Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network

Li, Meina; Kwak, Keun-Chang; Kim, Youn Tae

doi:10.3390/s16101566

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…It is difficult to clarify the reason for the overestimation, various factors including technical errors can be considered. Many algorithms using accelerometers and other devices such as combined monitors with an accelerometer, HR monitor, and patch-type sensor module have been developing rapidly [32][33][34][35]. However, those devices cannot measure specific activities like cycling, ascending stairs, and carrying packages.…”

Section: Discussionmentioning

confidence: 99%

Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals

Caballero

Ando

Nakae

et al. 2019

IJERPH

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Background: Heart rate (HR) during physical activity is strongly affected by the level of physical fitness. Therefore, to assess the effects of fitness, we developed predictive equations to estimate the metabolic equivalent (MET) of daily activities, which includes low intensity activities, by % HR reserve (%HRR), resting HR, and multiple physical characteristics. Methods: Forty volunteers between the ages of 21 and 55 performed 20 types of daily activities while recording HR and sampling expired gas to evaluate METs values. Multiple regression analysis was performed to develop prediction models of METs with seven potential predictors, such as %HRR, resting HR, and sex. The contributing parameters were selected based on the brute force method. Additionally, leave-one-out method was performed to validate the prediction models. Results: %HRR, resting HR, sex, and height were selected as the independent variables. %HRR showed the highest contribution in the model, while the other variables exhibited small variances. METs were estimated within a 17.3% difference for each activity, with large differences in document arrangement while sitting (+17%), ascending stairs (−8%), and descending stairs (+8%). Conclusions: The results showed that %HRR is a strong predictor for estimating the METs of daily activities. Resting HR and other variables were mild contributors. (201 words)

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Section: Discussionmentioning

confidence: 99%

Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals

Caballero

Ando

Nakae

et al. 2019

IJERPH

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“…2(b) shows a patch-type and a watch-type receiver. The patch-type module was modeled as a module for measuring the energy expenditure in the chest [30]. Fig.…”

Section: Measurement Setupmentioning

confidence: 99%

Analysis on frequency-dependency of conductive signal transmission channel for wearable devices

Park

Jeong

Baek

et al. 2019

IEICE Electron. Express

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Human body communication method technology can transmit the data signal through human body that is suitable for healthcare and mobile devices. This technology uses the human body channel and therefore is greatly influenced by human body characteristics. Impulse response measurements were performed on 120 adult males to analyze channel characteristics of human body. The average signal loss was −67 to −65 dB depending on the transmission direction. In addition, the correlation between human body channel characteristics and body weight, skeletal muscle mass, body fat mass, total body water and transmission and reception direction signal loss were obtained. Through analysis of channel characteristics of human body medium, channel characteristics suitable for human body communication device are presented.

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“…Duclos et al have used accelerometer data to measure energy expenditure (Duclos, 2016). Li et al used the ECG and accelerometer sensors to estimate energy expenditure (Li, 2016). Altini et al used five accelerometer sensors attached to the body to assess the energy expenditure during the daily activities (Altini, 2014).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Motion Activity Recognition Using Smartphone Sensors

Gildeh¹,

Fotouhi²,

Fotouhi³

et al. 2020

Proceedings of the 12th International Conference on E-Health (EH2020)

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MHealth systems establish a new way to transfer the health service to remote places. These systems offer significant benefits for continuous health monitoring. Motion activity recognition is one of the challenging mHealth use cases that incorporates continuous data collection and analysis of measurements. The main goal of this research is to analyze physical activity data. We employ measurements from the WISDM lab dataset 1 . These data are collected from participants performing motion activities. This data is then used by deep learning algorithms to predict special activities. In particular, CNN and CNN-LSTM algorithms are used to compare their accuracy, which resulted in approximately 95% and 97% respectively. Thus, the CNN-LSTM has higher accuracy in this analysis.

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Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network

Cited by 9 publications

References 25 publications

Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals

Simple Prediction of Metabolic Equivalents of Daily Activities Using Heart Rate Monitor without Calibration of Individuals

Analysis on frequency-dependency of conductive signal transmission channel for wearable devices

Deep Learning-Based Motion Activity Recognition Using Smartphone Sensors

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