• 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

Detection of real-life activities by a tri-axial accelerometer worn at different body locations: Analysis and interpretation.

作者信息

Porta Massimo, Chiesa Mario, Fornengo Paolo, Franceschini Marta, Tricarico Lucia, Mazzeo Aurora, Di Leva Anna, Bertello Stefania, Clerico Alessandra, Oleandri Salvatore, Trento Marina

机构信息

Laboratory of Clinical Pedagogy, Department of Medical Sciences, University of Turin, Turin, Italy.

Links Foundation, Turin, Italy.

出版信息

Diabet Med. 2021 Oct;38(10):e14609. doi: 10.1111/dme.14609. Epub 2021 Jun 10.

DOI:10.1111/dme.14609
PMID:34043833
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8518063/
Abstract
摘要

相似文献

1
Detection of real-life activities by a tri-axial accelerometer worn at different body locations: Analysis and interpretation.通过佩戴在身体不同部位的三轴加速度计检测现实生活活动：分析与解读。
Diabet Med. 2021 Oct;38(10):e14609. doi: 10.1111/dme.14609. Epub 2021 Jun 10.
2
Estimation of Physical Activity Energy Expenditure during Free-Living from Wrist Accelerometry in UK Adults.通过手腕加速度计估算英国成年人自由生活期间的身体活动能量消耗
PLoS One. 2016 Dec 9;11(12):e0167472. doi: 10.1371/journal.pone.0167472. eCollection 2016.
3
How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?你的腕部设备识别日常活动和检测跌倒的准确性如何？
Sensors (Basel). 2016 Jun 1;16(6):800. doi: 10.3390/s16060800.
4
Wrist-worn triaxial accelerometry predicts the energy expenditure of non-vigorous daily physical activities.腕部佩戴的三轴加速度计可预测非剧烈日常身体活动的能量消耗。
J Sci Med Sport. 2017 Aug;20(8):761-765. doi: 10.1016/j.jsams.2017.01.233. Epub 2017 Jan 25.
5
Validation of automatic wear-time detection algorithms in a free-living setting of wrist-worn and hip-worn ActiGraph GT3X.腕戴和髋戴 ActiGraph GT3X 在非实验室自由活动环境中自动佩戴时间检测算法的验证。
BMC Public Health. 2019 Feb 28;19(1):244. doi: 10.1186/s12889-019-6568-9.
6
On Placement, Location and Orientation of Wrist-Worn Tri-Axial Accelerometers during Free-Living Measurements.腕戴三轴加速度计在自由生活测量中的位置、定位和方向。
Sensors (Basel). 2019 May 6;19(9):2095. doi: 10.3390/s19092095.
7
Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn accelerometer.腕戴加速度计的体力活动强度预测的交叉验证和样本外测试。
J Appl Physiol (1985). 2018 May 1;124(5):1284-1293. doi: 10.1152/japplphysiol.00760.2017. Epub 2018 Jan 25.
8
Using accelerometry to classify physical activity intensity in older adults: What is the optimal wear-site?使用加速度计对老年人的体力活动强度进行分类：最佳佩戴部位在哪里？
Eur J Sport Sci. 2020 Sep;20(8):1131-1139. doi: 10.1080/17461391.2019.1694078. Epub 2019 Nov 26.
9
Physical activity among hospitalised older people: insights from upper and lower limb accelerometry.住院老年人的身体活动：来自上肢和下肢加速度计的见解。
Aging Clin Exp Res. 2018 Nov;30(11):1363-1369. doi: 10.1007/s40520-018-0930-0. Epub 2018 Mar 14.
10
Wrist-Worn Activity Trackers in Laboratory and Free-Living Settings for Patients With Chronic Pain: Criterion Validity Study.腕戴式活动追踪器在慢性疼痛患者的实验室和自由生活环境中的应用：效标效度研究。
JMIR Mhealth Uhealth. 2021 Jan 12;9(1):e24806. doi: 10.2196/24806.

本文引用的文献

1
Ambient intelligence for long-term diabetes care (AmILCare). Qualitative analysis of patients' expectations and attitudes toward interactive technology.用于长期糖尿病护理的环境智能（AmILCare）。对患者对交互式技术的期望和态度的定性分析。
Endocrine. 2021 Aug;73(2):472-475. doi: 10.1007/s12020-021-02694-1. Epub 2021 Mar 25.
2
Cut points of the Actigraph GT9X for moderate and vigorous intensity physical activity at four different wear locations.在四个不同佩戴位置，Actigraph GT9X 计测中高强度活动的切点。
J Sports Sci. 2020 Mar;38(5):503-510. doi: 10.1080/02640414.2019.1707956. Epub 2019 Dec 23.
3
Diabetes Digital App Technology: Benefits, Challenges, and Recommendations. A Consensus Report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group.糖尿病数字应用技术：效益、挑战和建议。欧洲糖尿病研究协会 (EASD) 和美国糖尿病协会 (ADA) 糖尿病技术工作组的共识报告。
Diabetes Care. 2020 Jan;43(1):250-260. doi: 10.2337/dci19-0062. Epub 2019 Dec 5.
4
Cross-sectional associations of active transport, employment status and objectively measured physical activity: analyses from the National Health and Nutrition Examination Survey.横断面研究中主动交通、就业状况与客观测量体力活动的关联：来自全国健康与营养调查的分析。
J Epidemiol Community Health. 2018 Sep;72(9):764-769. doi: 10.1136/jech-2017-210265. Epub 2018 May 5.
5
"What Is a Step?" Differences in How a Step Is Detected among Three Popular Activity Monitors That Have Impacted Physical Activity Research.“何为一步？”三种广受欢迎的活动监测器在检测步数方面的差异对体力活动研究产生影响。
Sensors (Basel). 2018 Apr 15;18(4):1206. doi: 10.3390/s18041206.
6
Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification.用于自由生活行为分类的髋部和腕部加速度计算法
Med Sci Sports Exerc. 2016 May;48(5):933-40. doi: 10.1249/MSS.0000000000000840.
7
A Survey on Ambient Intelligence in Health Care.医疗保健中的环境智能调查
Proc IEEE Inst Electr Electron Eng. 2013 Dec 1;101(12):2470-2494. doi: 10.1109/JPROC.2013.2262913.
8
Validation of accelerometer wear and nonwear time classification algorithm.计步器佩戴和不佩戴时间分类算法的验证。
Med Sci Sports Exerc. 2011 Feb;43(2):357-64. doi: 10.1249/MSS.0b013e3181ed61a3.
9
Calibration of the Computer Science and Applications, Inc. accelerometer.计算机科学与应用公司加速度计的校准。
Med Sci Sports Exerc. 1998 May;30(5):777-81. doi: 10.1097/00005768-199805000-00021.