Targeted transcranial direct current stimulation combined with bimanual training for children with cerebral palsy

项目摘要 父母补助金的总体目标是确定如何最佳地靶向经颅直流电 刺激（tDCS），以提高上肢（UE）训练的儿童单侧脑 麻痹（UCP）。确定这种干预是否导致改善UE功能的关键工具是使用 临床评估。然而，即使是最好的评估也没有能力确定运动学 孩子的手的动作。大多数评估使用时间来完成一项任务作为手的必然结果 功能或运动质量和功能的主观评级。通过不使用 在运动学上，有一个重要的信息损失，可以帮助优化干预。UE的现有方法 由于其尺寸、成本和操作复杂性，运动学采集不容易获得。 在本补充提案中，我们的目标是使用深度学习（DL）姿态估计模型沿着3D 深度传感相机，开发一个具有成本效益，易于使用，紧凑的基于深度学习的 无标记运动学数据采集（DL-KDA）系统，可应用于UCP儿童。为了 为了实现这一总体目标，我们必须建立从运动数据的准确性和有效性， 系统我们将开始开发一个模块化软件框架，用于构建和测试DL-KDA系统 这是一个非常精确的基于标记的运动捕捉黄金标准（VICON）。我们将研究3D的效果 相机和DL参数/架构对健康成人运动学数据的准确性的影响 参加BBT的人。来自DL-KDA和金标准系统的运动学数据沿着 相应的DL-KDA参数数据将被转换为AI/ML就绪的HDF 5文件。这将发表在 公共DL和NIH数据存储库，以鼓励使用运动学数据进一步开发ML应用程序。 一旦确定了经过验证的最佳DL-KDA配置，我们将调查该系统是否适用于 适用于UCP儿童。由于现有的训练数据集用于大多数DL姿态估计模型， 不包括患有运动障碍的儿童和/或成人，潜在的伦理和科学偏见可能 如果适用于代表性不足的群体，就会出现这种情况。为了解决这个问题，我们将使用我们的UCP大型数据库 在过去的9年里，我们收集了一些评估视频，以生成约12，000张UCP儿童的姿势图像。的图像 将被转换为ML/AI就绪的HDF 5数据集，并在公共DL和NIH存储库中发布。这些 数据集将可供其他研究人员在使用或构建DL姿态估计模型时考虑 用于UCP临床研究。我们将使用此数据集执行迁移学习并重新训练 先前确定的最佳DL模型。重新训练的DL模型的性能将在统计上 与原始DL模型进行比较，以验证偏差是否确实存在。最后，我们将应用最佳DL- KDA使用再训练模型在BBT期间对20名UCP儿童进行测试。UCP的运动学验证 人口，以及将被上传到公共DL和NIH存储库，用于未来的UCP研究。

PROJECT SUMMARY The overall objective for the parent grant is to determine how to optimally target transcranial direct current stimulation (tDCS) to enhance the efficacy of upper extremity (UE) training in children with unilateral cerebral palsy (UCP). A key tool to determine whether this intervention leads to improves UE function is the use of clinical assessments. However, even the best assessments do not have the capacity to identify kinematic features of a child’s hand movement. Most assessments use time to complete a task as a corollary of hand function or subjective ratings of movement quality and function. By not capturing movement patterns using kinematics, there is a vital loss of information that could help optimize interventions. Existing methods of UE kinematics acquisition are not easily accessible because of their size, cost, and operational complexities. In this supplement proposal, we aim to use Deep Learning (DL) pose estimation models along with 3D depth sensing cameras to develop a cost effective, easy to use, and compact Deep Learning based markerless kinematic data acquisition (DL-KDA) system that can be applied to children with UCP. In order to achieve this overall goal, we must establish the accuracy and validity of the kinematic data obtained from the system. We will begin by developing a modular software framework for building and testing DL-KDA systems against a very precise marker-based motion capture gold standard (VICON). We will study the effects of 3D camera and DL parameters/architecture on the accuracy of the resulting kinematic data from healthy adult participants during BBT. Kinematic data from both, the DL-KDA and gold standard systems along with the respective DL-KDA parameter data will be transformed into an AI/ML ready HDF5 file. This will be published in public DL and NIH data repositories to encourage further development of ML applications using kinematic data. Once a validated and optimal DL-KDA configuration is identified, we will investigate this system’s suitability for applications to children with UCP. Since existing training datasets used for most DL pose estimation models are not inclusive of children and/or adults with movement disorders, potential ethical and scientific biases may arise if applied to an underrepresented group. To address this, we will use our large database of UCP assessment videos over the last 9 years to generate ~12,000 pose images of children with UCP. The Images will be transformed into ML/AI ready HDF5 datasets and published in public DL and NIH repositories. These datasets will be available for other researchers to consider when using or building DL pose estimation models for applications in UCP clinical research. We will use this dataset to perform transfer learning and retrain the optimal DL model previously identified. The performance of the retrained DL model will be statistically compared to the original DL model to verify if bias was indeed present. Finally, we will apply the optimal DL- KDA using the retrained model to ~20 children with UCP during the BBT. Validated kinematics for the UCP population, as well, will be uploaded to public DL and NIH repositories for use in future UCP research.