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

Siret Läänelaid^1,2, Ignacio Garach Vélez³, Rait Rand⁴, Mihkel Tedremaa⁴, Luis J. Herrera³, Pablo Heredero³, Francisco M. Ortuño³, Triin Markus², Aivar Orav^2,5, Celia M. Tenorio^6,7, Ruth Kalda¹, Andres Salumets^8,9,10,11, 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.