Neuromuscular Research Centre, Faculty of Sport and Health Sciences, University of Jyvaskyla, Finland; School of Sport and Exercise, University of Gloucestershire, UK.
J Biomech. 2021 Jun 23;123:110460. doi: 10.1016/j.jbiomech.2021.110460. Epub 2021 May 2.
Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such analyses without markers, making outdoor applications feasible. In this paper I summarise 2D markerless approaches for estimating joint angles, highlighting their strengths and limitations. In computer science, so-called "pose estimation" algorithms have existed for many years. These methods involve training a neural network to detect features (e.g. anatomical landmarks) using a process called supervised learning, which requires "training" images to be manually annotated. Manual labelling has several limitations, including labeller subjectivity, the requirement for anatomical knowledge, and issues related to training data quality and quantity. Neural networks typically require thousands of training examples before they can make accurate predictions, so training datasets are usually labelled by multiple people, each of whom has their own biases, which ultimately affects neural network performance. A recent approach, called transfer learning, involves modifying a model trained to perform a certain task so that it retains some learned features and is then re-trained to perform a new task. This can drastically reduce the required number of training images. Although development is ongoing, existing markerless systems may already be accurate enough for some applications, e.g. coaching or rehabilitation. Accuracy may be further improved by leveraging novel approaches and incorporating realistic physiological constraints, ultimately resulting in low-cost markerless systems that could be deployed both in and outside of the lab.
运动学分析通常在实验室中使用光学相机和反射标记进行。随着人工智能技术（如深度神经网络）的出现，现在可以在不使用标记的情况下进行此类分析，从而实现户外应用。本文总结了用于估计关节角度的无标记 2D 方法，突出了它们的优势和局限性。在计算机科学中，所谓的“姿势估计”算法已经存在多年。这些方法涉及使用称为监督学习的过程训练神经网络来检测特征（例如解剖学标志），这需要手动注释“训练”图像。手动标记有几个限制，包括标记者的主观性、对解剖学知识的要求以及与训练数据质量和数量相关的问题。神经网络通常需要数千个训练示例才能进行准确预测，因此训练数据集通常由多人标记，每个人都有自己的偏见，这最终会影响神经网络的性能。一种称为迁移学习的新方法涉及修改专门用于执行特定任务的模型，使其保留一些学习到的特征，然后重新训练以执行新任务。这可以大大减少所需的训练图像数量。尽管正在进行开发，但现有的无标记系统对于某些应用（例如教练或康复）可能已经足够准确。通过利用新颖的方法并结合现实的生理约束，可以进一步提高准确性，最终导致低成本的无标记系统，可以在实验室内外部署。
