Journal article
Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermometer
IEEE transactions on biomedical engineering, Vol.67(2), pp.512-522
02/2020
DOI: 10.1109/TBME.2019.2916823
PMID: 31095472
Abstract
Objective: We present a non-invasive wearable device for fertility monitoring and propose an effective and flexible statistical learning algorithm to detect and predict ovulation using data captured by this device. Methods: The system consists of an earpiece, which measures the ear canal temperature every 5 min during night sleep hours, and a base station that transmits data to a smartphone application for analysis. We establish a data-cleaning protocol for data preprocessing and then fit a Hidden Markov Model (HMM) with two hidden states of high and low temperature to identify the more probable state of each time point via the predicted probabilities. Finally, a post-processing procedure is developed to incorporate biorhythm information to form a time-course biphasic profile for each subject. Results: The performance of the proposed algorithms applied to data collected by the device are compared with traditional methods in terms of match rate with self-reported ovulation days confirmed with an ovulation test kit. Empirical study results from a group of 34 users yielded significant improvements over the traditional methods in terms of detection accuracy (with sensitivity 92.31%) and prediction power (23.07-31.55% higher). Conclusion: We demonstrated the feasibility for reliable ovulation detection and prediction with high-frequency temperature data collected by a non-invasive wearable device. Significance: Traditional fertility monitoring methods are often either inaccurate or inconvenient. The wearable device and learning algorithm presented in this paper provide a user friendly and reliable platform for tracking ovulation, which may have a broad impact on both fertility research and real-world family planning.
Details
- Title: Subtitle
- Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermometer
- Creators
- Lan Luo - Department of BiostatisticsSchool of Public HealthUniversity of MichiganXichen She - Department of BiostatisticsSchool of Public HealthUniversity of MichiganJiexuan Cao - YONO Health IncYunlong Zhang - YONO Health IncYijiang Li - YONO Health IncPeter X. K Song - University of Michigan–Ann Arbor
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on biomedical engineering, Vol.67(2), pp.512-522
- Publisher
- IEEE
- DOI
- 10.1109/TBME.2019.2916823
- PMID
- 31095472
- ISSN
- 0018-9294
- eISSN
- 1558-2531
- Language
- English
- Date published
- 02/2020
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257600402771
Metrics
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