Automatic Heart Rate Normalization for Accurate Energy Expenditure Estimation

Altini, Marco; Penders, Julien; Vullers, Ruud; Amft, OD Oliver

doi:10.3414/me13-02-0031

Cited by 8 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also plan to explore other data segmentation approaches, including the implementation of an activity recognition algorithm to flag bouts of purposeful exercise. Fourth, although some previous works found personalising physiological data can reduce inter-individual differences and improve the accuracy of EE or PA recognition [48], our experiment was unable to personalise the physiological features due to absence of resting data or sleep HR. Fifth, our study developed and tested models using data from healthy young adults.…”

Section: Discussionmentioning

confidence: 84%

Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data

Chowdhury

Tjondronegoro

Chandran

et al. 2019

Sensors

View full text Add to dashboard Cite

This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participants provided ratings of perceived exertion (RPE). Three classifiers, including random forest (RF), neural network (NN) and support vector machine (SVM), were applied independently on each feature set to predict relative PA intensity as low (RPE ≤ 11), moderate (RPE 12–14), or high (RPE ≥ 15). Then, both feature fusion and decision fusion of all combinations of sensor modalities were carried out to investigate the best combination. Among the single modality feature sets, HR provided the best performance. The combination of modalities using feature fusion provided a small improvement in performance. Decision fusion did not improve performance over HR features alone. A machine learning approach using features from HR provided acceptable predictions of relative PA intensity. Adding features from other sensing modalities did not significantly improve performance.

show abstract

Section: Discussionmentioning

confidence: 84%

Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data

Chowdhury

Tjondronegoro

Chandran

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…found that the ICC between measured EE, calculated by Actiheart with HR, accelerometers, and standard-measured by doubly labelled water was 0.81. Altini et al 43 . pointed out that the EE estimation error of measuring low intensity activities of daily living (ADLs) such as sedentary activities and low-speed walk (3–4 km/h) could be decreased by the HR standardized parameter.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of the energy expenditure during uphill exercise measured by the Waist-worn ActiGraph

Chang

Lin

et al. 2019

Journal of Exercise Science & Fitness

View full text Add to dashboard Cite

Background/objectiveThe application of Micro-Electro-Mechanical Sensors (MEMS) as measurements of energy expenditure (EE) has certain disadvantages. For example, the inertial sensors cannot easily distinguish changes in ground slope during walking/running conditions, so the accuracy of EE calculation is biased. To resolve this issue, heart rate (HR) and heart rate reserve (HRR) were used as compensatory factors respectively to correct the classical empirical formula of the accelerometer analyzer for EE in this study.MethodsTo explore the improvement of the accuracy of EE during uphill exercise and compare the correction levels between HR and HRR, oxygen uptake was used as a criterion measure (CM). Thirty healthy adult males wore an ActiGraph GT3X with the Polar HR monitor and Vmax indirect calorimeter during twelve treadmill activities (3 gradients and 4 speeds).ResultsWhen the slopes were increased by 0%, 3%, and 6%, the measurement accuracy of the accelerometers, calculated by intraclass correlation coefficient (ICC), decreased by 0.877, 0.755, and 0.504, respectively (p < 0.05). The HR and HRR parameters of linear regression were used to correct the classical formula. The results showed that HR had higher coefficients of determination (R2) (0.801, 0.700, and 0.642 respectively) and ICCs (0.887, 0.825, and 0.785 respectively) than did the accelerometer outputs. HRR showed the highest coefficients of determination (R2) (0.821, 0.728, and 0.656 respectively) and ICCs (0.901, 0.844, and 0.795 respectively).ConclusionsThrough adding HRR parameters, the accuracy of the classical prediction formula EE was significantly improved during walking/running on sloping ground.

show abstract

Personalized cardiorespiratory fitness and energy expenditure estimation using hierarchical Bayesian models

Altini¹,

Casale

Penders

et al. 2015

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Accurate estimation of energy expenditure (EE) and cardiorespiratory fitness (CRF) is a key element in determining the causal relation between aspects of human behavior related to physical activity and health. In this paper we estimate CRF without requiring laboratory protocols and personalize energy expenditure (EE) estimation models that rely on heart rate data, using CRF. CRF influences the relation between heart rate and EE. Thus, EE estimation based on heart rate typically requires individual calibration. Our modeling technique relies on a hierarchical approach using Bayesian modeling for both CRF and EE estimation models. By including CRF level in a hierarchical Bayesian model, we avoid the need for individual calibration or explicit heart rate normalization since CRF accounts for the different relation between heart rate and EE in different individuals. Our method first estimates CRF level from heart rate during low intensity activities of daily living, showing that CRF can be determined without specific protocols. Reference VO2max and EE were collected on a sample of 32 participants with varying CRF level. CRF estimation error could be reduced up to 27.0% compared to other models. Secondly, we show that including CRF as a group level predictor in a hierarchical model for EE estimation accounts for the relation between CRF, heart rate and EE. Thus, reducing EE estimation error by 18.2% on average. Our results provide evidence that hierarchical modeling is a promising technique for generalized CRF estimation from activities of daily living and personalized EE estimation.

show abstract

Automatic Heart Rate Normalization for Accurate Energy Expenditure Estimation

Cited by 8 publications

References 27 publications

Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data

Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data

Accuracy of the energy expenditure during uphill exercise measured by the Waist-worn ActiGraph

Personalized cardiorespiratory fitness and energy expenditure estimation using hierarchical Bayesian models

Contact Info

Product

Resources

About