Field Measures of Functional Tasks for CIT Intervention

描述(由申请人提供)：中风后上肢瘫痪导致许多患者功能运动能力受限。最近的干预措施，如强制诱导运动疗法(CIT)，在临床上证明了对慢性中风患者功能运动的恢复有很强的效果。然而，目前评估临床运动治疗结果带到家庭和社区的方法仅限于患者访谈方法，如运动活动日志。目的遥测功能运动功能可帮助临床医生评估CIT及其他干预措施对卒中后运动功能恢复的影响。PIS建议收集健康老年人和中风患者的动态加速度计(ACC)和肌电(EMG)数据，以量化他们瘫痪上肢的功能使用量(AOU)和运动质量(QOM)。从理论上看，少量的成分子运动组成了所有的上肢日常生活活动(ADL)，PI假设，通过动态的ACC/EMG系统，他们将能够通过识别这些子运动或功能性运动任务(FMT)的存在来识别何时以及持续多长时间进行ADL。利用以前的研究，通过运动学协调和肌肉共同激活的模式来表征上肢运动的质量，他们进一步假设，对ACC/EMG数据的分析也将揭示FMT的损害程度QOM特征。他们预计，这一AOU和QOM数据将与上肢损伤和功能的临床测量相关。 实验室数据将从5名健康的老年人和20名中风后进行FMT的受试者中收集，这些受试者代表了进行ADL所必需的UE运动。人工神经网络将被训练来识别UE FMT，并将它们与与ADL无关的上肢运动(如伴随行走的周期性手臂运动)区分开来。瘫痪的上肢QOM将通过识别协调模式来确定，例如来自ACC数据的肌肉协同激活的肌电测量和运动学特征。将通过记录在非实验室环境中执行ADL随机序列的受试者的ACC/EMG数据来进一步验证该系统的响应性。受试者将使用上肢运动损伤和功能的标准临床量表进行评估，如Fgi-Meyer、Wolf运动功能测试和运动活动日志。PI预计，当他们完成这项动态研究后，远程和非显眼的ACC/EMG数据收集技术将可用。飞行员开发的监测偏瘫上肢AOU和QOM的技术将构成更大规模研究的基础。他们将在诊所外、家中和接受CIT的患者社区远程监控上肢功能的使用。

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.