Oubre Brandon, Daneault Jean-Francois, Jung Hee-Tae, Park Joonwoo, Ryu Taekyeong, Kim Yangsoo, Lee Sunghoon Ivan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3719-3722. doi: 10.1109/EMBC44109.2020.9175708.
Stroke is a major cause of long-term disability. Because patients recovering from stroke often perform differently in clinical settings than in their naturalistic environments, remote monitoring of motor performance is needed to evaluate the true impact of prescribed therapies. Wearable sensors have been considered as a technical solution to this problem, but most existing systems focus on measuring the amount of movement without considering the quality of movement. We present a novel method to seamlessly and unobtrusively measure the quality of individual reaching movements by leveraging a motor control theory that describes how the central nervous system plans and executes movements. We trained and evaluated our system on 19 stroke survivors to estimate the Functional Ability Scale (FAS) of reaching movements. The analysis showed that we can estimate the FAS scores of reaching movements, with some confusion between adjacent scores. Furthermore, we estimated the average FAS scores of subjects with a normalized root mean square error (NRMSE) of 22.5%. Though our model's high error on two severe subjects influenced our overall estimation performance, we could accurately estimate scores in most of the mild-to-moderate subjects (NRMSE of 13.1% without the outliers). With further development and testing, we believe the proposed technique can be applied to monitor patient recovery in home and community settings.
中风是导致长期残疾的主要原因。由于从中风恢复的患者在临床环境中的表现往往与在自然环境中不同,因此需要对运动表现进行远程监测,以评估规定治疗的真正效果。可穿戴传感器被认为是解决这一问题的技术方案,但大多数现有系统只专注于测量运动量,而不考虑运动质量。我们提出了一种新颖的方法,通过利用一种描述中枢神经系统如何规划和执行运动的运动控制理论,无缝且不引人注意地测量个体伸手动作的质量。我们在19名中风幸存者身上训练并评估了我们的系统,以估计伸手动作的功能能力量表(FAS)。分析表明,我们能够估计伸手动作的FAS分数,相邻分数之间存在一些混淆。此外,我们估计受试者的平均FAS分数时,归一化均方根误差(NRMSE)为22.5%。尽管我们的模型在两名严重受试者上的高误差影响了整体估计性能,但我们能够在大多数轻度至中度受试者中准确估计分数(去除异常值后NRMSE为13.1%)。随着进一步的开发和测试,我们相信所提出的技术可应用于家庭和社区环境中监测患者的康复情况。