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.

该研究提出了一个创新的计算机视觉框架，旨在通过与现有软件的无缝集成来分析工业环境中的人体运动，以增强生物力学分析。该框架利用先进的图像处理和建模技术，通过卷积神经网络（CNNs）、直接线性变换（DLT）和长短期记忆（LSTM）网络准确地检测关键身体节点，重建 3D 标记点，并生成详细的 3D 人体网格。广泛的评估验证了该框架的有效性，在各种动作下与传统基于标记点的模型展示出了可比较的结果，并在关节角度估计和体重、身高估计方面具有精确性。生物力学 - 57 关键骨骼模板的集成进一步增强了该框架的鲁棒性和可靠性。该框架显示出在工业环境中进行细致的生物力学分析的显著潜力，消除了笨重的标记物的需求，并将其用途扩展到包括研究特定外骨骼装置对促进受伤工人迅速返回工作任务的影响在内的各个研究领域。

运用数字感知技术进行人类生物力学过程的远程感知和分析：一种非接触式负荷和关节力量评估方法

Leveraging Digital Perceptual Technologies for Remote Perception and  Analysis of Human Biomechanical Processes: A Contactless Approach for  Workload and Joint Force Assessment

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. Additionally, we aim to evaluate their
performance and limitations in spine biomechanics applications. The real-world
applications explored in this study include assessment in workplace lifting,
evaluation of whiplash injuries in car accidents, and biomechanical analysis in
professional sports. Our results demonstrate potential and limitations of
various algorithms in estimating body shape, kinematics, and conducting
in-field biomechanical analyses. In industrial settings, the potential to
utilize these new technologies for biomechanical risk assessments offers a
pathway for preventive measures against back injuries. In sports activities,
the proposed framework provides new opportunities for performance optimization,
injury prevention, and rehabilitation. The application in forensic domain
further underscores the wide-reaching implications of this technology. While
certain limitations were identified, particularly in accuracy of predictions,
complex interactions, and external load estimation, this study demonstrates
their potential for advancement in spine biomechanics, heralding an optimistic
future in both research and practical applications.

脊柱生物力学通过机器学习和计算机视觉技术的引入和整合进入了转型期。该研究介绍了一个将这些方法与传统肌肉骨骼模型相结合的框架，使得从单个相机中能够进行复杂活动的脊柱生物力学全面分析成为可能。此框架在工作场所举重评估、车祸中鞭打伤评估以及专业运动的生物力学分析等实际应用中表现出潜力和局限性，并且对于预测准确性、复杂交互和外部负荷估计等方面存在限制，但展示出了在脊柱生物力学领域中进一步进展的潜力，预示着研究和实际应用都有乐观的未来。