Predicting energy cost from wearable sensors: A dataset of energetic and physiological wearable sensor data from healthy individuals performing multiple physical activities
2019-03-12T18:32:42Z (GMT) by
This dataset presents energetic and wearable physiological sensor data from ten healthy subjects performing six physical activities.
The activities tested were: walking, incline walking, backwards walking, and running on a treadmill, cycling on a stationary bike, and stair climbing on a stairmill -- all at a variety of speeds and/or intensities (21 total conditions).
The following physiological signals were collected from wearable sensors while subjects performed all the activities:
- Oxygen consumption and carbon dioxide production
- Respiratory exchange ratio
- Breath frequency
- Minute ventilation
- Oxygen saturation (SpO2)
- Heart rate
- Electrodermal activity
- Skin temperature
- Accelerations, angular velocity, and magnetic field measured from left/right wrist, left/right ankle, left/right foot, pelvis, and chest (IMUs)
- Surface EMG from left/right gluteus maximus, rectus femoris, vastus lateralis, semitendinosis, biceps femoris, medial gastrocnemius, soleus, tibialis anterior
The data are contained in ten (10) Matlab .mat files (one for each subject). For a complete description of the file structure please see the file: CompleteDataDescription_Ingraham_Ferris_Remy_2018
For a complete description of experimental methods please see the published article:
Ingraham, Kimberly A., Daniel P. Ferris, and C. David Remy. "Evaluating Physiological Signal Salience for Estimating Metabolic Energy Cost from Wearable Sensors." Journal of Applied Physiology (2019). DOI: 10.1152/japplphysiol.00714.2018
Edit history: Version 4 is the most current version (as of 3/12/2019). The only changes made between versions were updates to the CompleteDataDescription.pdf file for completeness.
Please direct any correspondence to: Kimberly Ingraham (email@example.com)