Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate

Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise

doi:10.1016/j.apergo.2015.03.001

Cited by 17 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method combining HR, ACC and respiratory signals using a neural network to estimate the VO 2 during occupational activities was investigated in another study . Kolus et al (2015) presented a machine learning model using personal demographic variables and resting HR to predict HR-Flex parameters and VO 2 without individual calibration. Moreover, Altini, Casale et al (2015) described a Bayesian model using HR and ACC calibrated during daily activities for EE estimation, which avoided the need for laboratory protocol calibration.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Yang

Forsman

et al. 2019

Ergonomics

View full text Add to dashboard Cite

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO 2 ), which is commonly assessed by heart rate (HR) in field studies. However, the VO 2 -HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hipworn accelerometers (ACC); and HR þ arm-leg ACC model, combining HR with wrist-and thighworn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR þ arm-leg ACC model had the overall best performance with limits of agreement (LoA) of À3.94 and 2.00 mL/min/kg, while the HR-Flex model had À5.01 and 5.36 mL/min/kg and the branched model, À6.71 and 1.52 mL/min/kg. In conclusion, the HR þ arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation.Practitioner Summary: Work with high energy demand can impair employees' health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist-and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.Abbreviations: ACC: accelerometer; EE: energy expenditure; HR: heart rate; LoA: limits of agreement; RAS: relative aerobic strain; REE: resting energy expenditure; RHR: resting heart rate; VO2: oxygen consumption; WM: work metabolism ARTICLE HISTORY

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Yang

Forsman

et al. 2019

Ergonomics

View full text Add to dashboard Cite

show abstract

“…Additionally, a 10 fold cross-validation was also used to internally validate the performance of these models 22 . The validation method randomly divided the original dataset into10 equal sized subsets, and the model is repeatedly trained and validated 10 times.…”

Section: Methodsmentioning

confidence: 99%

Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model

Wang

Shen

Geng

et al. 2018

Sci Rep

View full text Add to dashboard Cite

Using random survival forest, this study was intended to evaluate the prognostic value of serum markers for lung adenocarcinoma patients with brain metastasis (BM), and tried to integrate them into a prognostic model. During 2010 to 2015, the patients were retrieved from two medical centers. Besides the Cox proportional hazards regression, the random survival forest (RSF) were also used to develop prognostic model from the group A (n = 142). In RSF of the group A, the factors, whose minimal depth were greater than the depth threshold or had a negative variable importance (VIMP), were firstly excluded. Subsequently, C-index and Akaike information criterion (AIC) were used to guide us finding models with higher prognostic ability and lower overfitting possibility. These RSF models, together with the Cox, modified-RPA and lung-GPA index were validated and compared, especially in the group B (CAMS, n = 53). Our data indicated that the KSE125 model (KPS, smoking, EGFR-20 (exon 18, 19 and 21) and Ca125) was the best in survival prediction, and performed well in internal and external validation. In conclusions, for lung adenocarcinoma patients with brain metastasis, a validated prognostic nomogram (KPS, smoking, EGFR-20 and Ca125) can more accurately predict 1-year and 2-year survival of the patients.

show abstract

“…Additionally, using the bootstrap method, a 10 fold cross-validation was used to test the generalization ability of the model 12 . The patients (group A and B) were equally and randomly divided into10 subsets.…”

Section: Methodsmentioning

confidence: 99%

Hemoglobin level, a prognostic factor for nasal extranodal natural killer/T-cell lymphoma patients from stage I to IV: A validated prognostic nomogram

Cao

Lan

Shen

et al. 2017

Sci Rep

View full text Add to dashboard Cite

Although nasal extranodal natural killer/T-cell lymphoma (nasal ENKL) shares some prognostic factors with other lymphomas, seldom studies had explored the prognostic value of hemoglobin. The ENKL cases in stage I–IV during 2000 to 2015 were collected from two medical centers (group A, n = 192), and were randomly divided into the group B (n = 155) and C (n = 37). Although the significant factors identified by the univariate analysis differed between the group A and B, the multivariate Cox regression indicated the same factors. C-index of the model was slightly better than Yang’s, but its integrated Brier score (IBS) was obviously lower than Yang’s both in the group A and B. Additionally, minimal depth of random survival forest (RSF) classifier confirmed that the prognostic ability of hemoglobin was better than age both in the group A and B. In the calibration of the nomogram, the predicted 3-year or 5-year OS of our nomogram well agreed with the corresponding actual OS. In conclusion, Hemoglobin is a prognostic factor for nasal ENKL patients in stage I - IV, and integrating it into a validated prognostic nomogram, whose generalization error is the smallest among the evaluated models, can be used to predict the patients’ outcome.

show abstract

Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate

Cited by 17 publications

References 36 publications

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model

Hemoglobin level, a prognostic factor for nasal extranodal natural killer/T-cell lymphoma patients from stage I to IV: A validated prognostic nomogram

Contact Info

Product

Resources

About