• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

使用商用硬件持续检测生理应激

Continuous Detection of Physiological Stress with Commodity Hardware.

作者信息

Mishra Varun, Pope Gunnar, Lord Sarah, Lewia Stephanie, Lowens Byron, Caine Kelly, Sen Sougata, Halter Ryan, Kotz David

机构信息

Dartmouth College.

University of Massachusetts Amherst.

出版信息

ACM Trans Comput Healthc. 2020 Apr;1(2). doi: 10.1145/3361562.

DOI:10.1145/3361562
PMID:32832933
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7442214/
Abstract

Timely detection of an individual's stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer's stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an 1-score of up to 0.87 in the lab and 0.66 in the field, on par with clinical-grade sensors.

摘要

及时检测个人的压力水平有助于改善压力管理,从而降低因压力管理不善可能产生的不良健康后果的风险。可穿戴传感技术的最新进展带来了多种检测和监测压力的方法,其准确性各不相同。然而,最准确的方法依赖于临床级传感器来测量生理信号;它们通常体积庞大、定制且昂贵,因此限制了研究人员和普通大众对其的采用。在本文中,我们探讨了商用现成传感器用于压力监测的可行性。其理念是能够使用廉价的非临床传感器来捕捉生理信号,并基于该数据推断佩戴者的压力水平。我们描述了一个涉及一款流行的现成心率监测器——博能H7的系统;我们在一个有三种经过充分验证的压力诱导刺激的受控实验室环境以及自由生活的野外条件下,对26名参与者的系统进行了评估。我们的分析表明,仅使用现成传感器,我们在实验室中能够以高达0.87的I分数检测到压力事件,在野外为0.66,与临床级传感器相当。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/f7264d05fd98/nihms-1601275-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/421f9bce8416/nihms-1601275-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/48bb02fc0c4c/nihms-1601275-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/95ae14ebc399/nihms-1601275-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/d21469afe8ab/nihms-1601275-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/5f82ead21002/nihms-1601275-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/115535f65db8/nihms-1601275-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/e2aa08cc24da/nihms-1601275-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/9a3661593da1/nihms-1601275-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/820031148af4/nihms-1601275-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/f7264d05fd98/nihms-1601275-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/421f9bce8416/nihms-1601275-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/48bb02fc0c4c/nihms-1601275-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/95ae14ebc399/nihms-1601275-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/d21469afe8ab/nihms-1601275-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/5f82ead21002/nihms-1601275-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/115535f65db8/nihms-1601275-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/e2aa08cc24da/nihms-1601275-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/9a3661593da1/nihms-1601275-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/820031148af4/nihms-1601275-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6bc6/7442214/f7264d05fd98/nihms-1601275-f0010.jpg

相似文献

1
Continuous Detection of Physiological Stress with Commodity Hardware.使用商用硬件持续检测生理应激
ACM Trans Comput Healthc. 2020 Apr;1(2). doi: 10.1145/3361562.
2
3
Multichannel ECG recording from waist using textile sensors.使用纺织传感器从腰部进行多通道心电图记录。
Biomed Eng Online. 2020 Jun 16;19(1):48. doi: 10.1186/s12938-020-00788-x.
4
EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments.EarBit:利用可穿戴传感器在无约束环境中检测进食行为。
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2017 Sep;1(3). doi: 10.1145/3130902.
5
Continuous monitoring of body temperature for objective detection of health and safety risks in construction sites: An analysis of the accuracy and comfort of off-the-shelf wearable sensors.持续监测体温以客观检测建筑工地的健康与安全风险:对现成可穿戴传感器的准确性和舒适性分析
Heliyon. 2024 Mar 3;10(6):e26947. doi: 10.1016/j.heliyon.2024.e26947. eCollection 2024 Mar 30.
6
Counting Bites With Bits: Expert Workshop Addressing Calorie and Macronutrient Intake Monitoring.用数字计算摄入量:解决卡路里和宏量营养素摄入监测问题的专家研讨会
J Med Internet Res. 2019 Dec 4;21(12):e14904. doi: 10.2196/14904.
7
Evaluating the Reproducibility of Physiological Stress Detection Models.评估生理应激检测模型的可重复性。
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020 Dec;4(4). doi: 10.1145/3432220. Epub 2020 Dec 18.
8
Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App: Within-Subject Design Using Continuous Monitoring.通过可穿戴设备和智能手机应用程序测量的心率变异性对实习生压力源-压力过程的调节:采用连续监测的受试者内设计
JMIR Cardio. 2021 Oct 4;5(2):e28731. doi: 10.2196/28731.
9
Using Wearable Devices and Speech Data for Personalized Machine Learning in Early Detection of Mental Disorders: Protocol for a Participatory Research Study.利用可穿戴设备和语音数据进行精神障碍早期检测的个性化机器学习:一项参与性研究方案
JMIR Res Protoc. 2023 Nov 13;12:e48210. doi: 10.2196/48210.
10
SmokeMon: Unobtrusive Extraction of Smoking Topography Using Wearable Energy-Efficient Thermal.烟雾监测器:利用可穿戴节能热传感器进行不引人注意的吸烟行为特征提取
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2022 Dec;6(4). doi: 10.1145/3569460. Epub 2023 Jan 11.

引用本文的文献

1
Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.移动健康传感中主动和被动数据收集的当前挑战与机遇:一项范围综述
JAMIA Open. 2025 Jul 18;8(4):ooaf025. doi: 10.1093/jamiaopen/ooaf025. eCollection 2025 Aug.
2
Building an open-source community to enhance autonomic nervous system signal analysis: DBDP-autonomic.构建开源社区以增强自主神经系统信号分析:DBDP-自主神经系统分析项目
Front Digit Health. 2025 Jan 9;6:1467424. doi: 10.3389/fdgth.2024.1467424. eCollection 2024.
3
Lessons From 3 Longitudinal Sensor-Based Human Behavior Assessment Field Studies and an Approach to Support Stakeholder Management: Content Analysis.

本文引用的文献

1
Experience: Design, Development and Evaluation of a Wearable Device for mHealth Applications.经验:一款用于移动健康应用的可穿戴设备的设计、开发与评估。
Proc Annu Int Conf Mob Comput Netw. 2019 Aug;2019. doi: 10.1145/3300061.3345432. Epub 2019 Oct 11.
2
StressHacker: Towards Practical Stress Monitoring in the Wild with Smartwatches.压力黑客:利用智能手表在现实环境中实现实用的压力监测。
AMIA Annu Symp Proc. 2018 Apr 16;2017:830-838. eCollection 2017.
3
An Overview of Heart Rate Variability Metrics and Norms.心率变异性指标与规范概述
基于传感器的 3 项纵向人体行为评估现场研究的经验教训及一种支持利益相关者管理的方法:内容分析。
J Med Internet Res. 2024 Oct 31;26:e50461. doi: 10.2196/50461.
4
Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices.利用生理数据预测压力水平:使用可穿戴设备的实时压力预测模型。
AIMS Neurosci. 2024 Apr 19;11(2):76-102. doi: 10.3934/Neuroscience.2024006. eCollection 2024.
5
Perceived stress and associations between physical activity, sedentary time, and interstitial glucose in healthy adolescents.健康青少年的感知压力以及身体活动、久坐时间与组织间葡萄糖之间的关联。
Physiol Behav. 2024 Sep 1;283:114617. doi: 10.1016/j.physbeh.2024.114617. Epub 2024 Jun 16.
6
Explainable stress type classification captures physiologically relevant responses in the Maastricht Acute Stress Test.可解释的应激类型分类在马斯特里赫特急性应激测试中捕捉到生理相关反应。
Front Neuroergon. 2023 Dec 5;4:1294286. doi: 10.3389/fnrgo.2023.1294286. eCollection 2023.
7
Heart Rate Variability in Concussed College Athletes: Follow-Up Study and Biological Sex Differences.脑震荡大学生运动员的心率变异性:随访研究及生物性别差异
Brain Sci. 2023 Dec 1;13(12):1669. doi: 10.3390/brainsci13121669.
8
Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors.使用可穿戴传感器通过生物信号预测情绪:分类和回归模型在评估情感效价和唤醒水平方面的比较。
Sensors (Basel). 2023 Feb 1;23(3):1598. doi: 10.3390/s23031598.
9
Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines.利用生理信号和机器学习管道检测早期生活压力
Biology (Basel). 2023 Jan 6;12(1):91. doi: 10.3390/biology12010091.
10
Evaluating the Reproducibility of Physiological Stress Detection Models.评估生理应激检测模型的可重复性。
Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020 Dec;4(4). doi: 10.1145/3432220. Epub 2020 Dec 18.
Front Public Health. 2017 Sep 28;5:258. doi: 10.3389/fpubh.2017.00258. eCollection 2017.
4
Monitoring stress with a wrist device using context.使用情境通过腕部设备监测压力。
J Biomed Inform. 2017 Sep;73:159-170. doi: 10.1016/j.jbi.2017.08.006. Epub 2017 Aug 10.
5
: An App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device.一款用于在护身符腕戴设备上进行实时日常活动水平监测的应用程序。
Proc IEEE Int Conf Pervasive Comput Commun Workshops. 2017 Mar;2017. doi: 10.1109/PERCOMW.2017.7917601. Epub 2017 May 4.
6
Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research - Recommendations for Experiment Planning, Data Analysis, and Data Reporting.心理生理学研究中的心率变异性与心脏迷走神经张力——实验规划、数据分析和数据报告建议
Front Psychol. 2017 Feb 20;8:213. doi: 10.3389/fpsyg.2017.00213. eCollection 2017.
7
Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data.在快速变化的移动传感器数据的不连续时间序列中寻找显著压力事件。
Proc SIGCHI Conf Hum Factor Comput Syst. 2016 May;2016:4489-4501. doi: 10.1145/2858036.2858218.
8
Automatic identification of artifacts in electrodermal activity data.皮肤电活动数据中伪迹的自动识别。
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:1934-7. doi: 10.1109/EMBC.2015.7318762.
9
cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment.cStress:迈向移动环境中连续压力评估的黄金标准。
Proc ACM Int Conf Ubiquitous Comput. 2015 Sep;2015:493-504. doi: 10.1145/2750858.2807526.
10
Automatic Stress Detection in Working Environments From Smartphones' Accelerometer Data: A First Step.利用智能手机加速度计数据在工作环境中进行自动压力检测:第一步。
IEEE J Biomed Health Inform. 2016 Jul;20(4):1053-60. doi: 10.1109/JBHI.2015.2446195. Epub 2015 Jun 16.