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

2019 ◽  
Vol 33 (2) ◽  
Author(s):  
Kyeung Ho Kang ◽  
Si Ho Shin ◽  
Jaehyo Jung ◽  
Youn Tae Kim
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Artificial Neural Network ◽  
Energy Expenditure ◽  
Physical Activity Energy Expenditure ◽  
Patch Type ◽  
Sensor Module ◽  
Artificial Neural ◽  
Activity Energy

scholarly journals A recurrent neural network architecture to model physical activity energy expenditure in older people

2022 ◽  
Author(s):  
Stylianos Paraschiakos ◽  
Cláudio Rebelo de Sá ◽  
Jeremiah Okai ◽  
P. Eline Slagboom ◽  
Marian Beekman ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Health Care ◽  
Energy Expenditure ◽  
Older People ◽  
Recurrent Neural Network ◽  
Well Being ◽  
Elderly Subjects ◽  
Physical Activity Energy Expenditure ◽  
Activity Energy

AbstractThrough the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being.

An artificial neural network model of energy expenditure using nonintegrated acceleration signals

2007 ◽  
Vol 103 (4) ◽  
pp. 1419-1427 ◽  
Author(s):  
Megan P. Rothney ◽  
Megan Neumann ◽  
Ashley Béziat ◽  
Kong Y. Chen
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Artificial Neural Network ◽  
Energy Expenditure ◽  
Linear Regression Models ◽  
Ann Model ◽  
Free Living ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Acceleration Signals

Accelerometers are a promising tool for characterizing physical activity patterns in free living. The major limitation in their widespread use to date has been a lack of precision in estimating energy expenditure (EE), which may be attributed to the oversimplified time-integrated acceleration signals and subsequent use of linear regression models for EE estimation. In this study, we collected biaxial raw (32 Hz) acceleration signals at the hip to develop a relationship between acceleration and minute-to-minute EE in 102 healthy adults using EE data collected for nearly 24 h in a room calorimeter as the reference standard. From each 1 min of acceleration data, we extracted 10 signal characteristics (features) that we felt had the potential to characterize EE intensity. Using these data, we developed a feed-forward/back-propagation artificial neural network (ANN) model with one hidden layer (12 × 20 × 1 nodes). Results of the ANN were compared with estimations using the ActiGraph monitor, a uniaxial accelerometer, and the IDEEA monitor, an array of five accelerometers. After training and validation (leave-one-subject out) were completed, the ANN showed significantly reduced mean absolute errors (0.29 ± 0.10 kcal/min), mean squared errors (0.23 ± 0.14 kcal2/min2), and difference in total EE (21 ± 115 kcal/day), compared with both the IDEEA ( P < 0.01) and a regression model for the ActiGraph accelerometer ( P < 0.001). Thus ANN combined with raw acceleration signals is a promising approach to link body accelerations to EE. Further validation is needed to understand the performance of the model for different physical activity types under free-living conditions.

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

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

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.

Improved estimation of energy expenditure by artificial neural network modeling

2008 ◽  
Vol 33 (6) ◽  
pp. 1213-1222 ◽  
Author(s):  
Dean Charles Hay ◽  
Akinobu Wakayama ◽  
Ken Sakamura ◽  
Senshi Fukashiro
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Artificial Neural Network ◽  
Energy Expenditure ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Validation Data ◽  
Artificial Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

Estimation of energy expenditure in daily living conditions can be a tool for clinical assessment of health status, as well as a self-measure of lifestyle and general activity levels. Criterion measures are either prohibitively expensive or restricted to laboratory settings. Portable devices (heart rate monitors, pedometers) have gained recent popularity, but accuracy of the prediction equations remains questionable. This study applied an artificial neural network modeling approach to the problem of estimating energy expenditure with different dynamic inputs (accelerometry, heart rate above resting (HRar), and electromyography (EMG)). Nine feed-forward back-propagation models were trained, with the goal of minimizing the mean squared error (MSE) of the training datasets. Model 1 (accelerometry only) and model 2 (HRar only) performed poorly and had significantly greater MSE than all other models (p < 0.001). Model 3 (combined accelerometry and HRar) had overall performance similar to EMG models. Validation of all models was performed by simulating untrained datasets. MSE of all models increased when tested with validation data. While models 1 and 2 again performed poorly, model 3 MSE was lower than all but 2 EMG models. Squared correlation coefficients of measured and predicted energy expenditure for models 3 to 9 ranged from 0.745 to 0.817. Analysis of mean error within specific movement categories indicates that EMG models may be better at predicting higher-intensity energy expenditure, but combined accelerometry and HRar provides an economical solution, with sufficient accuracy.

An artificial neural network to predict resting energy expenditure in obesity

2018 ◽  
Vol 37 (5) ◽  
pp. 1661-1669 ◽  
Author(s):  
Emmanuel Disse ◽  
Séverine Ledoux ◽  
Cécile Bétry ◽  
Cyrielle Caussy ◽  
Christine Maitrepierre ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Expenditure ◽  
Resting Energy Expenditure ◽  
Artificial Neural ◽  
Resting Energy
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