Comprehensive Stroke Center and Department of Neurology, David Geffen School of Medicine at UCLA, Sukhbaatar District, Khoroo-1, 42-55, 11000 Ulaanbaatar, Mongolia.
Comprehensive Stroke Center and Department of Neurology, David Geffen School of Medicine at UCLA, Sukhbaatar District, Khoroo-1, 42-55, 11000 Ulaanbaatar, Mongolia.
J Stroke Cerebrovasc Dis. 2021 Jul;30(7):105826. doi: 10.1016/j.jstrokecerebrovasdis.2021.105826. Epub 2021 Apr 28.
To survey recent advances in acute stroke symptom automatic detection and Emergency Medical Systems (EMS) alerting by mobile health technologies.
Narrative review RESULTS: Delayed activation of EMS for stroke symptoms by patients and witnesses deprives patients of rapid access to brain-saving therapies and occurs due to public unawareness of stroke features, cognitive and motor deficits produced by the stroke itself, and sleep onset. A promising emerging approach to overcoming the inherent biologic constraints of patient capacity to self-detect and respond to stroke symptoms is continuous monitoring by mobile health technologies with wireless sensors and artificial intelligence recognition systems. This review surveys 11 sensing technologies - accelerometers, gyroscopes, magnetometers, pressure sensors, touch screen and keyboard input detectors, artificial vision, and artificial hearing; and 10 consumer device form factors in which they are increasingly implemented: smartphones, smart speakers, smart watches and fitness bands, smart speakers/voice assistants, home health robots, smart clothing, smart beds, closed circuit television, smart rings, and desktop/laptop/tablet computers.
The increase in computing power, wearable sensors, and mobile connectivity have ushered in an array of mobile health technologies that can transform stroke detection and EMS activation. By continuously monitoring a diverse range of biometric parameters, commercially available devices provide the technologic capability to detect cardinal language, motor, gait, and sensory signs of stroke onset. Intensified translational research to convert the promise of these technologies to validated, accurate real-world deployments are an important next priority for stroke investigation.
调查移动健康技术在急性中风症状自动检测和急救医疗服务（EMS）报警方面的最新进展。
叙述性综述
患者和目击者因公众对中风特征、中风本身引起的认知和运动障碍以及睡眠发作缺乏认识，延迟启动 EMS 对中风症状进行治疗，从而导致患者无法快速获得挽救大脑的治疗。一种有前途的新兴方法是通过移动健康技术，利用无线传感器和人工智能识别系统，对患者进行连续监测。本综述调查了 11 种传感技术——加速度计、陀螺仪、磁力计、压力传感器、触摸屏和键盘输入探测器、人工视觉和人工听觉；以及 10 种消费者设备形式因素，它们越来越多地被应用于智能手机、智能音箱、智能手表和健身带、智能音箱/语音助手、家庭健康机器人、智能服装、智能床、闭路电视、智能戒指和台式机/笔记本电脑/平板电脑。
计算能力、可穿戴传感器和移动连接性的提高带来了一系列移动健康技术，可以改变中风检测和 EMS 激活。通过连续监测多种生物特征参数，市售设备提供了检测中风发作时的语言、运动、步态和感觉等主要迹象的技术能力。加强转化研究，将这些技术的潜力转化为经过验证的、准确的实际应用，是中风研究的下一个重要优先事项。
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