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

中风的终生风险为六分之一，全世界估计有3300万中风幸存者。理想急性 中风患者将获得关于运动功能恢复的准确和快速的预后， 通过应用那些最能改善它的疗法。然而，关于急性期后治疗的决定， 对中风患者进行短期功能评估， 治疗、一天中的时间、动机和其他因素。这些评估往往被推迟， 延迟康复治疗。有一些重要的决定需要作出的设置 如果需要的话，在哪里进行康复，以及中风患者在哪里长期生活最好。这 该研究项目旨在显著增加目前对生物标志物的理解， 为退伍军人提供更好的诊断、康复治疗和长期处置建议， 中风导致的上肢损伤。我们旨在解决的知识差距是 1之间的未知关系。卒中后即刻运动和功能能力，和2.之间 同情语气和对残疾的心理反应。临床医生还不知道如何使用这些数据 从可穿戴技术，测量这些因素-一个问题所产生的数据量 缺乏可靠的生物标志物。我们的中心假设是， 对来自患者佩戴多个小时的多模式传感器阵列的数据的学习技术可以 提供更好的运动能力证据，评估潜在的心理因素，预测康复轨迹 比传统的短期评估更好。它还可以允许更快速的个性化治疗 基于通过传感器数据发现的真实世界缺陷的计划。我们将检验我们的中心假设 通过追求以下两个具体目标和相关的工作假设： 1.从可穿戴惯性、肌电和 皮肤电传感器阵列工作假设：一些战略性放置的传感器可以捕获 自主神经系统的功能运动和状态。运动和生理测量 在任务执行过程中采取的将与运动障碍和功能状态相关。完成 这一目标将导致识别的功能变量来自多模态传感器 测量和证明的可行性，和挑战，住院病人使用传感器阵列。 2.根据传感器阵列衍生变量预测急性中风的关键临床结局 住院患者接受出院后治疗评估。工作假设：机器学习 技术，包括贝叶斯融合，将预测赤字和放电处置从多模式 收集的变量。对挑战性运动的皮电反应是一个未探索的领域， 提供对动机和对损伤的情感反应的洞察。复苏的轨迹可能是 在为期两天的采样期间捕获。受影响手臂的整体低激活和缺乏情感 对挑战性运动的反应将与较差的恢复和出院处置有关。 在这项研究中，将由可穿戴传感器测量的模态是：加速度、表面张力、 肌肉电活动和皮肤电反应。我们将使用一套传感器从一个 单个制造商，有助于同步和方便收集时间序列的数据， 在医院的日常生活中，以及在运动任务和评估期间。生物标志物将使用 贝叶斯融合算法，结果将是运动功能和放电处置。 在这个项目的结论，我们将证明，拟议的传感器阵列可以 提供关于运动能力、对运动挑战的情感反应的有意义的数据，并将具有 探索了这些数据和出院处置之间的关系。

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.