Effect of a back-support exoskeleton on internal forces and lumbar spine stability during low load lifting task.

This study assessed the effect of a small-torque generating passive back-support exoskeleton during a low demanding occupational task, namely a repetitive lifting/lowering of an empty crate between the knee and shoulder heights. A comprehensive set of outcomes was considered, ranging from the measured trunk muscle activation and trunk movement to the estimated muscle group forces/coordination, spine loading and spine stability, using a dynamic subject-specific EMG-assisted musculoskeletal model. The exoskeleton decreased back muscle activation and corresponding muscle forces in the lowering phase and reduced spinal loading at larger trunk flexion angles (decreased peak compression and shear forces by ∼ 15%).

Validation of proprioception measures of the lumbar spine.

Background: To better personalize treatment and monitor recovery of individuals with low back pain, objective tests of sensorimotor functions, such as lumbar proprioception, must be selected based on their reliability and validity. The primary objective of this study was to test the concurrent validity of three measures of lumbar proprioception.

Methods: Thirty-one participants performed three lumbar proprioception tests (motion perception threshold, active and passive joint positioning sense), a whole-body mobility and balance (time up-and-go) and two trunk-specific postural control (threshold of stability and sensor-based sway measures) tests.

Disability reduction following a lumbar stabilization exercise program for low back pain: large vs. small improvement subgroup analyses of physical and psychological variables.

Background: Little is known about why patients with low back pain (LBP) respond differently to treatment, and more specifically, to a lumbar stabilization exercise program. As a first step toward answering this question, the present study evaluates how subgroups of patients who demonstrate large and small clinical improvements differ in terms of physical and psychological changes during treatment.

Methods: Participants (n = 110) performed the exercise program (clinical sessions and home exercises) over eight weeks, with 100 retained at six-month follow-up.

Machine learning applications in spine biomechanics.

Spine biomechanics is at a transformation with the advent and integration of machine learning and computer vision technologies. These novel techniques facilitate the estimation of 3D body shapes, anthropometrics, and kinematics from as simple as a single-camera image, making them more accessible and practical for a diverse range of applications. This study introduces a framework that merges these methodologies with traditional musculoskeletal modeling, enabling comprehensive analysis of spinal biomechanics during complex activities from a single camera.