Learning to Automatically Evaluate Pathological Gait: A Data-Driven System for Characterizing Disability and Informing Therapeutic Interventions

学习自动评估病理步态：用于表征残疾和告知治疗干预的数据驱动系统

• 批准号: 1804945
• 负责人: Kathleen Sienko
• 金额: $ 22.4万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1804945&HistoricalAwards=false
• 关键词: Learning; Automatically; Evaluate; Pathological; Gait

Being able to balance is something most of us take for granted. However, approximately 35% of U.S. citizens 40 years and older are affected by vestibular-related balance issues. The vestibular system, located in the inner ear, is one of several sensory systems that provides our central nervous system with balance and spatial orientation information. When a person's vestibular system is impaired by disease or injury, he/she can experience balance and gait deficits in addition to dizziness and vertigo. There are physical, emotional, and monetary costs associated with sensory-based balance disabilities, such as vestibular disabilities, and the falls that typically follow bouts of balance instability. Most fall-related injuries occur during walking (gait), but treating imbalance during gait is challenging. Current clinical tools for assessing gait pathologies (gait abnormalities due to injury or disease) in people with vestibular disabilities do not fully capture body motion, neglecting potentially critical features of sensory-related disabilities during gait-based activities. The goal of this project is to develop and test data-driven algorithms (problem solving instructions) for characterizing pathological motion. This work will lead to new methods for assessing sensory-related gait disorders and support the development of novel rehabilitation strategies. As part of this research, large motion sensing networks will be combined with machine learning algorithms to identify and measure gait abnormalities in people with vestibular disabilities. Though the focus of this project is on vestibular disabilities, the methods developed can be generalized to a wide range of balance impairments stemming from sensory disabilities, injuries, neural disabilities, motor disabilities, and aging. This research will also contribute to the training of both undergraduate and graduate students through capstone design projects, clinical immersion experiences to identify unmet rehabilitation needs, and the development and implementation of an open access, online educational module focused on applications of machine learning for societal impact.This project's primary purpose is to develop and assess data-driven machine learning (ML) algorithms that identify and quantify pathological gait in people with vestibular disabilities for the purposes of informing the creation of new assessment techniques and supporting the development of novel rehabilitation strategies. The Research Plan is organized under three objectives. The first objective is to create a shareable database of gait measurements from subjects with vestibular disabilities. Activities include: a) recruiting participants with vestibular deficits and age-matched healthy controls, b) collecting kinematic data during an experimental session in which subjects are instrumented with a full set of passive markers and up to 17 IMUs (Inertial Measurement Units), c) collecting clinical vestibular testing diagnostic data, e.g., electronystagmography test battery, and d) collecting Physical Therapist (PT) labels based on videotaped gait rehabilitation exercises that are viewed and rated on a 1-5 visual analog scale by a small cohort of PTs and d) sharing data by organizing data into tables that can be downloaded in a local database format. The second objective is to develop robust data-driven ML algorithms for automatically evaluating and characterizing pathological gait patterns in people with vestibular disabilities. Activities are organized under sub-objectives designed to learn data-driven models to a) automatically differentiate subjects with vestibular disabilities from healthy controls, b) characterize subpopulations by developing a notion of prototypical gait pathologies for each clinical subgroup and c) quantify the extent of the disability and generate hypotheses regarding the root sensorimotor or biomechanical problem. The third objective is to develop and prospectively evaluate a portable system for real-time assessment. Activities include: a) developing a portable smartphone gait assessment tool that will generate real-time ratings during gait-based rehabilitation exercises using data obtained from no more than 7 IMUs and b) prospectively testing the system in a proof-of-concept study involving 10 adults.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

能够平衡是我们大多数人认为理所当然的事情。然而，大约35%的40岁及以上的美国公民受到前庭相关平衡问题的影响。位于内耳的前庭系统是为我们的中枢神经系统提供平衡和空间方向信息的几个感觉系统之一。当一个人的前庭系统因疾病或受伤而受损时，除了头晕和眩晕外，他/她还会经历平衡和步态障碍。以感觉为基础的平衡障碍会带来身体、情感和金钱上的损失，比如前庭功能障碍，以及通常在平衡不稳定之后出现的跌倒。大多数与跌倒相关的损伤发生在步行（步态）过程中，但治疗步态不平衡是具有挑战性的。目前用于评估前庭功能障碍患者步态病理（因损伤或疾病导致的步态异常）的临床工具不能完全捕捉身体运动，忽略了在基于步态的活动中感觉相关残疾的潜在关键特征。这个项目的目标是开发和测试数据驱动的算法（解决问题的指令）表征病理运动。这项工作将导致评估感觉相关步态障碍的新方法，并支持新的康复策略的发展。作为这项研究的一部分，大型运动传感网络将与机器学习算法相结合，以识别和测量前庭功能障碍患者的步态异常。虽然本项目的重点是前庭功能障碍，但所开发的方法可以推广到由感觉障碍、损伤、神经障碍、运动障碍和衰老引起的广泛的平衡障碍。这项研究还将通过顶点设计项目、临床沉浸式体验来识别未满足的康复需求，以及开发和实施一个开放获取的在线教育模块，重点关注机器学习对社会影响的应用，从而为本科生和研究生的培训做出贡献。该项目的主要目的是开发和评估数据驱动的机器学习（ML）算法，以识别和量化前庭残疾患者的病理步态，为创建新的评估技术提供信息，并支持开发新的康复策略。研究计划有三个目标。第一个目标是创建一个可共享的前庭功能障碍受试者步态测量数据库。活动包括:a)招募前庭功能缺陷的参与者和年龄匹配的健康对照者，b)在实验过程中收集运动学数据，在实验过程中，受试者使用全套被动标记物和多达17个惯性测量单元（imu）进行测量，c)收集临床前庭测试诊断数据，例如眼震电图测试电池，d)收集物理治疗师（PT）的标签，这些标签基于步态康复练习的录像，由一小群PT观看并按1-5级视觉模拟量表打分。d)通过将数据组织成表格共享数据，这些表格可以以本地数据库格式下载。第二个目标是开发健壮的数据驱动ML算法，用于自动评估和表征前庭功能障碍患者的病理步态模式。活动组织在子目标下，旨在学习数据驱动的模型，以a)自动区分前庭功能障碍受试者和健康对照者，b)通过为每个临床亚组开发原型步态病理概念来表征亚群，c)量化残疾程度并产生关于根本感觉运动或生物力学问题的假设。第三个目标是开发和前瞻性地评估用于实时评估的便携式系统。活动包括：a)开发一种便携式智能手机步态评估工具，该工具将使用不超过7个imu获得的数据在基于步态的康复练习中生成实时评级；b)在涉及10名成年人的概念验证研究中对该系统进行前瞻性测试。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。