3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units.

Children's walking patterns evolve with age, exhibiting less repetitiveness at a young age and more variability than adults. Three-dimensional gait analysis (3DGA) is crucial for understanding and treating lower limb movement disorders in children, traditionally performed using Optical Motion Capture (OMC). Inertial Measurement Units (IMUs) offer a cost-effective alternative to OMC, although challenges like drift errors persist.

A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors.

A combination of wearable sensors' data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints' kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys' (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials.

Design, Fabrication, and Accuracy of a Novel Noncovering Lock-Mechanism Bilateral Patient-Specific Drill Guide Template for Nondeformed and Deformed Thoracic Spines.

: Pedicle screw (PS) placement has been widely used in fusion surgeries on the thoracic spine. Achieving cost-effective yet accurate placements through nonradiation techniques remains challenging. : Novel noncovering lock-mechanism bilateral vertebra-specific drill guides for PS placement were designed/fabricated, and their accuracy for both nondeformed and deformed thoracic spines was tested.