Expenditure Prediction Research Articles

Prediction Of Energy Expenditure Of Physical Activities During Balneotherapy Using Heart Rates

Prediction Of Energy Expenditure Of Physical Activities During Balneotherapy Using Heart Rates

Novel resting energy expenditure prediction equation for hospitalised older adults with polymorbity in Singapore

Novel resting energy expenditure prediction equation for hospitalised older adults with polymorbity in Singapore

Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors.

Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years.

Analysis of the structure and trend prediction of China's total health expenditure.

In the context of rapid economic and social development, there has been a continuous intensification of population aging, transformation of disease patterns, and wide application of new medical technologies. As a result, health expenditures in various countries have sharply soared. How to utilize limited medical resources to maximize the improvement of health levels has become a hot and challenging issue related to the well-being of all humanity. The relevant indicators of total health expenditure play a crucial role in monitoring and evaluating the fairness of health financing and health security in the region. This study explores the changes in the main expenses that constitute China's total health expenditure and uses indicators related to health expenditure to observe the changes and future development trends of China's health expenditure. Based on this, the utilization of China's health expenditure is monitored to identify possible problems, and thereby targeted suggestions for promoting the development of China's health and wellness cause are put forward. Based on the comparison of previous literature, this paper analyzes the changes and future development trends in China's health expenditure by using the relevant indicators of China's health expenditure through the structural variation analysis method and the gray prediction model. The results show that the scale of government, social, and out-of-pocket health expenditures has continuously expanded, with social health expenditures becoming the main funding source for total health expenditures. The burden of medical expenditures on individuals has been further reduced. In the institutional method of total health expenditures, hospital expenditures account for about 60% of the total and are the main component. The expenditures of health administration and medical insurance management institutions are the main driving force behind the growth of total health expenditures. However, the proportion of health expenditures in China's GDP is relatively low, so more investment is needed in the healthcare sector, and the burden of individual medical expenses also needs to be continuously reduced. In the future, China should further increase its investment in the medical and health sector. Specifically, the government should persist in investing in fundamental medical and health services. Simultaneously, efforts should be made to establish a scientific cost control mechanism for pharmaceuticals and broaden financing channels for healthcare, such as accelerating the development of commercial health insurance.

Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry

BackgroundAccurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling.MethodsWe first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure.ResultsThe activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively.ConclusionsThe integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

Energy expenditure prediction in preschool children: a machine learning approach using accelerometry and external validation

Objective.This study aimed to develop convolutional neural networks (CNNs) models to predict the energy expenditure (EE) of children from raw accelerometer data. Additionally, this study sought to external validation of the CNN models in addition to the linear regression (LM), random forest (RF), and full connected neural network (FcNN) models published in Steenbocket al(2019J. Meas. Phys. Behav.294-102).Approach.Included in this study were 41 German children (3.0-6.99 years) for the training and internal validation who were equipped with GENEActiv, GT3X+, and activPAL accelerometers. The external validation dataset consisted of 39 Canadian children (3.0-5.99 years) that were equipped with OPAL, GT9X, GENEActiv, and GT3X+ accelerometers. EE was recorded simultaneously in both datasets using a portable metabolic unit. The protocols consisted of a semi-structured activities ranging from low to high intensities. The root mean square error (RMSE) values were calculated and used to evaluate model performances.Main results.(1) The CNNs outperformed the LM (13.17%-23.81% lower mean RMSE values), FcNN (8.13%-27.27% lower RMSE values) and the RF models (3.59%-18.84% lower RMSE values) in the internal dataset. (2) In contrast, it was found that when applied to the external Canadian dataset, the CNN models had consistently higher RMSE values compared to the LM, FcNN, and RF.Significance.Although CNNs can enhance EE prediction accuracy, their ability to generalize to new datasets and accelerometer brands/models, is more limited compared to LM, RF, and FcNN models.

Validity of predictive equations for total energy expenditure against doubly labeled water

Variations in physical activity energy expenditure can make accurate prediction of total energy expenditure (TEE) challenging. The purpose of the present study was to determine the accuracy of available equations to predict TEE in individuals varying in physical activity (PA) levels. TEE was measured by DLW in 56 adults varying in PA levels which were monitored by accelerometry. Ten different models were used to predict TEE and their accuracy and precision were evaluated, considering the effect of sex and PA. The models generally underestimated the TEE in this population. An equation published by Plucker was the most accurate in predicting the TEE in our entire sample. The Pontzer and Vinken models were the most accurate for those with lower PA levels. Despite the levels of accuracy of some equations, there were sizable errors (low precision) at an individual level. Future studies are needed to develop and validate these equations.

Prediction of Resting Energy Expenditure in Individuals With Obstructive Sleep Apnea Using Machine Learning

Prediction of Resting Energy Expenditure in Individuals With Obstructive Sleep Apnea Using Machine Learning

Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks.

The accurate estimation of energy expenditure from simple objective accelerometry measurements provides a valuable method for investigating the effect of physical activity (PA) interventions or population surveillance. Methods have been evaluated previously, but none utilize the temporal aspects of the accelerometry data. In this study, we investigated the energy expenditure prediction from acceleration measured at the subjects' hip, wrist, thigh, and back using recurrent neural networks utilizing temporal elements of the data. The acceleration was measured in children (N = 33) performing a standardized activity protocol in their natural environment. The energy expenditure was modelled using Multiple Linear Regression (MLR), stacked long short-term memory (LSTM) networks, and combined convolutional neural networks (CNN) and LSTM. The correlation and mean absolute percentage error (MAPE) were 0.76 and 19.9% for the MLR, 0.882 and 0.879 and 14.22% for the LSTM, and, with the combined LSTM-CNN, the best performance of 0.883 and 13.9% was achieved. The prediction error for vigorous intensities was significantly different (p < 0.01) from those of the other intensity domains: sedentary, light, and moderate. Utilizing the temporal elements of movement significantly improves energy expenditure prediction accuracy compared to other conventional approaches, but the prediction error for vigorous intensities requires further investigation.

Assessment and Prediction of Energy Expenditure: One-Mile Walks and Runs Among African American

Among ethnicities, African Americans exhibit the highest prevalence of obesity. A more comprehensive grasp of energy expenditure while walking and running can be instrumental in managing and averting obesity. Regrettably, there is a scarcity of research dedicated to scrutinizing energy expenditure in African Americans during walking and running. Consequently, the primary objective of this study was to contrast energy expenditure disparities during a one-mile walk and run between African Americans of normal-weight and those classified as overweight. Additionally, the secondary aim was to formulate and validate a predictive equation for energy expenditure tailored specifically to African Americans. A total of 68 African American participants were involved in this study, comprising 21 individuals classified as normal-weight walkers, 27 as overweight walkers, and 20 as runners. Energy expenditure was assessed using indirect calorimetry. To compare energy expenditure across these groups, an Analysis of Variance (ANOVA) was conducted, followed by a post hoc Scheffe test. Linear regression analysis was employed for predicting energy expenditure. Additionally, a dependent t-test and a Chow test were utilized for cross-validating the predictive equation. The findings revealed that runners exhibited significantly higher energy expenditure compared to normal-weight walkers. When energy expenditure was normalized to body weight, runners expended significantly more energy than both normal-weight and overweight walkers. However, when expressed relative to fat-free mass, normal-weight walkers expended less energy than runners and overweight walkers. A prediction equation tailored specifically for African Americans was formulated as follows: EE = 1.012 BW − 9.233 Gender (M = 1, F = 2) + 47.188. The results from cross-validation tests confirmed the validity of this equation. Consequently, we recommend the use of this energy expenditure prediction equation for calculating energy expenditure during one-mile walks or runs for African American adults, whether they fall within the normal-weight or overweight categories.

PREDICTION ACCURACY OF NIGERIAN MILITARY EXPENDITURE: MLR, ARIMAX, AND ANN MODELS IN STATISTICAL AND MACHINE LEARNING FRAMEWORKS

Accurately predicting military expenditure is crucial for budgetary planning and national security. However, traditional forecasting methods often struggle to capture the complex dynamics of military spending. This study investigates the potential of statistical methods and machine learning to improve the accuracy of Nigerian military expenditure prediction. We compare the performance of three widely used models: multiple linear regression (MLR), autoregressive integrated moving average with exogenous variables (ARIMAX), and artificial neural networks (ANN). Using historical data on Nigerian military expenditure, GDP, and relevant economic indicators, we train and evaluate each model's prediction accuracy. We also employ statistical tests to assess the normality of residuals in the two distinct models of MLR and ARIMAX. Our findings indicate that the machine learning model, particularly ANN, significantly outperforms MLR in terms of prediction accuracy. ARIMAX shows promising results but lags behind ANN. We attribute the superior performance of the ANN to its ability to capture non-linear relationships and complex patterns in the data. This study adds to the body of knowledge by highlighting how machine learning methods can be used to increase the accuracy of military expenditure forecasts. Furthermore, the specific focus on Nigeria provides valuable insights into the unique dynamics of military spending in a developing country.

Resting energy expenditure prediction equations have low accuracy and high prediction error: From malnutrition to obesity

Resting energy expenditure prediction equations have low accuracy and high prediction error: From malnutrition to obesity

A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform

Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices.

New prediction equations for resting energy expenditure in older hospitalized patients: Development and validation

ObjectivesAccurate resting energy expenditure (REE) prediction is needed to prevent over- or underfeeding in older hospitalized patients. However, few validated REE prediction Equations are known for such patients. Therefore, this study aimed to develop new REE prediction Equations and evaluate their validity. MethodsThis single-center, cross-sectional study enrolled 134 patients ages ≥70 y. For holdout validation, patients were randomized in a 3:1 ratio; for the development data set, a new Equation was developed according to the measured REE using indirect calorimetry. The new and existing Equations were compared using the validation data set. ResultsMean patient age was 87.4 ± 6.9 y, and 34.3% were male. Two Equations were developed in multivariable regression models: Equation 1: REE (kcal/day) = 313.582 + Height (cm) × 3.973 + Body weight (kg) × 5.332 - Age (y) × 5.474 - (0 if male; 1 if female) × 20.012 + Calf circumference (cm) × 12.174; and Equation 2: REE (kcal/day) = 594.819 + Height (cm) × 3.760 + Body weight (kg) × 8.888 − Age (y) × 6.298 − (0 if male; 1 if female) × 16.396. The mean relative bias (95% CI) with measured REE as a reference had a small bias for Equations 1 and 2 (−0.1 [−4.1 to 3.9]% and −0.2 [−4.4 to 4.1]%, respectively); however, the Harris-Benedict, Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University, Ganpule, and body weight × 20 Equations had larger biases (−6.2 [−10.3 to −2.0]%; 5.3 [1.3 to 9.3]%; −13.9 [−18.6 to −9.3]%; and −11.6 [−16.1 to −7.1]%, respectively). ConclusionsNew prediction Equations using height, body weight, age, sex, and calf circumference improve REE prediction accuracy in older hospitalized patients.

Human locomotion over obstacles reveals real-time prediction of energy expenditure for optimized decision-making

Despite decades of evidence revealing a multitude of ways in which animals are adapted to minimize the energy cost of locomotion, little is known about how energy expenditure shapes adaptive gait over complex terrain. Here, we show that the principle of energy optimality in human locomotion can be generalized to complex task-level locomotor behaviours requiring advance decision-making and anticipatory control. Participants completed a forced-choice locomotor task requiring them to choose between discrete multi-step obstacle negotiation strategies to cross a ‘hole’ in the ground. By modelling and analysing mechanical energy cost of transport for preferred and non-preferred manoeuvres over a wide range of obstacle dimensions, we showed that strategy selection was predicted by relative energy cost integrated across the complete multi-step task. Vision-based remote sensing was sufficient to select the strategy associated with the lowest prospective energy cost in advance of obstacle encounter, demonstrating the capacity for energetic optimization of locomotor behaviour in the absence of online proprioceptive or chemosensory feedback mechanisms. We highlight the integrative hierarchic optimizations that are required to facilitate energetically efficient locomotion over complex terrain and propose a new behavioural level linking mechanics, remote sensing and cognition that can be leveraged to explore locomotor control and decision-making.

Prediction of university fund revenue and expenditure based on fuzzy time series with a periodic factor.

Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities.

Prediction of the Physical Activity Level of Community-Dwelling Older Japanese Adults with a Triaxial Accelerometer Containing a Classification Algorithm for Ambulatory and Non-Ambulatory Activities

Accurate methods for the prediction of the total energy expenditure and physical activity level (PAL) in community-dwelling older adults have not been established. Therefore, we examined the validity of estimating the PAL using an activity monitor (Active style Pro HJA-350IT, [ASP]) and proposed correction formulae for such populations in Japan. Data for 69 Japanese community-dwelling adults aged 65 to 85 years were used. The total energy expenditure in free-living conditions was measured with the doubly labeled water method and the measured basal metabolic rate. The PAL was also estimated from metabolic equivalent (MET) values obtained with the activity monitor. Adjusted MET values were also calculated with the regression equation of Nagayoshi et al. (2019). The observed PAL was underestimated, but significantly correlated, with the PAL from the ASP. When adjusted using the Nagayoshi et al. regression equation, the PAL was overestimated. Therefore, we developed regression equations to estimate the actual PAL (Y) from the PAL obtained with the ASP for young adults (X) as follows: women: Y = 0.949 × X + 0.205, mean ± standard deviation of the prediction error = 0.00 ± 0.20; men: Y = 0.899 × X + 0.371, mean ± standard deviation of the prediction error = 0.00 ± 0.17.

Grey and green infrastructure alternatives for water-related disaster mitigation: the case of Mandailing Natal, North Sumatera, Indonesia

Flood disaster in Mandailing Natal Regency, North Sumatera, Indonesia is challenging due to its barriers to reduce the disaster impact. Flood disaster mitigation should be prepared for both structural and non-structural alternatives to apply. Meanwhile, the government seems to face budget allocation and geographical condition of the area to choose the better alternatives. This research aims to investigate the suitable alternative to be applied in the study area as flood disaster mitigation. There are three alternatives: (1) river channel modification, (2) earth-embankment dam construction, or (3) rainwater harvesting (RWH) application. The investigation is carried out through hydrological analysis, hydraulics analysis, and numerical analysis of channel and dam by using HEC-RAS and Geo-Studio supporting tool. Meanwhile, rainwater harvesting is computed based on a mathematical model from previous studies. Since the alternative is chosen because of the runoff reduction capability and budget expenditure prediction, rainwater harvesting seems to be the more recommended to be applied in the study area. The RWH could make 86.36% runoff reduction. Despite river channel modification could extract almost 100% of runoff, the allocation for the construction is too high for the government to handle.

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer.

One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers. This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances. The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97-99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%. The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.

Using machine-learning algorithms to improve imputation in the medical expenditure panel survey.

To assess the feasibility of applying machine learning (ML) methods to imputation in the Medical Expenditure Panel Survey (MEPS). All data come from the 2016-2017 MEPS. Currently, expenditures for medical encounters in the MEPS are imputed with a predictive mean matching (PMM) algorithm in which a linear regression model is used to predict expenditures for events with (donors) and without (recipients) data. Recipient events and donor events are then matched based on the smallest distance between predicted expenditures, and the donor event's expenditures are used as the recipient event's imputation. We replace linear regression algorithm in the PMM framework with ML methods to predict expenditures. We examine five alternatives to linear regression: Gradient Boosting, Random Forests, Extreme Random Forests, Deep Neural Networks, and a Stacked Ensemble approach. Additionally, we introduce an alternative matching scheme, which matches on a vector of predicted expenditures by sources of payment instead of a single total expenditure prediction to generate potentially superior matches. Study data is derived from a large federal survey. ML algorithms perform better at both prediction and matching imputation than Ordinary Least Squares (OLS), the most common prediction algorithm used in PMM. On average, the Stacked Ensemble approach that combines all the ML algorithms performs best, improving expenditure prediction R2 by 108% (0.156 points) and final imputation R2 by 227% (0.397 points). Matching on a prediction vector also improves alignment of sources of payments between donor and recipient events. ML algorithms and an alternative matching scheme improve the overall quality of expenditure PMM imputation in the MEPS. These methods may have additional value in other national surveys that currently rely on PMM or similar methods for imputation.