WEEE, A Multi-Device and Multi-Modal Dataset for Wearable Human Energy Expenditure Estimation
- 1. Università della Svizzera italiana (USI), Lugano, Switzerland
- 2. Nokia Bell Labs, Cambridge, United Kingdom
- 3. Nokia Bell Labs, Cambridge, United Kingdom
- 4. Università della Svizzera italiana (USI), , Lugano, Switzerland
We present WEEE, a multi-device and multi-modal dataset collected from 17 participants under different physical activities.
WEEE contains: 1) sensor data collected using 7 wearable devices placed on 4 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., muscle or fat percentage), 4) activity type - and their corresponding metabolic equivalent of task (MET) values - and intensity level, and 5) answers to questionnaires related to physical activity level, diet, stress and sleep. Thanks to the diversity of sensors and body locations of the WEEE dataset, we envision that this dataset will enable the development of novel human energy expenditure estimation techniques for a diverse set of application scenarios. Energy expenditure (EE) refers to the amount of energy an individual uses to maintain body functions and as a result of physical activity. The ability to estimate EE allows computing systems obtaining valuable insights regarding people’s physical activity and providing personalized recommendations for promoting a healthier and more active lifestyle.