Field Measures of Functional Tasks for CIT Intervention

描述（由申请方提供）：卒中后上肢麻痹导致许多患者的功能性运动能力受限。最近的干预措施，如限制性运动疗法（CIT）已被证明在临床上对慢性脑卒中患者的功能性运动恢复有很强的效果。然而，评估临床运动治疗结果对家庭和社区的延续目前仅限于患者访谈措施，例如运动活动日志。功能性运动表现的客观远程测量可以帮助临床医生评估CIT和其他干预措施对卒中后运动恢复的结果。PI建议从健康老年人和中风受试者中收集动态加速度（ACC）和肌电图（EMG）数据，以量化其麻痹上肢的功能使用量（AOU）和运动质量（库姆）。从理论的角度来看，少量的组成部分的子运动包括所有的上肢日常生活活动（ADL），PI假设，与流动ACC/EMG系统，他们将能够识别何时和多久ADL是通过识别这些子运动的存在，或功能性运动任务（FMT）。使用以前的研究，其特征在于质量的上肢运动的模式的运动协调和肌肉共激活，他们进一步假设，ACC/EMG数据的分析将揭示受损水平的库姆功能的FMT以及。他们期望AOU和库姆数据与上肢损伤和功能的临床测量相关。 将从进行FMT的5名健康老年人和20名卒中后受试者中收集实验室数据，这些FMT代表进行ADL所需的UE运动。将训练人工神经网络以识别UE FMT并将其与不涉及ADL的上肢运动（例如伴随Ambassador的周期性手臂运动）区分开。麻痹性上肢库姆将通过识别协调模式来确定，例如来自ACC数据的肌肉共激活和运动学特征的EMG测量。将通过记录在非实验室环境中进行ADL随机序列的受试者的ACC/EMG数据，对该系统的响应性进行进一步验证。将使用上肢运动损伤和功能的标准临床量表（如FugI-Meyer、Wolf运动功能测试和运动活动日志）评估受试者。PI预计，当他们完成这项门诊研究时，远程和非侵入性ACC/EMG数据收集技术将可用。在试点中开发的用于监测麻痹性上肢AOU和库姆的技术将形成更大研究的基础。他们将远程监测接受CIT的患者在诊所外、家中和社区的上肢功能使用情况。

DESCRIPTION (provided by the applicant): Paresis of the upper limb after stroke results in limited functional movement ability for many patients. Recent interventions such as Constraint Induced Movement Therapy (CIT) have demonstrated strong effects on the recovery of functional movement for chronic stroke patients in the clinic. Assessing the carryover of clinical exercise therapy outcomes to the home and community is currently limited, however, to patient interview measures such as the Motor Activity Log. Objective remote measurement of functional movement performance could assist clinicians in evaluating the outcomes of CIT and other interventions for movement recovery after stroke. The PIs propose to collect ambulatory accelerometry (ACC) and electromyographic (EMG) data from healthy elderly and stroke subjects in order to quantify the functional amount of use (AOU) and quality of movement (QOM) in their paretic upper limbs. From the theoretical perspective that a small number of component submovements comprise all upper limb activities of daily living (ADL), the PIs hypothesize that with the ambulatory ACC/EMG system they will be able to identify when and for how long an ADL is performed by recognition of the presence of these submovements, or Functional Motor Tasks (FMT). Using previous studies that have characterized the quality of upper limb movement by patterns of kinematic coordination and muscle coactivation, they further hypothesize that analysis of the ACC/EMG data will reveal impairment level QOM features of FMT as well. They expect that this AOU and QOM data will correlate with clinical measures of upper limb impairment and function. Laboratory data will be collected from 5 healthy elderly and 20 post-stroke subjects performing FMT that are representative of UE movements necessary for the performance of ADL. Artificial neural networks will be trained to recognize the UE FMT and distinguish them from upper limb movements not involved with ADL (such as cyclical arm movement accompanying ambulation). Paretic upper limb QOM will be determined by identifying patterns of coordination such as EMG measures of muscle coactivation and kinematic features from ACC data. Further validation of the responsiveness of this system will be performed by recording ACC/EMG data of subjects performing a random sequence of ADLs in a non-laboratory setting. Subjects will be assessed using standard clinical scales of upper limb motor impairment and function such as the FugI-Meyer, the Wolf Motor Function Test, and the Motor Activity Log. The PIs anticipate that the technology for remote and unobtrusive ACC/EMG data collection will be available when they have completed this ambulatory study. Techniques developed in the pilot for monitoring paretic upper limb AOU and QOM will form the basis of a larger study. They will remotely monitor functional upper limb use outside the clinic in the homes and in the community of patients undergoing CIT.