Improving Telerehabilitation in Pediatric Cerebral Palsy Using Machine Learning and Social Robots

项目摘要/摘要 脑性瘫痪(CP)是幼儿最常见的运动障碍。没有治愈的办法，但有纪律 康复可以改善结果。康复的一个关键组成部分是对患者的持续评估 功能。农村地区的患者可以获得不同的fi疾病访问护理。远程康复提供了一种扩展 获得护理的机会，但有局限性。这个项目的重点是1)了解社交机器人如何在身体上 在远程康复评估期间与患者共处一室可能会改变患者的活动 导致临床医生进行评估的能力发生变化，以及2)计算机视觉和 机器学习可以用来评估患者。这两个相辅相成的目标将共同指明一条道路 远期用于远程治疗CP和类似情况的患者。 在远程康复中使用社交机器人的效果将通过一项研究来检验，在该研究中，儿童CP 受试者和典型受试者在三种情况下与远程操作员交互：面对面、传统的 网真，并在社交机器人在场的情况下超越网真。受试者互动水平的直接变化 合规性将通过调查和视频编码来衡量。对评估质量的影响将是 通过向专家治疗师展示来自每种情况的fi第一人视频记录并比较 他们在每种情况下的评分差异。 要真正实现使用远程评估扩展护理的承诺，请对评估进行自动评分 是必要的。为了评估这一方法的可行性，将具有不同程度上肢功能的儿童的视频 连同他们的框和块分数和临床医生评级将被用来训练两个算法。这两种算法都将 首先使用现成的基于卷积神经网络的工具来提取对象的姿势。ThefiRst 算法将手工设计。它将学习如何对已知的运动度量进行加权，如移动速度、 达到最大速度的时间，以及速度峰值的数量，使用主成分分析和朴素高斯 Classifi呃。第二种算法将使用自定义神经网络直接对时间序列姿势数据进行操作。 这两种算法都将尝试在给定新对象的视频的情况下预测功能级别 一位心理医生。将对这两种算法进行分析，以发现其潜在的决策原理，这可能 洞察哪些运动参数可以清楚地区分不同的功能水平。 该项目将在博士前培训计划的背景下进行。该计划的重点是发展 在机器人学和康复科学的交叉点上的独立研究员。这将在 机械工程，物理医学和康复，以及普通机器人，自动化，传感， 和感知(GRAP)实验室，在宾夕法尼亚大学有额外的指导和经验 在费城儿童医院。

PROJECT SUMMARY/ABSTRACT Cerebral palsy (CP) is the most common motor disorder in young children. There is no cure, but disciplined rehabilitation can improve outcomes. A critical component of rehabilitation is continuous assessment of patient function. Patients in rural areas can have difficulty accessing care. Telerehabilitation provides an option to extend access to care, but has limitations. The foci of this project are 1) understanding how a social robot physically co-located with the patient during a telerehabilitation assessment alters the activity by the patient, potentially leading to changes in the ability of the clinician to perform assessment and 2) whether computer vision and machine learning can be used to assess patients. Together, these two complementary goals will show a path forward for remote treatment of patients with CP and similar conditions. The effect of using a social robot in telerehabilitation will be examined through a study where pediatric CP subjects and typical subjects interact with a remote operator in three conditions: face-to-face, over traditional telepresence, and over telepresence with a social robot present. Direct changes in the level of subject interaction and compliance will be measured through surveys and video coding. The effect on quality of assessment will be measured by presenting expert therapists with first-person video recordings from each condition and comparing the variance of their grading for each condition. To truly realize the promise of using remote assessment to extend care, automated grading of assessments is necessary. To evaluate the feasibility of this, videos of children with various levels of upper extremity function along with their box and block scores and clinician ratings will be used to train two algorithms. Both algorithms will begin by using off the shelf convolutional neural network based tools to extract the pose of the subjects. The first algorithm will be hand designed. It will learn how to weight known metrics of motion, such as movement speed, time to maximum speed, and number of speed peaks, using principal component analysis and a naive Gaussian classifier. The second algorithm will use a custom neural network operating directly on the time-series pose data. Both algorithms will attempt to, given video of a novel subject, predict the level of function as would be predicted by a therapist. Both algorithms will be analyzed to discover their underlying decision-making philosophies, which may give insight into what parameters of motion clearly differentiate levels of function. The project will be done in the context of a pre-doctoral training plan. The plan focuses on developing an independent researcher at the intersection of robotics and rehabilitation science. This will be done within Mechanical Engineering, Physical Medicine and Rehabilitation, and the General Robotics, Automation, Sensing, and Perception (GRASP) laboratory at the University of Pennsylvania with additional mentorship and experience at the Children's Hospital of Philadelphia.