Activity-specific caloric expenditure estimation from kinetic energy harvesting in wearable devices

Ling, Xiao; Wu, Kai; Tian, Xiaobing; Luo, Juan

doi:10.1016/j.pmcj.2020.101185

Cited by 16 publications

(8 citation statements)

References 21 publications

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“…The requirements analysis and performance evaluation of the wearable sensors in medical applications was presented in [26], which addresses the fundamental issue of piezoelectric kinetic energy harvesting devices. In [31], a kinetic energy harvesting device is used instead of an accelerometer to assess calorie consumption as kinetic energy is generated when the user expends calories through bodily movements. A wearable medical sensor system for long-term health monitoring is described in [28].…”

Section: Energy Sources Power Densitymentioning

confidence: 99%

Designing an Efficient Hybrid Energy Harvesting System Compatible with Head/Wrist-Worn Medical Wearable Devices

Tohidinejad,

Danyali,

Valizadeh

et al. 2024

Preprint

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Section: Energy Sources Power Densitymentioning

confidence: 99%

Designing an Efficient Hybrid Energy Harvesting System Compatible with Head/Wrist-Worn Medical Wearable Devices

Tohidinejad,

Danyali,

Valizadeh

et al. 2024

Preprint

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“…Recent sudden developments in the field of machine learning has increased the potential for remote monitoring and diagnostics using data obtained from wearable devices (23). Such as standing balance estimation (24), ECG signal classification (25), and EE prediction (26), etc. Some studies have achieved good accuracy by using BPNN, random forest (RF) and other machine learning algorithms to build models (23,27).…”

Section: Estimation Algorithmmentioning

confidence: 99%

Energy Expenditure Estimation of Tabata by Combining Acceleration and Heart Rate

Yan

Chen

2022

Front. Public Health

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Tabata training plays an important role in health promotion. Effective monitoring of exercise energy expenditure is an important basis for exercisers to adjust their physical activities to achieve exercise goals. The input of acceleration combined with heart rate data and the application of machine learning algorithm are expected to improve the accuracy of EE prediction. This study is based on acceleration and heart rate to build linear regression and back propagate neural network prediction model of Tabata energy expenditure, and compare the accuracy of the two models. Participants (n = 45; Mean age: 21.04 ± 2.39 years) were randomly assigned to the modeling and validation data set in a 3:1 ratio. Each participant simultaneously wore four accelerometers (dominant hand, non-dominant hand, right hip, right ankle), a heart rate band and a metabolic measurement system to complete Tabata exercise test. After obtaining the test data, the correlation of the variables is calculated and passed to linear regression and back propagate neural network algorithms to predict energy expenditure during exercise and interval period. The validation group was entered into the model to obtain the predicted value and the prediction effect was tested. Bland-Alterman test showed two models fell within the consistency interval. The mean absolute percentage error of back propagate neural network was 12.6%, and linear regression was 14.7%. Using both acceleration and heart rate for estimation of Tabata energy expenditure is effective, and the prediction effect of back propagate neural network algorithm is better than linear regression, which is more suitable for Tabata energy expenditure monitoring.

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“…Visual estimation of calorie values has been mainly investigated in food image analysis (i.e., tracking the amount of caloric intake) [34,40,48]. Energy expenditure induced by physical activity is mostly studied from an egocentric perspective featuring data from wearable sensors, such as accelometors or heart rate monitors [2,5,18,26,37,39,41,55,64], with a recent survey provided in [70]. Only very few works address the visual predicted activity-related caloric expenditure [39,55].…”

Section: Related Workmentioning

confidence: 99%

Should I take a walk? Estimating Energy Expenditure from Video Data

Peng¹,

Roitberg²,

Yang³

et al. 2022

Preprint

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We explore the problem of automatically inferring the amount of kilocalories used by human during physical activity from his/her video observation. To study this underresearched task, we introduce Vid2Burn -an omni-source benchmark for estimating caloric expenditure from video data featuring both, high-and low-intensity activities for which we derive energy expenditure annotations based on models established in medical literature.In practice, a training set would only cover a certain amount of activity types, and it is important to validate, if the model indeed captures the essence of energy expenditure, (e.g., how many and which muscles are involved and how intense they work) instead of memorizing fixed values of specific activity categories seen during training. Ideally, the models should look beyond such category-specific biases and regress the caloric cost in videos depicting activity categories not explicitly present during training. With this property in mind, Vid2Burn is accompanied with a crosscategory benchmark, where the task is to regress caloric expenditure for types of physical activities not present during training. An extensive evaluation of state-of-the-art approaches for video recognition modified for the energy expenditure estimation task demonstrates the difficulty of this problem, especially for new activity types at test-time, marking a new research direction. Dataset and code are available at https://github.com/KPeng9510/ Vid2Burn.

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Activity-specific caloric expenditure estimation from kinetic energy harvesting in wearable devices

Cited by 16 publications

References 21 publications

Designing an Efficient Hybrid Energy Harvesting System Compatible with Head/Wrist-Worn Medical Wearable Devices

Designing an Efficient Hybrid Energy Harvesting System Compatible with Head/Wrist-Worn Medical Wearable Devices

Energy Expenditure Estimation of Tabata by Combining Acceleration and Heart Rate

Should I take a walk? Estimating Energy Expenditure from Video Data

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