• 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

基于小波的肥胖成年人可穿戴传感器活动和睡眠运动数据的分析。

Wavelet-Based Analysis of Physical Activity and Sleep Movement Data from Wearable Sensors among Obese Adults.

机构信息

Department of Physical Therapy, Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA 92618, USA.

Department of Physical Therapy, California State University, Long Beach, 1250 Bellflower Blvd, Long Beach, CA 90840, USA.

出版信息

Sensors (Basel). 2019 Aug 27;19(17):3710. doi: 10.3390/s19173710.

DOI:10.3390/s19173710
PMID:31461827
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6749575/
Abstract

Decreased physical activity in obese individuals is associated with a prevalence of cardiovascular and metabolic disorders. Physicians usually recommend that obese individuals change their lifestyle, specifically changes in diet, exercise, and other physical activities for obesity management. Therefore, understanding physical activity and sleep behavior is an essential aspect of obesity management. With innovations in mobile and electronic health care technologies, wearable inertial sensors have been used extensively over the past decade for monitoring human activities. Despite significant progress with the wearable inertial sensing technology, there is a knowledge gap among researchers regarding how to analyze longitudinal multi-day inertial sensor data to explore activities of daily living (ADL) and sleep behavior. The purpose of this study was to explore new clinically relevant metrics using movement amplitude and frequency from longitudinal wearable sensor data in obese and non-obese young adults. We utilized wavelet analysis to determine movement frequencies on longitudinal multi-day wearable sensor data. In this study, we recruited 10 obese and 10 non-obese young subjects. We found that obese participants performed more low-frequency (0.1 Hz) movements and fewer movements of high frequency (1.1-1.4 Hz) compared to non-obese counterparts. Both obese and non-obese subjects were active during the 00:00-06:00 time interval. In addition, obesity affected sleep with significantly fewer transitions, and obese individuals showed low values of root mean square transition accelerations throughout the night. This study is critical for obesity management to prevent unhealthy weight gain by the recommendations of physical activity based on our results. Longitudinal multi-day monitoring using wearable sensors has great potential to be integrated into routine health care checkups to prevent obesity and promote physical activities.

摘要

肥胖人群体力活动减少与心血管和代谢紊乱的患病率有关。医生通常建议肥胖者改变生活方式，特别是改变饮食、锻炼和其他针对肥胖管理的体育活动。因此，了解体力活动和睡眠行为是肥胖管理的重要方面。随着移动和电子医疗保健技术的创新，过去十年中，可穿戴惯性传感器已被广泛用于监测人体活动。尽管可穿戴惯性传感技术取得了重大进展，但研究人员在如何分析纵向多天惯性传感器数据以探索日常生活活动（ADL）和睡眠行为方面仍存在知识差距。本研究旨在探索使用肥胖和非肥胖年轻成年人纵向可穿戴传感器数据中的运动幅度和频率来获得新的临床相关指标。我们利用小波分析来确定纵向多天可穿戴传感器数据中的运动频率。在这项研究中，我们招募了 10 名肥胖者和 10 名非肥胖者。我们发现肥胖参与者进行的低频（0.1Hz）运动较多，高频（1.1-1.4Hz）运动较少。与非肥胖者相比，肥胖者和非肥胖者在 00:00-06:00 时间间隔内都很活跃。此外，肥胖会影响睡眠，导致睡眠过程中的转换次数明显减少，并且肥胖者在整个晚上的均方根过渡加速度值都较低。这项研究对于肥胖管理至关重要，可以根据我们的研究结果，通过基于体力活动的建议来预防不健康的体重增加。使用可穿戴传感器进行纵向多天监测具有很大的潜力，可以整合到常规健康检查中，以预防肥胖和促进体育活动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/97ffef677ce7/sensors-19-03710-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/422fa6daef21/sensors-19-03710-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/82e231939124/sensors-19-03710-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/d314911bfb57/sensors-19-03710-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/812aa88663d4/sensors-19-03710-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/991e712fe54b/sensors-19-03710-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/93ca1354248c/sensors-19-03710-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/1257c299c715/sensors-19-03710-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/6fb222f6c14a/sensors-19-03710-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/4e3252134eca/sensors-19-03710-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/be553f21dba3/sensors-19-03710-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/1a2ec18337d0/sensors-19-03710-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/3d78d7382a0b/sensors-19-03710-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/a36a4df6bcc2/sensors-19-03710-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/f1195942c3cc/sensors-19-03710-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/00dd6bf4b0d6/sensors-19-03710-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/46f74730cc02/sensors-19-03710-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/7c144ece9bca/sensors-19-03710-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/a3a5cccd7248/sensors-19-03710-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/68e754c10169/sensors-19-03710-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/97ffef677ce7/sensors-19-03710-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/422fa6daef21/sensors-19-03710-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/82e231939124/sensors-19-03710-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/d314911bfb57/sensors-19-03710-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/812aa88663d4/sensors-19-03710-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/991e712fe54b/sensors-19-03710-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/93ca1354248c/sensors-19-03710-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/1257c299c715/sensors-19-03710-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/6fb222f6c14a/sensors-19-03710-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/4e3252134eca/sensors-19-03710-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/be553f21dba3/sensors-19-03710-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/1a2ec18337d0/sensors-19-03710-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/3d78d7382a0b/sensors-19-03710-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/a36a4df6bcc2/sensors-19-03710-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/f1195942c3cc/sensors-19-03710-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/00dd6bf4b0d6/sensors-19-03710-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/46f74730cc02/sensors-19-03710-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/7c144ece9bca/sensors-19-03710-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/a3a5cccd7248/sensors-19-03710-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/68e754c10169/sensors-19-03710-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d595/6749575/97ffef677ce7/sensors-19-03710-g020.jpg

相似文献

1
Wavelet-Based Analysis of Physical Activity and Sleep Movement Data from Wearable Sensors among Obese Adults.基于小波的肥胖成年人可穿戴传感器活动和睡眠运动数据的分析。
Sensors (Basel). 2019 Aug 27;19(17):3710. doi: 10.3390/s19173710.
2
The effect of weight management interventions that include a diet component on weight-related outcomes in pregnant and postpartum women: a systematic review protocol.包含饮食成分的体重管理干预措施对孕妇和产后女性体重相关结局的影响：一项系统评价方案
JBI Database System Rev Implement Rep. 2015 Jan;13(1):88-98. doi: 10.11124/jbisrir-2015-1812.
3
A Wide-Range, Wireless Wearable Inertial Motion Sensing System for Capturing Fast Athletic Biomechanics in Overhead Pitching.一种用于捕捉头顶投掷中快速运动生物力学的宽范围、无线可穿戴惯性运动感测系统。
Sensors (Basel). 2019 Aug 21;19(17):3637. doi: 10.3390/s19173637.
4
Electronic Skin Wearable Sensors for Detecting Lumbar-Pelvic Movements.电子皮肤可穿戴传感器，用于检测腰骨盆运动。
Sensors (Basel). 2020 Mar 9;20(5):1510. doi: 10.3390/s20051510.
5
Postural Transitions during Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty during Unsupervised Condition.日常活动中的姿势转换可以识别虚弱状态：可穿戴技术在非监督条件下识别虚弱的应用。
Gerontology. 2017;63(5):479-487. doi: 10.1159/000460292. Epub 2017 Mar 11.
6
Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study.利用可穿戴惯性传感器识别成年人的日常活动：深度学习方法研究
JMIR Med Inform. 2024 Aug 9;12:e57097. doi: 10.2196/57097.
7
Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.利用可穿戴传感器机器学习模型提取日常生活中有氧运动系统的动态。
J Appl Physiol (1985). 2018 Feb 1;124(2):473-481. doi: 10.1152/japplphysiol.00299.2017. Epub 2017 Jun 8.
8
Quantifying Caregiver Movement when Measuring Infant Movement across a Full Day: A Case Report.量化全天婴儿活动中照料者的活动量：病例报告。
Sensors (Basel). 2019 Jun 29;19(13):2886. doi: 10.3390/s19132886.
9
A Combination of Indoor Localization and Wearable Sensor-Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results.室内定位与基于可穿戴传感器的身体活动识别相结合，以评估接受亚急性康复治疗的老年患者：基线研究结果。
JMIR Mhealth Uhealth. 2019 Jul 10;7(7):e14090. doi: 10.2196/14090.
10
Investigating the within-person relationships between activity levels and sleep duration using Fitbit data.利用 Fitbit 数据研究个体内活动水平与睡眠时间之间的关系。
Transl Behav Med. 2021 Mar 16;11(2):619-624. doi: 10.1093/tbm/ibaa071.

引用本文的文献

1
Can Wearable Inertial Measurement Units Be Used to Measure Sleep Biomechanics? Establishing Initial Feasibility and Validity.可穿戴惯性测量单元能否用于测量睡眠生物力学？确立初步可行性与有效性。
Biomimetics (Basel). 2022 Dec 21;8(1):2. doi: 10.3390/biomimetics8010002.
2
Visualization-Driven Time-Series Extraction from Wearable Systems Can Facilitate Differentiation of Passive ADL Characteristics among Stroke and Healthy Older Adults.基于可视化的可穿戴系统时间序列提取有助于区分卒中患者与健康老年人的被动日常生活活动特征。
Sensors (Basel). 2022 Jan 13;22(2):598. doi: 10.3390/s22020598.
3
Prediction of fall risk among community-dwelling older adults using a wearable system.

本文引用的文献

1
Sleep deprivation and obesity in adults: a brief narrative review.成人睡眠剥夺与肥胖：简要叙述性综述
BMJ Open Sport Exerc Med. 2018 Oct 4;4(1):e000392. doi: 10.1136/bmjsem-2018-000392. eCollection 2018.
2
Wearable sensors for the monitoring of movement disorders.可穿戴传感器在运动障碍监测中的应用。
Biomed J. 2018 Aug;41(4):249-253. doi: 10.1016/j.bj.2018.06.003. Epub 2018 Sep 11.
3
The impact of wearable motion sensing technology on physical activity in older adults.可穿戴运动感应技术对老年人身体活动的影响。
使用可穿戴系统预测社区居住的老年人跌倒风险。
Sci Rep. 2021 Oct 25;11(1):20976. doi: 10.1038/s41598-021-00458-5.
Exp Gerontol. 2018 Oct 2;112:9-19. doi: 10.1016/j.exger.2018.08.002. Epub 2018 Aug 10.
4
A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations.对消费级可穿戴设备、移动应用程序以及为体育活动人群提供生物反馈、监测压力和睡眠的设备的批判性综述。
Front Physiol. 2018 Jun 28;9:743. doi: 10.3389/fphys.2018.00743. eCollection 2018.
5
Comparison of Wearable Trackers' Ability to Estimate Sleep.可穿戴追踪器估计睡眠能力的比较。
Int J Environ Res Public Health. 2018 Jun 15;15(6):1265. doi: 10.3390/ijerph15061265.
6
Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults.全球 1975 年至 2016 年的体重指数、消瘦、超重和肥胖趋势：12890 万儿童、青少年和成年人 2416 项基于人群的测量研究的汇总分析。
Lancet. 2017 Dec 16;390(10113):2627-2642. doi: 10.1016/S0140-6736(17)32129-3. Epub 2017 Oct 10.
7
Muscle strength and body composition in severe obesity.重度肥胖者的肌肉力量与身体组成
Clinics (Sao Paulo). 2017 May;72(5):272-275. doi: 10.6061/clinics/2017(05)03.
8
The impact of obesity on skeletal muscle strength and structure through adolescence to old age.从青春期到老年期，肥胖对骨骼肌力量和结构的影响。
Biogerontology. 2016 Jun;17(3):467-83. doi: 10.1007/s10522-015-9626-4. Epub 2015 Dec 14.
9
The Relationship Between Time of Day of Physical Activity and Obesity in Older Women.体力活动时间与老年女性肥胖的关系。
J Phys Act Health. 2016 Apr;13(4):416-8. doi: 10.1123/jpah.2015-0152. Epub 2015 Oct 7.
10
Prevalence of Overweight and Obesity in the United States, 2007-2012.2007 - 2012年美国超重和肥胖的患病率
JAMA Intern Med. 2015 Aug;175(8):1412-3. doi: 10.1001/jamainternmed.2015.2405.