The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors

Park, Hee Su; Dong, Suh-Yeon; Lee, Miran; Youn, Inchan

doi:10.3390/s17071698

Cited by 30 publications

(15 citation statements)

References 31 publications

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“…Although some of these activities could be related to sports, such as running, research was not focused on the sports field. A considerable number of jobs (30 articles) were also labeled as "not focused on wrist devices" (e.g., [23][24][25][26]), since the wearable device used was not intended to be worn on the wrist or forearm, or because a wearable wrist device was used, but it had little significance when additional wearables were used in other parts of the body or in the equipment used in sports. 26 articles were labeled for exclusion either because they were focused on analyzing the performance of a sensor (13 articles) (e.g., [27][28][29][30]) or because they were oriented to propose some algorithm for improving the performance of a heart rate sensor (13 articles) (e.g., [31][32][33]).…”

Section: Resultsmentioning

confidence: 99%

Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review

et al. 2019

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Wrist wearables are becoming more and more popular, and its use is widespread in sports, both professional and amateur. However, at present, they do not seem to exploit all their potential. The objective of this study is to explore innovative proposals for the use of wearable wrist technology in the field of sports, to understand its potential and identify new challenges and lines of future research related to this technology. A systematic review of the scientific literature, collected in 4 major repositories, was carried out to locate research initiatives where wrist wearables were introduced to address some sports-related challenges. Those works that were limited to evaluating sensor performance in sports activities and those in which wrist wearable devices did not play a significant role were excluded. 26 articles were eventually selected for full-text analysis that discuss the introduction of wrist-worn wearables to address some innovative use in the sports field. This study showcases relevant proposals in 10 different sports. The research initiatives identified are oriented to the use of wearable wrist technology (i) for the comprehensive monitoring of sportspeople’s behavior in activities not supported by the vendors, (ii) to identify specific types of movements or actions in specific sports, and (iii) to prevent injuries. There are, however, open issues that should be tackled in the future, such as the incorporation of these devices in sports activities not currently addressed, or the provision of specific recommendation services for sport practitioners.

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Section: Resultsmentioning

confidence: 99%

Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review

et al. 2019

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“…To evaluate the performance of EE estimation and compare the performance with the related research, three indicators used in the related research [ 5 , 6 , 7 , 10 , 12 ], mean absolute error (MAE), MSE, and root MSE (RMSE), were adopted to evaluate the performance. The formulas for calculating MAE, MSE, and RMSE are as follows:

where

denotes the estimated EE from each model, and

denotes the actual EE from

portable calorimetry system.…”

Section: Methodsmentioning

confidence: 99%

“…Several studies have proposed systems using various sensors to map the EE measured by metabolic analyzers. Sensor systems can be divided into two types: contact-based [ 4 , 5 , 6 , 7 ] and noncontact [ 8 , 9 , 10 , 11 , 12 ]. In a contact-based sensor system, users must wear devices on body segments to measure EE.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to evaluating the EE, they analyzed the number and contributions of various sensors. Park et al [ 6 ] evaluated the EE of six activities in daily living by combining various sensors, including an IMU and electrocardiogram sensor. Three feature selection methods were combined with the linear support vector machine (SVM) to find the best input feature set.…”

Section: Introductionmentioning

confidence: 99%

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Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm

Lin

Lee

Fahn

et al. 2021

Sensors

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Insufficient physical activity is common in modern society. By estimating the energy expenditure (EE) of different physical activities, people can develop suitable exercise plans to improve their lifestyle quality. However, several limitations still exist in the related works. Therefore, the aim of this study is to propose an accurate EE estimation model based on depth camera data with physical activity classification to solve the limitations in the previous research. To decide the best location and amount of cameras of the EE estimation, three depth cameras were set at three locations, namely the side, rear side, and rear views, to obtain the kinematic data and EE estimation. Support vector machine was used for physical activity classification. Three EE estimation models, namely linear regression, multilayer perceptron (MLP), and convolutional neural network (CNN) models, were compared and determined the model with optimal performance in different experimental settings. The results have shown that if only one depth camera is available, optimal EE estimation can be obtained using the side view and MLP model. The mean absolute error (MAE), mean square error (MSE), and root MSE (RMSE) of the classification results under the aforementioned settings were 0.55, 0.66, and 0.81, respectively. If higher accuracy is required, two depth cameras can be set at the side and rear views, the CNN model can be used for light-to-moderate activities, and the MLP model can be used for vigorous activities. The RMSEs for estimating the EEs of standing, walking, and running were 0.19, 0.57, and 0.96, respectively. By applying the different models on different amounts of cameras, the optimal performance can be obtained, and this is also the first study to discuss the issue.

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“…If athletes adopt accelerometers with an EE predictive equation derived merely from the general population, their EE may be underestimated. Some approaches attempt to calibrate estimate EE simply by including heart rates (HR) as a parameter reflecting the exercise intensity ( Domene & Easton, 2014 ; Park et al., 2017 ; Lu et al., 2018 ; Kuo et al., 2018 ). However, it, on the other hand, introduces biases to estimate EE because HR is susceptible to physical fitness and psychological factors, such as excitement and nervousness ( Patrik Johansson et al., 2006 ).…”

Section: Introductionmentioning

confidence: 99%

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Chang

Hsu

et al. 2020

PeerJ

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Background Inertial sensors, such as accelerometers, serve as convenient devices to predict the energy expenditures (EEs) during physical activities by a predictive equation. Although the accuracy of estimate EEs especially matter to athletes receive physical training, most EE predictive equations adopted in accelerometers are based on the general population, not athletes. This study included the heart rate reserve (HRR) as a compensatory parameter for physical intensity and derived new equations customized for sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults. Methods With indirect calorimetry as the criterion measure (CM), the EEs of participants on a treadmill were measured, and vector magnitudes (VM), as well as HRR, were simultaneously recorded by a waist-worn accelerometer with a heart rate monitor. Participants comprised a sedentary group (SG), an exercise-habit group (EHG), a non-endurance group (NEG), and an endurance group (EG), with 30 adults in each group. Results EE predictive equations were revised using linear regression with cross-validation on VM, HRR, and body mass (BM). The modified model demonstrates valid and reliable predictions across four populations (Pearson correlation coefficient, r: 0.922 to 0.932; intraclass correlation coefficient, ICC: 0.919 to 0.930). Conclusion Using accelerometers with a heart rate monitorcan accurately predict EEs of athletes and non-athletes with an optimized predictive equation integrating the VM, HRR, and BM parameters.

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The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors

Cited by 30 publications

References 31 publications

Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review

Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review

Depth-Camera Based Energy Expenditure Estimation System for Physical Activity Using Posture Classification Algorithm

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

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