AI Edge Computing Spinal Stiffness Early Warning System

Author: Yu I-Chen; WANG, TING-WEI; YU, CHEN; Hsia, Wei-Hsin; SU, GUAN-LIN

Company/Institution: Chang Gung University of Science and Technology National Tsing Hua University

Country: TAIWAN

e-mail: eddie@wiipa.org.tw

web: https://www.tippa.org.tw/

This smart patch combines eddy-current sensing with a flexible, biocompatible design and uses an MCU for edge computing. It measures fingertip-to-floor distance during spinal bending tests to assess the severity of ankylosing spondylitis. An AI model trained with logistic regression classifies the data into disease stages, providing accurate monitoring. The patch is lightweight, stretchable, and easy to attach to the body, enabling quick and comfortable measurements. It is especially useful for patients with ankylosing spondylitis, helping physicians and patients track disease progression. Beyond clinical applications, the patch also supports rehabilitation and home health monitoring, improving efficiency and medical decision-making accuracy.