Development of patch-type sensor module for wireless monitoring of heart rate and movement index

Meina, Li; Kim, Youn Tae

doi:10.1016/j.sna.2011.10.013

Cited by 22 publications

(24 citation statements)

References 16 publications

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“…The distances for transmission can achieve more than 400 m in an open field using the ZigBee telecommunication. The technical details of the sensor design can be found in a previously published study [26]. …”

Section: System Architecturementioning

confidence: 99%

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

Kwak

Kim

2016

Sensors

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Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.

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Section: System Architecturementioning

confidence: 99%

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

Kwak

Kim

2016

Sensors

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“…This module consists of a sensor board, rubber board, and communication module. Figure 1 shows the module equipped with sensor board and silicon packaging case during treadmill running in an indoor environment [2]. …”

Section: Wireless Monitoring System With a Patch-type Sensor Modulementioning

confidence: 99%

“…To enable wireless exercise management, the acquired sensor data should be passed to a central monitoring system. In this system, a commercial Zigbee telecommunication module is used for data transfer [2]. Figure 2 shows the patch-type sensor board with 3-axis accelerometer and ECG acquisition [2].…”

Section: Wireless Monitoring System With a Patch-type Sensor Modulementioning

confidence: 99%

“…Such a wireless system is used extensively, not only to estimate EE, but it is also useful for patient monitoring and athletic training. Previous commercial physiological sensors are relatively large and their power consumption is also considerable amount, so we recently developed a patch-type sensor module to solve these problems [2]. The leading characteristics of this sensor module are small size, wireless operation, low cost, light weight, and comfort for long-term wear, as well as integration of all required measurement parameters into a single sensor.…”

Section: Introductionmentioning

confidence: 99%

“…It has been demonstrated that the device used in this paper is suitable through reliability tests, performance evaluation of heart rate detection and movement indexes, as well as field tests using the Bruce protocol [2]. The intelligent predictor is designed by a Takagi-Sugeno-Kang-Linguistic Model (TSK-LM) with an interval prediction with the aid of fuzzy granulation realized via Context-based Fuzzy c-Means (CFCM) clustering [16].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Predictor of Energy Expenditure with the Use of Patch-Type Sensor Module

Meina

Kwak

Kim

2012

Sensors

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This paper is concerned with an intelligent predictor of energy expenditure (EE) using a developed patch-type sensor module for wireless monitoring of heart rate (HR) and movement index (MI). For this purpose, an intelligent predictor is designed by an advanced linguistic model (LM) with interval prediction based on fuzzy granulation that can be realized by context-based fuzzy c-means (CFCM) clustering. The system components consist of a sensor board, the rubber case, and the communication module with built-in analysis algorithm. This sensor is patched onto the user's chest to obtain physiological data in indoor and outdoor environments. The prediction performance was demonstrated by root mean square error (RMSE). The prediction performance was obtained as the number of contexts and clusters increased from 2 to 6, respectively. Thirty participants were recruited from Chosun University to take part in this study. The data sets were recorded during normal walking, brisk walking, slow running, and jogging in an outdoor environment and treadmill running in an indoor environment, respectively. We randomly divided the data set into training (60%) and test data set (40%) in the normalized space during 10 iterations. The training data set is used for model construction, while the test set is used for model validation. The experimental results revealed that the prediction error on treadmill running simulation was improved by about 51% and 12% in comparison to conventional LM for training and checking data set, respectively.

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Estimation of a physical activity energy expenditure with a patch‐type sensor module using artificial neural network

Kang

Shin

Jung

et al. 2019

Concurrency and Computation

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Chronic diseases such as coronary artery diseases and diabetes are caused by lack of physical activities and are leading causes of high death and morbidity rates. In particular, the imbalance of consumption energy and intake energy has increased adult diseases such as obesity with high mortality. Until recently, direct calorimetry by production calorie and indirect calorimetry by energy expenditure have been regarded as the best methods for estimating physical activity and energy expenditure. These calorimetry methods are associated with limited practicality such as data acquisition in a limited time, high cost, and wearing an inconvenient mask for oxygen uptake measurement. In this study, we propose the most accurate method using a wireless patch-type sensor to predict the energy expenditure of physical activities. Through the optimization of the prediction of energy expenditure of physical activities using the neural network algorithm, we achieved RMSE of 0.1893 and R 2 of 0.91 for the energy expenditures of aerobic and anaerobic exercises. These results indicate that the proposed system is useful and reliable for monitoring user's energy expenditure when using attached patch-type sensors workouts.

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Development of patch-type sensor module for wireless monitoring of heart rate and movement index

Cited by 22 publications

References 16 publications

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

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

Intelligent Predictor of Energy Expenditure with the Use of Patch-Type Sensor Module

Estimation of a physical activity energy expenditure with a patch‐type sensor module using artificial neural network

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