Multimodal Guidance towards Precision Rehabilitation to Improve Upper Extremity Function in Stroke Patients

多模式精准康复指导改善中风患者上肢功能

• 批准号: 10586179
• 负责人: GEORGE F. WITTENBERG
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10586179
• 关键词: Acceleration; Activities of Daily Living; Acute; Address; Admission activity; Affect; Affective; Algorithms; Area; Autonomic nervous system; Biological Markers; Clinical; Computer software; Data; Data Collection; Decision Making; Diagnosis; Dimensions; Electromyography; Evaluation; Family; Future; Galvanic Skin Response; Goals; Home; Hospitalization; Hospitals; Hour; Impairment; Individual; Inpatients; Knowledge; Life; Machine Learning; Manufacturer; Measurement; Measures; Medical; Modality; Monitor; Motivation; Motor; Movement; Muscle; Outcome; Outcome Assessment; Patients; Physiological; Productivity; Prognosis; Psychological Factors; Quality of life; Recovery; Rehabilitation therapy; Research Project Grants; Sampling; Series; Services; Side; Stress; Stroke; Surface; Sweating; Task Performances; Techniques; Testing; Therapy Evaluation; Time; Upper Extremity; Veterans; acute stroke; arm; arm function; cost; design; disability; experience; functional status; improved; inpatient service; insight; kinematics; lifetime risk; motor impairment; multimodality; personalized intervention; personalized medicine; post stroke; psychologic; response; sensor; stroke patient; stroke rehabilitation; stroke risk; stroke survivor; stroke therapy; usability; wearable device; wearable sensor technology

The lifetime risk of stroke is 1 in 6 with an estimated 33 million stroke survivors worldwide. Ideally acute stroke patients would receive an accurate and rapid prognosis regarding return of motor function, followed by application of those therapies most able to improve it. Yet decisions regarding post-acute treatment of stroke patients are made on short-term assessments of function that may be influenced by concurrent treatment, time-of-day, motivation, and other factors. Those assessments are often delayed, with resultant delays in rehabilitation treatments. There are important decisions that need to be made about the setting where rehabilitation occurs, if it is needed, and where the stroke patient will best live in the long-term. This research project aims to significantly add to the current understanding of biomarkers that can be used to provide better diagnosis, rehabilitative treatment, and long-term disposition advice for veterans who experience upper-extremity impairments from stroke. The gaps in knowledge we aim to address are the unknown relationships between 1. immediate post-stroke movement and functional ability, and 2. between sympathetic tone and psychological response to disability. Clinicians do not yet know how to use the data from wearable technologies that measure these factors – a problem caused by the volume of data generated and lack of reliable biomarkers derived from it. Our central hypothesis is that application of machine learning techniques to data from a multimodal sensor array worn by a patient for multiple hours can provide better evidence of motor ability, assess latent psychological factors, and predict recovery trajectory better than conventional short-term assessments. It may also allow more rapid personalization of therapy plans based on real-world deficits discovered through sensor-based data. We will test our central hypothesis by pursuing the two following specific aims with associated working hypotheses: 1. Collect functionally relevant data from a wearable inertial, electromyographic, and electrodermal sensor array. Working Hypothesis: A few strategically placed sensors can capture functional movement and state of the autonomic nervous system. Kinematic and physiological measures taken during task performance will be correlated with motor impairment and functional status. Completion of this aim will lead to the identification of functional variables derived from multimodal sensor measurements and demonstrate the feasibility of, and challenges to, inpatient use of a sensor array. 2. Predict key clinical outcomes from sensor array-derived variables in acute stroke inpatients being evaluated for post-discharge therapies. Working Hypothesis: Machine learning techniques, including Bayesian fusion, will predict deficits and discharge disposition from the multimodal variables collected. The electrodermal response to challenging movement is an unexplored area that may provide insight into motivation and affective response to impairment. The trajectory of recovery may be captured during a two-day sampling period. Overall low activation of the affected arm and lack of affective responses to challenging movement will be related to poorer recovery and discharge disposition. The modalities that will be measured by wearable sensors in this study are: acceleration, surface muscle electrical activity, and galvanic skin responses. We will acquire data using a suite of sensors from a single manufacturer, aiding the synchronization and convenience of collecting a time-series of data during daily life in the hospital, as well as during motor tasks and assessments. Biomarkers will be extracted using the Bayesian fusion algorithm, and outcomes will be both motor function and discharge disposition. At the conclusion of this project we will have demonstrated that the proposed sensor array can provide meaningful data regarding movement ability, affective response to motor challenges, and will have explored the relationship between that data and discharge disposition.

中风的终生风险为六分之一，全球估计有 3300 万中风幸存者。 中风患者将获得关于运动功能恢复的准确而快速的预后，然后 通过应用那些最能改善它的疗法。 对中风患者进行短期功能评估，这些评估可能会受到并发疾病的影响 这些评估往往会因治疗、时间、动机和其他因素而被延迟。 康复治疗的延误需要就环境做出重要的决定。 如果需要的话，在哪里进行康复，以及中风患者最适合长期居住的地方。 研究项目旨在显着增加目前对可用于 为退伍军人提供更好的诊断、康复治疗和长期处置建议 我们旨在解决的知识差距是中风造成的上肢损伤。 1. 中风后立即运动和功能能力之间的未知关系，以及 2. 临床医生还不知道如何使用这些数据。 来自测量这些因素的可穿戴技术——由生成的数据量引起的问题 我们的中心假设是机器的应用。 学习来自患者佩戴多个小时的多模态传感器阵列的数据的技术可以 提供更好的运动能力证据，评估潜在的心理因素，并预测恢复轨迹 比传统的短期评估更好，它还可以实现更快速的个性化治疗。 基于通过传感器数据发现的现实世界缺陷制定的计划我们将测试我们的中心假设。 通过相关工作假设来实现以下两个具体目标： 1. 从可穿戴惯性、肌电图和 工作假设：一些策略性放置的传感器可以捕获数据。 自主神经系统的功能运动和状态运动和生理测量。 任务执行期间采取的措施将与运动障碍和功能状态相关。 这一目标将导致识别来自多模态传感器的功能变量 测量并展示住院患者使用传感器阵列的可行性和挑战。 2. 根据传感器阵列衍生的变量预测急性中风的关键临床结果 住院患者接受出院后治疗评估 工作假设：机器学习。 包括贝叶斯融合在内的技术将预测多模式的缺陷和出院处置 对挑战性运动的皮肤电反应是一个尚未探索的领域。 提供对损伤的动机和情感反应的洞察 恢复的轨迹可能是。 在两天的采样期间捕获的受影响手臂的总体激活度较低且缺乏情感。 对挑战性运动的反应将与较差的恢复和出院处置有关。 在本研究中，可穿戴传感器将测量的模态是：加速度、表面 我们将使用一组传感器从肌肉电活动和皮肤电反应中获取数据。 单一制造商，有助于同步和方便地收集时间序列数据 医院的日常生活以及运动任务和评估期间将使用生物标记物进行提取。 贝叶斯融合算法，结果将是运动功能和放电处置。 在该项目结束时，我们将证明所提出的传感器阵列可以 提供有关运动能力、对运动挑战的情感反应的有意义的数据，并将 探讨了该数据与排放处置之间的关系。