EAGER: An Exploratory Pilot Project to Build an Intelligent Human-Computer Interface System for Neurological Disorder Assessment and Rehabilitation

EAGER：构建用于神经疾病评估和康复的智能人机界面系统的探索性试点项目

• 批准号: 1041637
• 负责人: Fillia Makedon
• 金额: $ 15.21万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1041637&HistoricalAwards=false
• 关键词: EAGER; Exploratory; Pilot; Project; Build

In this EAGER the PI will explore issues relating to the development and use of data from game playing for assessment and rehabilitation of neurological disorders. The focus is on Cerebral Palsy (CP), a disease that causes a variety of motor and other impairments. The most common symptoms of CP are a lack of muscle coordination, stiff muscles, exaggerated reflexes, and impaired gait, and treatment includes Physical Therapy (PT) and Occupational Therapy (OT), speech therapy, drugs, surgery, and orthotic devices. Initially, the PI will investigate game-based systems for the upper extremities. Traditionally, OT experts use subjective judgment in conjunction with the Manual Ability Classification System (MACS) to measure the motor skills without connection to cortical level activity. The PI's approach is to connect cortical activity with motor activity. She will explore the coupling and integration of computer games and wearable sensors for both neurological and motor assessment testing, as well as for long-term rehabilitation at home. In particular, she will design a family of computer games that correspond to OT exercises and build an initial set of feature classifiers for types of CP with motor skill assessment. She will also build computer infrastructure for remote rehabilitation and a cyclical evaluation methodology. The primary outcome of this research will be an initial setup for remote rehabilitation that uses machine reinforcement learning and fuses multimodal information collected from a variety of sensors. Using computer games in conjunction with brain imaging and traditional rehabilitation outcomes is a radical and untested but potentially transformative approach to healthcare practices for chronic conditions such as CP.Broader Impacts: The PI's long-term goal is to develop an intelligent system called CPLAY, whose front end is a set of computer games to provide controlled stimuli to children with CP in order to facilitate a desired motor response and generate performance data for diagnosis and rehabilitation treatment. CPLAY's backend will be a set of computational engines to enable data logging (from a child playing the game), data fusion, analysis and decision support. The @lab version of CPLAY will be used for functional near infrared (fNIR) imaging, while the @home version will be used for rehabilitation with various additional sensors capturing and fusing data during game playing. The fNIR imaging is used to determine neuro-plasticity and motor recovery. The @home version tracks performance over time, considers context of the game, and can be monitored remotely if needed. Both versions will allow for game adjustments to provide personalized treatment. The CPLAY approach promises to lower costs and facilitate family engagement in the rehabilitation. Project outcomes will include a paradigm, methodology and tools with broad applicability to other neurological disorders.

在这个渴望中，PI将探索与开发和使用来自游戏的数据来评估和康复神经障碍有关的问题。重点放在脑瘫(CP)上，这是一种会导致各种运动和其他障碍的疾病。CP最常见的症状是肌肉缺乏协调性、肌肉僵硬、反应过度和步态受损，治疗方法包括物理疗法(PT)和职业疗法(OT)、语言疗法、药物、手术和矫形器。最初，PI将调查基于游戏的上肢系统。传统上，OT专家使用主观判断结合手动能力分类系统(MACS)来测量运动技能，而与皮质水平的活动无关。PI的方法是将皮质活动与运动活动联系起来。她将探索电脑游戏和可穿戴传感器的耦合和集成，用于神经和运动评估测试，以及在家进行长期康复。特别是，她将设计一系列与OT练习相对应的电脑游戏，并为具有运动技能评估的CP类型建立一组初始的特征分类器。她还将为远程康复和周期性评估方法建立计算机基础设施。这项研究的主要成果将是远程康复的初始设置，它使用机器强化学习并融合从各种传感器收集的多模式信息。将电脑游戏与脑成像和传统康复结果结合使用是一种激进且未经测试但具有潜在变革性的慢性疾病医疗实践方法，如CP.Broader Impact：PI的长期目标是开发一种名为CPLAY的智能系统，其前端是一套电脑游戏，为患有CP的儿童提供受控刺激，以促进所需的运动反应，并为诊断和康复治疗生成性能数据。CPLAY的后端将是一套计算引擎，以实现数据记录(来自玩游戏的儿童)、数据融合、分析和决策支持。@Lab版本的CPLAY将用于功能性近红外(FNIR)成像，而@home版本将用于康复，在玩游戏时将使用各种额外的传感器捕获和融合数据。FNIR成像用于确定神经可塑性和运动恢复。@home版本跟踪随时间推移的性能，考虑游戏的上下文，并可以在需要时进行远程监控。这两个版本都将允许游戏调整，以提供个性化的治疗。CPLAY方法承诺降低成本，并促进家庭参与康复。项目成果将包括广泛适用于其他神经疾病的范例、方法和工具。