System and method for contraception

1 . A method for determining for a female a time interval for using contraception, the method comprising:

receiving, in a processor, from a sensor system of a wearable device of the female, physiological data of the female;

generating, by the processor, cycle phase probabilities of the female being in one or more cycle phases of her menstrual cycle on a given day, by use of a machine learning model and the physiological data, the cycle phases comprising an early follicular phase, a fertile window, and/or a luteal phase;

determining, by the processor, the time interval for using contraception using the physiological data, the cycle phase probabilities, and pre-determined cycle phase probability thresholds; and

generating, by the processor, a message for the female comprising the time interval for using contraception.

2 . The method according to claim 1 , wherein receiving the physiological data comprises the processor receiving one or more of: skin temperature data, breathing rate data, resting pulse rate data, heart rate variability data, perfusion data, and pulse wave analysis data.

3 . The method according to claim 1 or 2 , wherein generating the cycle phase probabilities comprises the processor:

recording the physiological data for one or more menstrual cycles in a physiological data log; and

generating the cycle phase probabilities by use of the machine learning model, the physiological data, and the physiological data log.

4 . The method according to claim 1 or 2 , wherein generating the cycle phase probabilities comprises the processor training the machine learning model to generate the cycle phase probabilities using machine learning and a training dataset of physiological training data of a large group of females.

5 . The method according to claim 1 or 2 , wherein generating cycle phase probabilities comprises the processor using the machine learning model based on a neural network.

6 . The method according to claim 5 , wherein generating the cycle phase probabilities comprises the processor using the neural network with one or more of: a one-dimensional convolutional layer and a recurrent layer.

7 . The method according to claim 5 , wherein generating the cycle phase probabilities comprises the processor using the neural network with one or more one-dimensional convolutional layers configured to determine short-term features of an input data sequence of the physiological data, by generating a current output h i using a one-dimensional convolution of kernel size three, the one-dimensional convolution being modified to be solely retrospective by having as inputs input data terms x i−2 , X i−1 , and x i of the input data sequence, where the index i denotes time.

8 . A computer system for determining for a female a time interval for using contraception, the computer system comprising a processor configured to:

receive, from a sensor system of a wearable device of the female, physiological data of the female;

generate cycle phase probabilities of the female being in one or more cycle phases of her menstrual cycle on a given day, by use of a machine learning model and the physiological data, the cycle phases comprising an early follicular phase, a fertile window, and/or a luteal phase;

determine the time interval for using contraception using the physiological data, the cycle phase probabilities, and pre-determined cycle phase probability thresholds; and

generate a message for the female comprising the time interval for using contraception.

9 . The computer system according to claim 8 , wherein the processor is configured to receive one or more of:

skin temperature data, breathing rate data, resting pulse rate data, heart rate variability data, perfusion data, and pulse wave analysis data.

10 . The computer system) according to claim 8 or 9 , wherein the processor is further configured to:

record the physiological data for one or more menstrual cycles in a physiological data log; and

generate the cycle phase probabilities by use of the machine learning model, the physiological data, and the physiological data log.

11 . The computer system according to claim 8 or 9 , wherein the processor is further configured to train the machine learning model to generate the cycle phase probabilities using machine learning and a training dataset of physiological training data of a large group of females.

12 . The computer system according to claim 8 or 9 , wherein the processor is configured to use the machine learning model based on a neural network.

13 . The computer system according to claim 12 , wherein the processor is configured to use the neural network with one or more of: a one-dimensional convolutional layer and a recurrent layer.

14 . The computer system according to claim 12 , wherein the processor is configured to use the neural network with one or more one-dimensional convolutional layers configured to determine short-term features of an input data sequence of the physiological data, by generating a current output h i using a one-dimensional convolution of kernel size three, wherein the one-dimensional convolution is modified to be solely retrospective by having as inputs input data terms x i−2 , X i−1 , and x i of the input data sequence, where the index i denotes time.

15 . A computer program product comprising a non-transitory computer-readable medium having stored thereon computer program code configured to control a processor of a computer such that the computer performs the steps:

receiving from a sensor system of a wearable device of a female, physiological data of the female;

generating cycle phase probabilities of the female being in one or more cycle phases of her menstrual cycle on a given day, by use of a machine learning model and the physiological data, the cycle phases comprising an early follicular phase, a fertile window, and/or a luteal phase;

determining a time interval for using contraception using the physiological data, the cycle phase probabilities, and pre-determined cycle phase probability thresholds; and

generating a message for the female comprising the time interval for using contraception.