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通过可穿戴设备推进神经科学。

Advancing Neuroscience through Wearable Devices.

机构信息

Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

出版信息

Neuron. 2020 Oct 14;108(1):8-12. doi: 10.1016/j.neuron.2020.09.030.

DOI:10.1016/j.neuron.2020.09.030
PMID:33058768
Abstract

Faster, more reliable, and comfortably wearable personal devices are producing data from biosensors on an unprecedented scale. Combined with context and analytics, these signals hold great promise to advance neuroscience via real-world data. Here, we discuss wearable technology broadly and provide specific examples of activity patterns from electrodermal sensors found during sleep, stress, and seizures.

摘要

更快、更可靠、更舒适的个人可穿戴设备正在以前所未有的规模从生物传感器产生数据。这些信号与上下文和分析相结合,通过真实世界的数据为推进神经科学提供了巨大的潜力。在这里,我们广泛讨论了可穿戴技术,并提供了在睡眠、压力和癫痫发作期间从皮肤电传感器发现的活动模式的具体示例。

相似文献

1
Advancing Neuroscience through Wearable Devices.通过可穿戴设备推进神经科学。
Neuron. 2020 Oct 14;108(1):8-12. doi: 10.1016/j.neuron.2020.09.030.
2
Long-term effects of electrodermal biofeedback training on seizure control in patients with drug-resistant epilepsy: two case reports.电皮肤生物反馈训练对耐药性癫痫患者癫痫控制的长期影响:两例报告。
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Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study.使用可穿戴传感器在现实生活中进行连续压力检测:算法编程竞赛案例研究。
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Skin conductance biofeedback training in adults with drug-resistant temporal lobe epilepsy and stress-triggered seizures: a proof-of-concept study.药物难治性颞叶癫痫和应激性发作成人的皮肤电导生物反馈训练:一项概念验证研究。
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Electrodermal Activity Based Pre-surgery Stress Detection Using a Wrist Wearable.基于皮肤电活动的腕部可穿戴式术前应激检测
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Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity.基于深度支持向量机的皮肤电活动应激状态识别
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Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications .用于可穿戴应用的超低资源皮肤电活动传感器的设计与实现。
Sensors (Basel). 2019 May 29;19(11):2450. doi: 10.3390/s19112450.

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Ambulatory physiological measures obtained under naturalistic urban mobility conditions have acceptable reliability.在自然主义的城市移动性条件下获得的动态生理测量具有可接受的可靠性。
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Seizure Detection Devices.癫痫发作检测设备
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