Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermometer

Luo, Lan; She, Xichen; Cao, Jiexuan; Zhang, Yunlong; Li, Yijiang; Song, Peter

doi:10.1109/tbme.2019.2916823

Cited by 31 publications

(37 citation statements)

References 23 publications

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“…[ 32 ] IR detection has become an important tool for wide application because of its noninvasive nature and the unique information it reveals. Some typical examples include night vision, [ 7 ] surveillance or target tracking in the military, temperature monitoring in the field of health care, [ 1,33 ] and noninvasive temperature detection and mapping in the industrial field. [ 34–36 ] Although new thermal imaging approaches are innovative, these designs are limited owing to the measurements with high spatial resolution and achieving conformal designs.…”

Section: Bioinspired Optical Sensorsmentioning

confidence: 99%

“…As evident from the ongoing COVID‐19 pandemic, the rapid and accurate noncontact detection of body temperature and viruses is crucial for the effective prevention of disease spread. [ 1–10 ] Sensor, as the most critical component in the detection process, is becoming extremely critical and important. Sensor can easily detect the measured information and transform them into electrical signals or other required forms according to certain rules.…”

Section: Introductionmentioning

confidence: 99%

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Toward the Burgeoning Optical Sensors with Ultra‐Precision Hierarchical Structures Inspired by Butterflies

Han

Liu

Chi

et al. 2021

Adv Materials Inter

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In recent years, there has been an increasing interest in the novel bioinspired sensors, especially for their selectivity, response time, and sensitivity. Particular attention is paid to the novel optical functional materials that are crucial for the sensors to achieve various energy transduction. Ideally, many critical parameters of the optical sensing materials can be appropriately tailored to meet various specific requirements using optimal design. Over a long and cruel evolution, nature's creatures have created a variety of photonic structures. One impressive example is the iridescent wings of butterfly. The natural photonic crystals on its surfaces have inspired diverse novel designs of optical functional sensing materials, especially in the past three years. Herein, this review mainly discusses recent advances of the burgeoning butterfly‐inspired sensing materials with optical properties that can be modulated in response to different external stimuli. First, their optical sensing performance to infrared radiation/thermal, vapor, liquid, pH, and so on are discussed in details. Then, the fabrication methods and their working mechanisms are also introduced. Furthermore, the general strategies of these emerging sensing materials and their potential applications are also discussed in‐depth as well as their limitations and the future challenges.

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Section: Bioinspired Optical Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward the Burgeoning Optical Sensors with Ultra‐Precision Hierarchical Structures Inspired by Butterflies

Han

Liu

Chi

et al. 2021

Adv Materials Inter

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“…Pairing machine learning with health data from wearable devices has the potential to improve the practice of clinical medicine. Some studies have already used machine learning to predict menstruation, ovulation and the fertile window based on body temperature data recorded by wearables [ 10 ]. Since previous studies have shown that heart rate (HR) varies during different phases of the menstrual cycle, with a higher rate during ovulation [ 11 ], some studies have employed wearables that collect data on body temperature, HR, heart rate variability (HRV), respiratory rate, etc., to develop algorithms for the prediction of ovulation and the fertile window [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms

Zhang

et al. 2022

Reprod Biol Endocrinol

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Background Fertility awareness and menses prediction are important for improving fecundability and health management. Previous studies have used physiological parameters, such as basal body temperature (BBT) and heart rate (HR), to predict the fertile window and menses. However, their accuracy is far from satisfactory. Additionally, few researchers have examined irregular menstruators. Thus, we aimed to develop fertile window and menstruation prediction algorithms for both regular and irregular menstruators. Methods This was a prospective observational cohort study conducted at the International Peace Maternity and Child Health Hospital in Shanghai, China. Participants were recruited from August 2020 to November 2020 and followed up for at least four menstrual cycles. Participants used an ear thermometer to assess BBT and wore the Huawei Band 5 to record HR. Ovarian ultrasound and serum hormone levels were used to determine the ovulation day. Menstruation was self-reported by women. We used linear mixed models to assess changes in physiological parameters and developed probability function estimation models to predict the fertile window and menses with machine learning. Results We included data from 305 and 77 qualified cycles with confirmed ovulations from 89 regular menstruators and 25 irregular menstruators, respectively. For regular menstruators, BBT and HR were significantly higher during fertile phase than follicular phase and peaked in the luteal phase (all P < 0.001). The physiological parameters of irregular menstruators followed a similar trend. Based on BBT and HR, we developed algorithms that predicted the fertile window with an accuracy of 87.46%, sensitivity of 69.30%, specificity of 92.00%, and AUC of 0.8993 and menses with an accuracy of 89.60%, sensitivity of 70.70%, and specificity of 94.30%, and AUC of 0.7849 among regular menstruators. For irregular menstruators, the accuracy, sensitivity, specificity and AUC were 72.51%, 21.00%, 82.90%, and 0.5808 respectively, for fertile window prediction and 75.90%, 36.30%, 84.40%, and 0.6759 for menses prediction. Conclusions By combining BBT and HR recorded by the Huawei Band 5, our algorithms achieved relatively ideal performance for predicting the fertile window and menses among regular menstruators. For irregular menstruators, the algorithms showed potential feasibility but still need further investigation. Trial registration ChiCTR2000036556. Registered 24 August 2020.

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“…When in ovaries, the eggs remain in sacs referred as follicles. The presence and size of follicles in ovaries is detected using conventional pelvic and/or trans-vaginal ultrasound scans [7]. As a follicle reaches approximate size of 18-28 mm, it is considered ready for ovulation.…”

Section: Introductionmentioning

confidence: 99%

A Nano-Architecture for Fertility Monitoring via Intra-body Communication

Siddiqui¹,

Khan²,

Abbasi³

et al. 2022

Preprint

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Fertility monitoring in humans for either natural conception or artificial insemination and fertilization has been a critical challenge both for the treating physician and the treated patients. Eggs in human female can be fertilized when they reach the Fallopian tube from the upper parts of the reproductive system. However, no technology, till date, on its own could detect the presence of eggs in the Fallopian tube and communicate their presence to the consulting physician or nurse and the patient, so that the next step can be initiated in a timely fashion. In this paper, we propose a conceptual architecture from a communications engineering point of view. The architecture combines intra-body nano-sensor network for detecting Fallopian tube activity, with body-area network for receiving information from the intra-body network and communicating the same over-the-air to the involved personnel (physician/nurse/patient). Preliminary simulations have been conducted using particle based stochastic simulator to investigate the relationship between achievable information rates, signal to noise ratio (SNR) and distance. It has been found that data rate as high as 300 Mbps is achievable at SNR 45. Hence, the proposed architecture ensures transfer of information with near-zero latency and minimum energy along with high throughput, so that necessary action can be taken within the short time-window of the Fallopian tube activity.

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Detection and Prediction of Ovulation From Body Temperature Measured by an In-Ear Wearable Thermometer

Cited by 31 publications

References 23 publications

Toward the Burgeoning Optical Sensors with Ultra‐Precision Hierarchical Structures Inspired by Butterflies

Toward the Burgeoning Optical Sensors with Ultra‐Precision Hierarchical Structures Inspired by Butterflies

Tracking of menstrual cycles and prediction of the fertile window via measurements of basal body temperature and heart rate as well as machine-learning algorithms

A Nano-Architecture for Fertility Monitoring via Intra-body Communication

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