Enhancing Spinal Health: Personalized Exoskeleton for Preventing and Rehabilitating Heavy Lifting-Related Conditions

This study explores the efficacy of the Support Vector Machine (SVM) algorithm as a diagnostic classifier for distinguishing between excessive spinal loading and other spinal conditions. By visualizing a multidimensional image classification dataset in three-dimensional space, distinct clusters corresponding to patients with scoliosis, spondylolisthesis, and normal spinal conditions were identified. Principal component analysis revealed a correlation between high weight-bearing loads and spondylolisthesis development, while scoliosis was associated with different factors. SVM training achieved a robust 97% accuracy when assessing cases beyond the training set, affirming its reliability in diagnosing spinal conditions. A customized exoskeleton prototype, designed to fit individual body measurements, was created for rehabilitation, postural support, and preventing spondylolisthesis. The integration of an Electric Actuator System offered active control and adaptability, albeit with higher initial expenses. The exoskeleton improved shoulder stability using Scapula Support Plates and Shoulder Support Plates (Acromion). These findings highlight the potential of both SVM technology and personalized exoskeletons in enhancing spinal health and posture.