S2HM: Self-Powered Spine Health Monitoring using piezo/triboelectric nanogenerators

Individuals in professions such as dentistry, aircraft maintenance, and frequent computer usage, are susceptible to developing degenerative changes in the cervical spine due to poor neck posture over time. This can eventually progress to cervical spondylosis. Continuous monitoring of neck health is essential to prevent permanent damage. This study proposes a self-powered, flexible neck brace integrated with four piezoelectric/triboelectric nanogenerators (P/TENGs) designed for the purpose of monitoring neck strength. When the neck undergoes movement, the resultant deformation of the neck brace under force stimulates the P/TENG array, generating voltage output signals from four PENGs and four TENGs. These eight signals are then converted into a 2D intensity map, which is subsequently leveraged for training and prediction through a Convolutional Neural Network (CNN). This approach enables precise differentiation of six distinct neck movements with a precision of 97.78%. The neck brace is integrated and equipped with Inertial Measurement Unit (IMU) sensors to capture neck movement angles and velocities. Combined with data from the P/TENGs, the system offers a comprehensive set of multidimensional data for the evaluation of neck and spine health. Clinical experiments used Principal Component Analysis (PCA) to streamline multidimensional data and applied the KNN algorithm to forecast and categorize cervical curvature abnormality levels (L1-L4), achieving 92.5% accuracy in a trial with 67 participants. In summary, the proposed P/TENG-based neck brace device displays substantial potential for motion recognition and curvature anomaly diagnosis, thereby introducing new prospects for clinical adjunctive diagnosis and home health monitoring.