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

项目概要/摘要 脑瘫（CP）是幼儿中最常见的运动障碍。没有治愈方法，但要遵守纪律 康复可以改善结果。康复的一个重要组成部分是对患者的持续评估 功能。农村地区的患者可能很难获得护理。远程康复提供了延长时间的选择 获得护理，但也有局限性。该项目的重点是 1) 了解社交机器人的物理结构 在远程康复评估期间与患者共处一地可能会改变患者的活动 导致临床医生执行评估的能力发生变化，2）计算机视觉和 机器学习可用于评估患者。这两个互补的目标共同将指明一条道路 为 CP 和类似病症患者的远程治疗提供支持。 将通过一项研究来检验在远程康复中使用社交机器人的效果，其中儿科 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、通过传统方式 远程呈现，以及有社交机器人在场的远程呈现。主体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 通过向专家治疗师提供每种情况下的第一人称视频记录并进行比较来进行测量 每个条件下他们的评分差异。 为了真正实现使用远程评估来扩展护理的承诺，自动评估评分 是必要的。为了评估其可行性，拍摄了具有不同上肢功能水平的儿童的视频 连同它们的框和块分数以及临床医生评级将用于训练两种算法。两种算法都会 首先使用现成的基于卷积神经网络的工具来提取主体的姿势。第一个 算法将是手工设计的。它将学习如何对已知的运动指标进行加权，例如运动速度， 使用主成分分析和朴素高斯分析达到最大速度的时间以及速度峰值的数量 分类器。第二种算法将使用直接对时间序列姿势数据进行操作的自定义神经网络。 两种算法都将尝试在给定新主题的视频的情况下预测功能水平，正如以下方法所预测的那样 治疗师。将分析这两种算法以发现其潜在的决策哲学，这可能 深入了解哪些运动参数可以清楚地区分功能水平。 该项目将在博士前培训计划的背景下完成。该计划重点发展 机器人学和康复科学交叉领域的独立研究员。这将在 机械工程、物理医学与康复、通用机器人、自动化、传感、 宾夕法尼亚大学的感知和感知 (GRASP) 实验室，提供额外的指导和经验 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.