Geffen School of Medicine at UCLA, Department of Neurology, Reed Neurologic Research Center, 710 Westwood Plaza, Los Angeles, CA, 90095-1769, USA.
Curr Neurol Neurosci Rep. 2018 Oct 6;18(12):87. doi: 10.1007/s11910-018-0896-5.
Measurements obtained during real-world activity by wearable motion sensors may contribute more naturalistic accounts of clinically meaningful changes in impairment, activity, and participation during neurologic rehabilitation, but obstacles persist. Here we review the basics of wearable sensors, the use of existing systems for neurological and rehabilitation applications and their limitations, and strategies for future use.
Commercial activity-recognition software and wearable motion sensors for community monitoring primarily calculate steps and sedentary time. Accuracy declines as walking speed slows below 0.8 m/s, less so if worn on the foot or ankle. Upper-extremity sensing is mostly limited to simple inertial activity counts. Research software and activity-recognition algorithms are beginning to provide ground truth about gait cycle variables and reveal purposeful arm actions. Increasingly, clinicians can incorporate inertial and other motion signals to monitor exercise, activities of daily living, and the practice of specific skills, as well as provide tailored feedback to encourage self-management of rehabilitation. Efforts are growing to create a compatible collection of clinically relevant sensor applications that capture the type, quantity, and quality of everyday activity and practice in known contexts. Such data would offer more ecologically sound measurement tools, while enabling clinicians to monitor and support remote physical therapies and behavioral modification when combined with telemedicine outreach.
可穿戴运动传感器在真实活动中获得的测量结果，可能有助于更自然地描述神经康复过程中损伤、活动和参与方面的临床有意义变化，但仍存在障碍。本文回顾了可穿戴传感器的基础知识、现有系统在神经和康复应用中的使用情况及其局限性，以及未来使用的策略。
用于社区监测的商业活动识别软件和可穿戴运动传感器主要计算步数和久坐时间。当步行速度低于 0.8m/s 时，准确性会下降，如果佩戴在脚部或脚踝上，则下降幅度较小。上肢感应主要限于简单的惯性活动计数。研究软件和活动识别算法开始提供有关步态周期变量的真实情况，并揭示有目的的手臂动作。越来越多的临床医生可以将惯性和其他运动信号结合起来，监测运动、日常生活活动以及特定技能的练习情况，并提供针对性的反馈，以鼓励康复的自我管理。人们正在努力创建一个兼容的临床相关传感器应用集合，以在已知环境中捕获日常活动和练习的类型、数量和质量。此类数据将提供更符合生态学的测量工具，同时使临床医生能够在结合远程医疗服务的情况下，远程监测和支持物理治疗和行为矫正。
