DatasetWearables.csv

Methods that individually predict oxygen uptake (VO2) without the need for calibrations based on biological data obtained by wearable sensors may offer an effective approach to measure physical activity levels during realistic unsupervised activities of daily living. To evoke insights for further scientists, this dataset describes the most comprehensive second-by-second estimated VO2 data from wearables during unsupervised activities of daily living. The variables heart rate, breathing frequency, minute ventilation, and total hip acceleration were obtained from a smart shirt. The VO2 data were estimated from these variables by a previously validated algorithm. In conclusion, this repertory made available the most comprehensive dataset of predicted second-by-second oxygen uptake estimated during unsupervised activities of daily living. In addition, a few data analyses were performed to further explore this dataset. Future studies may use this same dataset to search for more complex patterns between the variables presented.