Predicting energy cost from wearable sensors: A dataset of energetic and physiological wearable sensor data from healthy individuals performing multiple physical activities

This dataset presents energetic and wearable physiological sensor data from ten healthy subjects performing six physical activities.<br>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<br>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<br>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." <i>Journal of Applied Physiology</i> (2019). DOI: 10.1152/japplphysiol.00714.2018<br><br>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. <br><br>Please direct any correspondence to: Kimberly Ingraham ([e-mail])<br><br><br><br>