scholarly journals Rapid energy expenditure estimation for assisted and inclined loaded walking

2018 ◽  
Author(s):  
Patrick Slade ◽  
Rachel Troutman ◽  
Mykel J. Kochenderfer ◽  
Steven H. Collins ◽  
Scott L. Delp
Keyword(s):  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Wearable Sensors ◽  
Use Cases ◽  
Training Data ◽  
Data Driven ◽  
Average Percentage ◽  
First Case ◽  
Ankle Exoskeleton ◽  
Loaded Walking

AbstractBackgroundEstimating energy expenditure with indirect calorimetry requires expensive equipment and provides slow and noisy measurements. Rapid estimates using wearable sensors would enable techniques like optimizing assistive devices outside a lab. Existing methods correlate data from wearable sensors to measured energy expenditure without evaluating the accuracy of the estimated energy expenditure for activity conditions or subjects not included in the correlation process. Our goal is to assess data-driven models that are capable of rapidly estimating energy expenditure for new conditions and subjects.MethodsWe developed models that estimated energy expenditure from two datasets during walking conditions with (1) ankle exoskeleton assistance and (2) various loads and inclines. The estimation was portable and rapid, using input features that are possible to measure with wearable sensors and restricting the input data length to a single gait cycle or four second interval. The performance of the models was evaluated for three use cases. The first case estimated energy expenditure during walking conditions for subjects with some subject specific training data available. The second case estimated all conditions in the dataset for a new subject not included in the training data. The third case estimated new conditions for a new subject. The models also ordered the magnitude of energy expenditure across all conditions for a new subject.ResultsThe average errors in energy expenditure estimation during assisted walking conditions were 4.4%, 8.0%, and 8.1% for the three use cases, respectively. The average errors in energy expenditure estimation during inclined and loaded walking conditions were 6.1%, 9.7%, and 11.7% for the three use cases. The models ordered the magnitude of energy expenditure with a maximum and average percentage of correctly ordered conditions of 56% and 43% for assisted walking and 85% and 55% for incline and loaded walking.ConclusionsOur data-driven models determined the accuracy of energy expenditure estimation for three use cases. For experiments where the accuracy of a data-driven model is sufficient, standard indirect calorimetry can be replaced. The energy expenditure ordering could aid in selecting optimal assistance conditions. The models, code, and datasets are provided for reproduction and extension of our results.

Estimating Energy Expenditure Using Wearable Sensors During Locomotion

2021 ◽  
Author(s):  
Mohammad Mohammad ◽  
Megan McAllister ◽  
Jessica Selinger
Keyword(s):  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Gold Standard ◽  
Low Cost ◽  
Wearable Sensors ◽  
Metabolic Energy ◽  
Valuable Insight ◽  
Metabolic Energy Expenditure ◽  
And Control ◽  
External Loads

Introduction. Measures of metabolic energy expenditure can provide valuable insight into healthy and impaired gait, the design and control of assistive devices, and rehabilitation progress. The gold standard for estimating energy expenditure during locomotion is indirect calorimetry, where oxygen use is captured at the mouth. Although accurate, indirect calorimetry systems are expensive, cumbersome, and often limited to lab settings. Objective. The aim of our research is to develop a lightweight, portable, and low-cost method for accurately estimating energy expenditure using wearable sensors. Our method must meet the following design criteria: i. estimate walking and running energy expenditure within 5% error of gold standard measures, ii. maintain accuracy given changes to terrain and external loads, iii. provide a continuous estimate with estimate intervals a maximum of one minute apart, and iv. cost under $1000. Methods. In pilot testing, we instrumented two participants (male, 21-22 years, 84-90 kg, 1.88-1.90m) with indirect calorimetry to measure gold standard energy expenditure, as well as the following wearable sensors: an accelerometer at the pelvis and foot, a heart rate monitor, and a respiratory belt. The participants walked and ran on a predefined outdoor route on Queen’s campus, including sections with distinct average inclines (0% and 5%). Participants also wore ankle weights (3% body weight) for particular sections of the route. We will use a multiple regression analysis, with cross-validation design, to predict energy expenditure using custom metrics derived from the wearable sensors.  

scholarly journals A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 107
Author(s):  
Elahe Jamalinia ◽  
Faraz S. Tehrani ◽  
Susan C. Steele-Dunne ◽  
Philip J. Vardon
Keyword(s):  
Numerical Simulation ◽  
Water Flux ◽  
Temporal Stability ◽  
Synthetic Data ◽  
Climatic Conditions ◽  
Training Data ◽  
Data Driven ◽  
Data Set ◽  
Surface Cracking ◽  
Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

scholarly journals The Diagnostic-Measurement Method—Resting Energy Expenditure Assessment of Polish Children Practicing Football

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Edyta Łuszczki ◽  
Anna Bartosiewicz ◽  
Katarzyna Dereń ◽  
Maciej Kuchciak ◽  
Łukasz Oleksy ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Composition ◽  
Energy Expenditure ◽  
Muscle Mass ◽  
Indirect Calorimetry ◽  
Energy Demand ◽  
Body Height ◽  
Resting Energy Expenditure ◽  
Blood Pressure Monitor ◽  
Resting Energy

Establishing the amount of energy needed to cover the energy demand of children doing sport training and thus ensuring they achieve an even energy balance requires the resting energy expenditure (REE) to be estimated. One of the methods that measures REE is the indirect calorimetry method, which may be influenced by many factors, including body composition, gender, age, height or blood pressure. The aim of the study was to assess the correlation between the resting energy expenditure of children regularly playing football and selected factors that influence the REE in this group. The study was conducted among 219 children aged 9 to 17 using a calorimeter, a device used to assess body composition by the electrical bioimpedance method by means of segment analyzer and a blood pressure monitor. The results of REE obtained by indirect calorimetry were compared with the results calculated using the ready-to-use formula, the Harris Benedict formula. The results showed a significant correlation of girls’ resting energy expenditure with muscle mass and body height, while boys’ resting energy expenditure was correlated with muscle mass and body water content. The value of the REE was significantly higher (p ≤ 0.001) than the value of the basal metabolic rate calculated by means of Harris Benedict formula. The obtained results can be a worthwhile suggestion for specialists dealing with energy demand planning in children, especially among those who are physically active to achieve optimal sporting successes ensuring proper functioning of their body.

scholarly journals Data-driven deep density estimation

2021 ◽  
Author(s):  
Patrik Puchert ◽  
Pedro Hermosilla ◽  
Tobias Ritschel ◽  
Timo Ropinski
Keyword(s):  
Data Analysis ◽  
Density Estimation ◽  
Population Data ◽  
Training Data ◽  
Data Driven ◽  
Discrete Observations ◽  
Efficient Manner ◽  
Continuous Models ◽  
3D Scans ◽  
Spatial Locations

AbstractDensity estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.

scholarly journals Effect of raw and encapsulated policosanol on lipid profiles, blood biochemistry, activity, energy expenditure and macronutrient metabolism of adult cats

2021 ◽  
pp. 1098612X2110137
Author(s):  
James R Templeman ◽  
Kylie Hogan ◽  
Alexandra Blanchard ◽  
Christopher PF Marinangeli ◽  
Alexandra Camara ◽  
...  
Keyword(s):  
Body Weight ◽  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Respiratory Quotient ◽  
Blood Count ◽  
Complete Blood Count ◽  
Serum Biochemistry ◽  
Blood Collection ◽  
Adult Cats ◽  
Activity Energy

Objectives The objective of this study was to verify the safety of policosanol supplementation for domestic cats. The effects of raw and encapsulated policosanol were compared with positive (L-carnitine) and negative (no supplementation) controls on outcomes of complete blood count, serum biochemistry, energy expenditure, respiratory quotient and physical activity in healthy young adult cats. Methods The study was a replicated 4 × 4 complete Latin square design. Eight cats (four castrated males, four spayed females; mean age 3.0 ± 1.0 years; mean weight 4.36 ± 1.08 kg; mean body condition score 5.4 ± 1.4) were blocked by sex and body weight then randomized to treatment groups: raw policosanol (10 mg/kg body weight), encapsulated policosanol (50 mg/kg body weight), L-carnitine (200 mg/kg body weight) or no supplementation. Treatments were supplemented to a basal diet for 28 days with a 1-week washout between periods. Food was distributed equally between two offerings to ensure complete supplement consumption (first offering) and measure consumption time (second offering). Blood collection (lipid profile, complete blood count, serum biochemistry) and indirect calorimetry (energy expenditure, respiratory quotient) were conducted at days 0, 14 and 28 of each period. Activity monitors were worn 7 days prior to indirect calorimetry and blood collection. Data were analyzed using a repeated measures mixed model (SAS, v.9.4). Results Food intake and body weight were similar among treatments. There was no effect of treatment on lipid profile, serum biochemistry, activity, energy expenditure or respiratory quotient ( P >0.05); however, time to consume a second meal was greatest in cats fed raw policosanol ( P <0.05). Conclusions and relevance These data suggest that policosanol is safe for feline consumption. Further studies with cats demonstrating cardiometabolic risk factors are warranted to confirm whether policosanol therapy is an efficacious treatment for hyperlipidemia and obesity.

Energy Expenditure in Playground Games in Primary School Children Measured by Accelerometer and Heart Rate Monitors

2017 ◽  
Vol 27 (5) ◽  
pp. 467-474 ◽  
Author(s):  
Jorge Cañete García-Prieto ◽  
Vicente Martinez-Vizcaino ◽  
Antonio García-Hermoso ◽  
Mairena Sánchez-López ◽  
Natalia Arias-Palencia ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Energy Expenditure ◽  
Primary School ◽  
Correlation Coefficient ◽  
School Children ◽  
Indirect Calorimetry ◽  
Coefficient Of Variation ◽  
Primary School Children ◽  
Heart Rate Monitors

The aim of this study was to examine the energy expenditure (EE) measured using indirect calorimetry (IC) during playground games and to assess the validity of heart rate (HR) and accelerometry counts as indirect indicators of EE in children´s physical activity games. 32 primary school children (9.9 ± 0.6 years old, 19.8 ± 4.9 kg · m-2 BMI and 37.6 ± 7.2 ml · kg-1 · min-1 VO2max). Indirect calorimetry (IC), accelerometry and HR data were simultaneously collected for each child during a 90 min session of 30 playground games. Thirty-eight sessions were recorded in 32 different children. Each game was recorded at least in three occasions in other three children. The intersubject coefficient of variation within a game was 27% for IC, 37% for accelerometry and 13% for HR. The overall mean EE in the games was 4.2 ± 1.4 kcals · min-1 per game, totaling to 375 ± 122 kcals/per 90 min/session. The correlation coefficient between indirect calorimetry and accelerometer counts was 0.48 (p = .026) for endurance games and 0.21 (p = .574) for strength games. The correlation coefficient between indirect calorimetry and HR was 0.71 (p = .032) for endurance games and 0.48 (p = .026) for strength games. Our data indicate that both accelerometer and HR monitors are useful devices for estimating EE during endurance games, but only HR monitors estimates are accurate for endurance games.

A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

2013 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Pingfeng Wang ◽  
Chao Hu
Keyword(s):  
Sampling Method ◽  
Failure Time ◽  
Health Management ◽  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Statistical Relationship ◽  
Cooling Fans ◽  
On Line

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

scholarly journals 87: Resting Energy Expenditure Measured by Indirect Calorimetry for Optimizing Nutrient Balance in Postsurgical Infants

2015 ◽  
Vol 20 (5) ◽  
pp. e65-e66
Author(s):  
SG Albersheim ◽  
NN Rao ◽  
TJ Risbud ◽  
B McRae ◽  
H Osiovich ◽  
...  
Keyword(s):  
Energy Expenditure ◽  
Indirect Calorimetry ◽  
Nutrient Balance ◽  
Resting Energy Expenditure ◽  
Resting Energy
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