A combined heart rate and movement index sensor for estimating the energy expenditure

Meina, Li; Kim, Jang Myoung; Kim, Youn Tae

doi:10.1109/icsens.2010.5690999

Cited by 5 publications

(5 citation statements)

References 6 publications

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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%

“…To adequately represent activity-induced caloric expenditure, we conducted a literature review on this physiological process [1,37,43,58,63]. Tracking the heat expended by nutrients oxidation (i.e., monitoring oxygen intake and carbon dioxide production) is considered the most accu- rate way of estimating energy expenditure [43].…”

Section: Caloric Expenditure Annotationsmentioning

confidence: 99%

“…Such catalogues provide energy expenditure values for specific activities that are often available online as look-up tables. A common way of estimating caloric cost, which is also leveraged by us, is deriving it from the heartrate with validated physiological models [27,37]. Our annotation scheme leverages three methods to estimate hourly energy expenditure: established medical compendiums [1], heart-rate based measurements [27] as well as adjustments based on the captured body movement [58].…”

Section: Caloric Expenditure Annotationsmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Related Workmentioning

confidence: 99%

Section: Caloric Expenditure Annotationsmentioning

confidence: 99%

Section: Caloric Expenditure Annotationsmentioning

confidence: 99%

See 1 more Smart Citation

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

Peng¹,

Roitberg²,

Yang³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Therefore we select iPod touch 4 as the device to develop heart rate extraction algorithm. The heart rate is linearly proportional to EE [3][4] [5] and this makes it a good indirect measurement of EE. However, everyone has his/her particular slope for the EE versus heart rate.…”

Section: A Core Application Flowmentioning

confidence: 99%

“…Previous works [2][3] [4] [5] have been done to estimate the EE. In these researches, the EE has shown a very strong linear relationship with the heart rate.…”

Section: Introductionmentioning

confidence: 99%

Mobile energy expenditure tracking system based on heart rate and motion providing extra extensions for personalized care

Chen

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-In order to keep a healthy body it is very important to have balanced energy expenditure (EE). The proposed system utilized the phonocardiogram (PCG) and motion from the user to generate the heart rate. The heart rate has been deemed as a very reliable index to estimate the EE of human body. Efforts are put on developing our system to deal the situations when severe interference coupled into the system while the user is exercising. Accelerometer is used to get the motion status and also the posture of the user which is monitored to determine any anomaly. If anomaly is detected, the system actively issues notification to whom that might give back a timely aid.

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