Anatomy-Based Assessment of Spinal Posture Using IMU Sensors and Machine Learning.

ABSTRACT:

Background

This study used inertial measurement unit (IMU)-based posture angle estimates to define proxy risk labels and investigated whether these labels can be predicted from demographic, anthropometric, and lifestyle variables through machine learning analysis.

Methods

Thirty healthy individuals aged 18-25 years were included. Demographic and anthropometric data and information on daily living activities were collected. The IMU sensors were placed at vertebral levels C1, C7, T5, T12, and L5. Participants were instructed to stand in an upright posture, followed by a relaxed daily posture. Anatomic postural changes between these positions were analyzed. Cervical lordosis, thoracic kyphosis, lumbar lordosis, and scoliosis risks were predicted using machine learning algorithm

SUBMITTER: Koca R 

PROVIDER: S-EPMC12526714 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature