  This study presents an innovative computer vision framework designed to
analyze human movements in industrial settings, aiming to enhance biomechanical
analysis by integrating seamlessly with existing software. Through a
combination of advanced imaging and modeling techniques, the framework allows
for comprehensive scrutiny of human motion, providing valuable insights into
kinematic patterns and kinetic data. Utilizing Convolutional Neural Networks
(CNNs), Direct Linear Transform (DLT), and Long Short-Term Memory (LSTM)
networks, the methodology accurately detects key body points, reconstructs 3D
landmarks, and generates detailed 3D body meshes. Extensive evaluations across
various movements validate the framework's effectiveness, demonstrating
comparable results to traditional marker-based models with minor differences in
joint angle estimations and precise estimations of weight and height.
Statistical analyses consistently support the framework's reliability, with
joint angle estimations showing less than a 5-degree difference for hip
flexion, elbow flexion, and knee angle methods. Additionally, weight estimation
exhibits an average error of less than 6 % for weight and less than 2 % for
height when compared to ground-truth values from 10 subjects. The integration
of the Biomech-57 landmark skeleton template further enhances the robustness
and reinforces the framework's credibility. This framework shows significant
promise for meticulous biomechanical analysis in industrial contexts,
eliminating the need for cumbersome markers and extending its utility to
diverse research domains, including the study of specific exoskeleton devices'
impact on facilitating the prompt return of injured workers to their tasks.
