; Sensors; Signal classification; Spine; Spine Monitoring; Sports medicine; Wearable; Wearable sensors; Wearable technology
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2024.3396844

Monitoring the curvature of the lumbar spine is important for determining the incidence of lower back pain and other spinal disorders in individuals undergoing physical therapy and rehabilitation, and in the field of sports medicine. Especially, to recognize and prevent habitual incorrect spinal curves, a well-suited measurement system is required. In this study, a wearable smart sensing system integrating four flexible sensors and three inertial measurement unit sensors with machine learning was developed. The proposed system was tested on 20 subjects to evaluate its performance. In the experiment, 11 postures were tested using five classes as targets. A feature extraction algorithm was proposed for generating 52 features based on a combination of seven different sensor signals and building classification algorithms for detecting spine events based on the extracted features. The accuracies for classifying five levels of spine curves were 99.38 % overall and 99.79 % in a 10-fold cross validation test, respectively. The proposed method can estimate spine curve class levels without personalized calibrations.