Learning invariant representation from high- dimensional data for quantitative stroke reha

可穿戴电子产品、个人移动的设备和传感器技术的进步正在为 在医疗保健和生物医学研究中有许多有前途的应用。但是，生成的数据集 由于与任务无关的外来影响引起的可变性， 感兴趣，例如环境条件的变化、测量噪声的异方差性或患者 特质这些影响在用于训练机器的数据之间产生了系统性差异， 学习算法和它们在实践中应用的数据，损害了现实世界的性能。 拟议中的研究将解决排除外部影响的基本问题 与已知的滋扰变量相关联。我们将开发一种新的方法来提取特征， 对讨厌的变量是不变的，因此对相关的外来效应也是不变的，但仍然是 用于分类或回归。该方法基于非参数深度网络模型 它执行数据的自动规范化，并通过对抗学习进一步增强不变性。 我们将把这种方法应用于中风康复中的一个重要问题，即运动的定量给药。 训练使用基于传感器的运动数据的数据集，我们将训练模型来识别和计数 脑卒中患者进行康复活动时的功能性运动。我们希望证明我们的 方法可以克服患者的变异性，以实现严格的运动分类和定量。的 拟议的工作是重要的，因为它将使研究人员能够进行严格的剂量试验 中风康复所需要的所提出的工作是创新的，因为它离开了传统的数据 预处理技术相结合的先进数据标准化和模型校准程序。 我们的工作可能会对中风康复产生积极的影响，促进所需的研究， 改变临床实践并改善卒中结局。我们的定量方法是广泛的推广 应用于受干扰变量（例如医学诊断和基因组学）阻碍的应用。

Advances in wearable electronics, personal mobile devices, and sensor technology are opening the door to many promising applications in medical care and biomedical research. However, the resulting datasets are often challenging to process due to variability caused by extraneous effects unrelated to the tasks of interest, such as changes in environmental conditions, heteroscedasticity in measurement noise, or patient idiosyncrasies. These effects produce systematic differences between the data used to train machine- learning algorithms and the data on which they are applied in practice, impairing real-world performance. The proposed research will address the fundamental problem of factoring out extraneous effects associated with known nuisance variables. We will develop a novel methodology for extracting features that ar.e invariant to nuisance variables-and hence also to the associated extraneous effects-but that are still useful for classification or regression. The methodology is based on nonparametric deep-network models that perform automatic normalization of the data, and further enforce invariance via adversarial learning. We will apply the approach to an important problem in stroke rehabilitation, the quantitated dosing of motor training. Using a dataset of sensor-based motion data, we will train the model to identify and count functional movements in stroke patients performing rehabilitation activities. We expect to show that our approach can surmount patient variability to enable rigorous movement classification and quantitation. The proposed work is significant, because it will empower investigators to undertake the dosing trials critically needed in stroke rehabilitation. The proposed work is innovative, because it departs from traditional data preprocessing techniques by combining advanced data normalization and model calibration procedures. Our work is likely to have a positive impact on stroke rehabilitation by facilitating the research required to change clinical practice and improve stroke outcomes. Our quantitative approach is broadly generalizable to applications hindered by nuisance variables, such as medical diagnostics and genomics.