• 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

用于测量卡路里消耗的可穿戴无线传感器。

Wearable Wireless Sensors for Measuring Calorie Consumption.

作者信息

Fotouhi-Ghazvini Faranak, Abbaspour Saedeh

机构信息

Department of Computer Engineering and IT, Faculty of Engineering, University of Qom, Iran.

出版信息

J Med Signals Sens. 2020 Feb 6;10(1):19-34. doi: 10.4103/jmss.JMSS_15_18. eCollection 2020 Jan-Mar.

DOI:10.4103/jmss.JMSS_15_18
PMID:32166074
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7038742/
Abstract

BACKGROUND

The tracking devices could help measuring the heart rate and energy expenditure and recognizing the user's activity. The calorie measurement is a significant achievement for the fitness tracking and the continuous health monitoring.

METHODS

In this paper, a combination of an accelerometer and a photoplethysmography (PPG) sensor is implemented to calculate the calories consumed. These sensors were mounted next to each other and then were placed on the ankle and finger by flat cable. The sensed data are transferred via Bluetooth to a smartphone in a serial and real-time manner. An Android App is designed to display the user's health data. The average amount of consumed energy is obtained from the combination of the accelerometer sensor based on the laws of motion and the PPG sensor based on the heart rate data.

RESULTS

The designed system is tested on 10 nonathlete males and 10 nonathlete females randomly. By applying the wavelet, the value of the acceleration signal variance was reduced from 3.2 to 0.8. The correlation between PPG and pulse oximeter was 0.9. Moreover, the correlation of the accelerometer and treadmill was 0.9. The root mean square error (RMSE) and the value of the calorie output from PPG and pulse oximeter are 0.53 and 0.008, respectively. The RMSE and the value of the calories output from the accelerometer and the treadmill are 0.42 and 0.007, respectively.

CONCLUSION

Our device validity and reliability were good by comparing it with a typical smart band, smart watch, and smartphone available in the market. The combined PPG and the accelerometer sensors were compared with the gold standard, the pulse oximeter, and the treadmill. According to the results, there is no significant difference in the values obtained. Therefore, a mobile system is augmented with the wireless accelerometer and PPG that are connected to a smartphone. The system could be carried out with the user at any time and any place.

摘要

背景

追踪设备有助于测量心率和能量消耗，并识别用户的活动。卡路里测量是健身追踪和持续健康监测的一项重大成果。

方法

本文采用加速度计和光电容积脉搏波描记法（PPG）传感器相结合的方式来计算消耗的卡路里。这些传感器彼此相邻安装，然后通过扁平电缆放置在脚踝和手指上。传感数据通过蓝牙以串行和实时方式传输到智能手机。设计了一个安卓应用程序来显示用户的健康数据。消耗能量的平均量是根据运动定律由加速度计传感器和基于心率数据的PPG传感器相结合得出的。

结果

所设计的系统在10名非运动员男性和10名非运动员女性中进行了随机测试。通过应用小波变换，加速度信号方差的值从3.2降至0.8。PPG与脉搏血氧仪之间的相关性为0.9。此外，加速度计与跑步机之间的相关性为0.9。PPG和脉搏血氧仪的卡路里输出的均方根误差（RMSE）和 值分别为0.53和0.008。加速度计和跑步机的卡路里输出的RMSE和 值分别为0.42和0.007。

结论

与市场上典型的智能手环、智能手表和智能手机相比，我们的设备有效性和可靠性良好。将PPG和加速度计传感器相结合与金标准、脉搏血氧仪和跑步机进行了比较。根据结果，所获得的值没有显著差异。因此，一个移动系统通过连接到智能手机的无线加速度计和PPG得到了增强。该系统可以在任何时间、任何地点与用户一起使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/12910f727eda/JMSS-10-19-g029.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/392423c6fdbd/JMSS-10-19-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/f5aac1756173/JMSS-10-19-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/40413bcb1a8f/JMSS-10-19-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/0ab90add6aac/JMSS-10-19-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/876ed21b1f30/JMSS-10-19-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/8175f7cacd5e/JMSS-10-19-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/edd764631e24/JMSS-10-19-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/e6d98f0dd488/JMSS-10-19-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/dcb261a00e59/JMSS-10-19-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/57e7234a45a0/JMSS-10-19-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/1786dbb14670/JMSS-10-19-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/00710642667f/JMSS-10-19-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/7f45cf7ba1c0/JMSS-10-19-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/3f89546ebfd1/JMSS-10-19-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/cc05a5d28270/JMSS-10-19-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/811506736535/JMSS-10-19-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/287f348b9fdc/JMSS-10-19-g024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/25191ac8cc33/JMSS-10-19-g025.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/3edb825f91ab/JMSS-10-19-g026.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/02c72e26a071/JMSS-10-19-g027.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/bb3cd61c165e/JMSS-10-19-g028.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/12910f727eda/JMSS-10-19-g029.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/392423c6fdbd/JMSS-10-19-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/f5aac1756173/JMSS-10-19-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/40413bcb1a8f/JMSS-10-19-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/0ab90add6aac/JMSS-10-19-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/876ed21b1f30/JMSS-10-19-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/8175f7cacd5e/JMSS-10-19-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/edd764631e24/JMSS-10-19-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/e6d98f0dd488/JMSS-10-19-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/dcb261a00e59/JMSS-10-19-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/57e7234a45a0/JMSS-10-19-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/1786dbb14670/JMSS-10-19-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/00710642667f/JMSS-10-19-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/7f45cf7ba1c0/JMSS-10-19-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/3f89546ebfd1/JMSS-10-19-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/cc05a5d28270/JMSS-10-19-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/811506736535/JMSS-10-19-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/287f348b9fdc/JMSS-10-19-g024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/25191ac8cc33/JMSS-10-19-g025.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/3edb825f91ab/JMSS-10-19-g026.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/02c72e26a071/JMSS-10-19-g027.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/bb3cd61c165e/JMSS-10-19-g028.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc74/7038742/12910f727eda/JMSS-10-19-g029.jpg

相似文献

1
Wearable Wireless Sensors for Measuring Calorie Consumption.用于测量卡路里消耗的可穿戴无线传感器。
J Med Signals Sens. 2020 Feb 6;10(1):19-34. doi: 10.4103/jmss.JMSS_15_18. eCollection 2020 Jan-Mar.
2
Rapid Prototyping of a Smart Device-based Wireless Reflectance Photoplethysmograph.基于智能设备的无线反射式光电容积脉搏波描记仪的快速原型制作
Proc South Biomed Eng Conf. 2016 Mar;2016:175-176. doi: 10.1109/SBEC.2016.15. Epub 2016 Apr 28.
3
A wireless sensor network compatible wearable u-healthcare monitoring system using integrated ECG, accelerometer and SpO2.一种使用集成心电图、加速度计和血氧饱和度监测仪的兼容无线传感器网络的可穿戴式个人健康监测系统。
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1529-32. doi: 10.1109/IEMBS.2008.4649460.
4
A wearable signal acquisition system for physiological signs including throat PPG.
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:603-606. doi: 10.1109/EMBC.2016.7590774.
5
Resting and Postexercise Heart Rate Detection From Fingertip and Facial Photoplethysmography Using a Smartphone Camera: A Validation Study.使用智能手机摄像头通过指尖和面部光电容积脉搏波描记法检测静息和运动后心率：一项验证研究。
JMIR Mhealth Uhealth. 2017 Mar 13;5(3):e33. doi: 10.2196/mhealth.7275.
6
Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes.基于可穿戴表面肌电和加速度计无线传感器节点的人体活动监测系统。
Biomed Eng Online. 2018 Nov 20;17(Suppl 1):132. doi: 10.1186/s12938-018-0567-4.
7
Reference signal less Fourier analysis based motion artifact removal algorithm for wearable photoplethysmography devices to estimate heart rate during physical exercises.基于无参考信号傅里叶分析的运动伪影去除算法，用于可穿戴式光电容积脉搏波描记术设备在体育锻炼期间估计心率。
Comput Biol Med. 2022 Feb;141:105081. doi: 10.1016/j.compbiomed.2021.105081. Epub 2021 Dec 5.
8
A Smartphone-Only Pulse Transit Time Monitor Based on Cardio-Mechanical and Photoplethysmography Modalities.基于心机械和光容积脉搏波描记法模态的仅智能手机脉搏传输时间监测仪。
IEEE Trans Biomed Circuits Syst. 2019 Dec;13(6):1462-1470. doi: 10.1109/TBCAS.2019.2936414. Epub 2019 Aug 21.
9
Heart Rate Estimation using PPG signal during Treadmill Exercise.在跑步机运动期间使用光电容积脉搏波信号进行心率估计。
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:3253-3256. doi: 10.1109/EMBC.2019.8857633.
10
Comparison of Three Prototypes of PPG Sensors for Continual Real-Time Measurement in Weak Magnetic Field.三种 PPG 传感器原型在弱磁场中连续实时测量的比较。
Sensors (Basel). 2022 May 16;22(10):3769. doi: 10.3390/s22103769.

引用本文的文献

1
Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.智能互联医疗中的趋势、技术及关键挑战
IEEE Access. 2021 May 11;9:74044-74067. doi: 10.1109/ACCESS.2021.3079217. eCollection 2021.
2
Wearables for Industrial Work Safety: A Survey.可穿戴设备在工业安全中的应用：综述
Sensors (Basel). 2021 Jun 2;21(11):3844. doi: 10.3390/s21113844.

本文引用的文献

1
A SURVEY OF SMARTWATCHES IN REMOTE HEALTH MONITORING.远程健康监测中的智能手表调查
J Healthc Inform Res. 2018 Jun;2(1-2):1-24. doi: 10.1007/s41666-017-0012-7. Epub 2017 Dec 18.
2
Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies.评估和增强低成本活动和位置传感器在暴露研究中的效用。
Environ Monit Assess. 2018 Feb 20;190(3):155. doi: 10.1007/s10661-018-6537-2.
3
Physical activity among children: objective measurements using Fitbit One and ActiGraph.儿童的身体活动：使用Fitbit One和ActiGraph进行客观测量
BMC Res Notes. 2017 Apr 20;10(1):161. doi: 10.1186/s13104-017-2476-1.
4
Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network.使用带有增量径向基函数神经网络的贴片式传感器模块估算能量消耗
Sensors (Basel). 2016 Sep 22;16(10):1566. doi: 10.3390/s16101566.
5
Validation of the Fitbit Zip for monitoring physical activity among free-living adolescents.验证Fitbit Zip在监测自由生活青少年身体活动方面的有效性。
BMC Res Notes. 2016 Sep 21;9(1):448. doi: 10.1186/s13104-016-2253-6.
6
Using Smartphone Sensors for Improving Energy Expenditure Estimation.利用智能手机传感器提高能量消耗估算。
IEEE J Transl Eng Health Med. 2015 Sep 18;3:2700212. doi: 10.1109/JTEHM.2015.2480082. eCollection 2015.
7
Sensing Home: A Cost-Effective Design for Smart Home via Heterogeneous Wireless Networks.感知家庭：一种通过异构无线网络实现智能家居的经济高效设计。
Sensors (Basel). 2015 Dec 3;15(12):30270-92. doi: 10.3390/s151229797.
8
Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning.使用穿戴式加速度计估算能量消耗：方法、传感器数量和位置的比较
IEEE J Biomed Health Inform. 2015 Jan;19(1):219-26. doi: 10.1109/JBHI.2014.2313039. Epub 2014 Mar 20.
9
Prediction of energy expenditure from heart rate monitoring during submaximal exercise.次最大运动期间通过心率监测预测能量消耗
J Sports Sci. 2005 Mar;23(3):289-97. doi: 10.1080/02640410470001730089.
10
Target heart rates for the development of cardiorespiratory fitness.发展心肺适能的目标心率。
Med Sci Sports Exerc. 1994 Jan;26(1):112-6.