Patch type sensor module for estimating the energy expenditure

Meina, Li; Byun, Kyeong Ho; Kim, Hyo Jung; Kang, Ji Hee; Kim, Youn Tae

doi:10.1109/icsens.2009.5398448

Cited by 4 publications

(5 citation statements)

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“…The measurement data are stored in each sensor module and are sent to the central computer through the Zigbee communication model. The commercial Zigbee communication model was used for data conversion 27–29 . The hardware (H/W) device attached to the human body measures signals generated by the HR in the physical body and the movements of humans (movement index).…”

Section: Methodsmentioning

confidence: 99%

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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Section: Methodsmentioning

confidence: 99%

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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“…The proposed system comprises a flexible printed circuit board that contains an analog, a digital electrical network, and sensors. As shown in Table 2, the device enables up to eight people within 400 m radius to perform multi-communication in real time [4,6]. The EE estimation model of a wireless sensor system designed to evaluate the EE is presented in Figure 2.…”

Section: Overview Of Wireless Sensor Systemmentioning

confidence: 99%

“…Many studies have been reported on systems that evaluate EE based on the amount of exercise [5]; these systems generally aim to enhance the health and strength of patients using wireless healthcare monitoring devices [6,7]. Furthermore, in the wake of ubiquitous healthcare systems [8], calorie trackers, heart rate (HR) monitors, and motion detectors have been used to measure EE on exercise indexes.…”

Section: Introductionmentioning

confidence: 99%

Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Kang

Shin

et al. 2021

Electronics

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Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

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“…Therefore we select iPod touch 4 as the device to develop heart rate extraction algorithm. The heart rate is linearly proportional to EE [3][4] [5] and this makes it a good indirect measurement of EE. However, everyone has his/her particular slope for the EE versus heart rate.…”

Section: A Core Application Flowmentioning

confidence: 99%

“…The weight history helps to achieve the first adaptation in our system. As for another adaptation, researches [3][5] point out that heart rate is easily affected by users' mental emotions, and a good solution is to take body motion into consideration.…”

Section: A Core Application Flowmentioning

confidence: 99%

Mobile energy expenditure tracking system based on heart rate and motion providing extra extensions for personalized care

Chen

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-In order to keep a healthy body it is very important to have balanced energy expenditure (EE). The proposed system utilized the phonocardiogram (PCG) and motion from the user to generate the heart rate. The heart rate has been deemed as a very reliable index to estimate the EE of human body. Efforts are put on developing our system to deal the situations when severe interference coupled into the system while the user is exercising. Accelerometer is used to get the motion status and also the posture of the user which is monitored to determine any anomaly. If anomaly is detected, the system actively issues notification to whom that might give back a timely aid.

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Patch type sensor module for estimating the energy expenditure

Cited by 4 publications

References 0 publications

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

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

Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Mobile energy expenditure tracking system based on heart rate and motion providing extra extensions for personalized care

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