ArticleIEEE journal of translational engineering in health and medicine2015
Using Smartphone Sensors for Improving Energy Expenditure Estimation.
Article in IEEE journal of translational engineering in health and medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- OpenMetabolics: Estimating energy expenditure using a smartphone worn in a pocket.Communications engineering · 2026Article
- Smartphone-based activity tracking for spine patients: Current technology and future opportunities.World neurosurgery: X · 2024Review
- A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices.Scientific data · 2022Article
- Windows Into Human Health Through Wearables Data Analytics.Current opinion in biomedical engineering · 2019Article
- A Versatile Ultrasound Simulation System for Education and Training in High-Fidelity Emergency Scenarios.IEEE journal of translational engineering in health and medicine · 2017Article
- Wearable Wireless Sensors for Measuring Calorie Consumption.Journal of medical signals and sensorsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.