Monitoring female fertile ground: a pilot study of continuous body temperature measurement using a skin wearable device and machine learning integration

Siret Läänelaid1,2, Ignacio Garach Vélez3, Rait Rand4, Mihkel Tedremaa4, Luis J. Herrera3, Pablo Heredero3, Francisco M. Ortuño3, Triin Markus2, Aivar Orav2,5, Celia M. Tenorio6,7, Ruth Kalda1, Andres Salumets8,9,10,11, Signe Altmäe 6,7,10,11 1Department of Family Medicine, Faculty of Medicine, University of Tartu, 50411 Tartu, Estonia 2 Tartu Applied Health Sciences University, 50411 Tartu, Estonia 3 Department of Computer Engineering, Automatics and Robotics, ICAR, University of Granada, 18071 Granada, Spain 4TempID, Tallinn, 10621, Estonia 5 United Laboratories of Tartu University Hospital, 51014 Tartu, Estonia 6Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, 18071 Granada, Spain 7 Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, 18012, Spain 8 Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, 51014 Tartu, Estonia 9 Celvia CC, Tartu, 50411, Estonia 10 Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, Huddinge, Stockholm, 14183, Sweden 11 Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Huddinge, Stockholm, 14183, Sweden temperature_ovulation_dataset.csv contains anonymized, cleaned, filtered, annotated, and imputed temperature and ovulation data prepared for downstream analysis. MaxRange.csv contains the hourly mean temperature values computed within the maximum temperature range described in the article. These processed features were used as input for the machine learning pipeline.