ArticleScientific data2022
A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- A Personalized Energy Expenditure Estimation Method Using Modified MET and Heart Rate-Based DQN.Sensors (Basel, Switzerland) · 2025Article
- A Multimodal Dataset of Cardiac, Electrodermal, and Environmental Signals.Scientific data · 2025Article
- Conventional Training Integrated with SteamVR Tracking 2.0: Body Stability and Coordination Training Evaluation on ICAROS Pro.Sensors (Basel, Switzerland) · 2025Article
- Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study.JMIR mHealth and uHealth · 2024Observational
- Spiking neural networks for predictive and explainable modelling of multimodal streaming data with a case study on financial time series and online news.Scientific reports · 2023Article
- Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- A multidevice and multimodal dataset for human energy expenditure estimation using wearable devices.Scientific data · 2022Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a multi-device and multi-modal dataset, called WEEE, collected from 17 participants while they were performing different physical activities. WEEE contains: (1) sensor data collected using seven wearable devices placed on four body locations (head, ear, chest, and wrist); (2) respiratory data collected with an indirect calorimeter serving as ground-truth information; (3) demographics and body composition data (e.g., fat percentage); (4) intensity level and type of physical activities, along with their corresponding metabolic equivalent of task (MET) values; and (5) answers to questionnaires about participants' physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations, we believe that the dataset will enable the development of novel human energy expenditure (EE) estimation techniques for a diverse set of application scenarios. EE refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. A reliable estimate of people's EE thus enables computing systems to make inferences about users' physical activity and help them promoting a healthier lifestyle.
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