MENSTRUAL STATE MONITORING

• US 20160058428A1
• Filed: 06/01/2015
• Published: 03/03/2016
• Est. Priority Date: 09/03/2014
• Status: Active Grant

• Current Assignee: EarlySense Ltd.
• Original Assignee: EarlySense Ltd.
• Inventors: SHINAR, Zvika, TSOREF, Liat, HALPERIN, Avner, MEGER, Guy
• Granted Patent: US 10,575,829 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 10/0012 Ovulation-period determination for animals A61D17/002
• CPC Class Codes: A61B 2503/045 Newborns, e.g. premature baby monitoring
• CPC Class Codes: A61B 2503/22 Motor vehicles operators, e.g. drivers, pilots, captains
• CPC Class Codes: A61B 2560/0487 Special user inputs or interfaces
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/4306 for evaluating the female reproductive systems, e.g. gynaecological evaluations
• CPC Class Codes: A61B 5/4343 Pregnancy and labour monitoring, e.g. for labour onset detection
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6891 Furniture
• CPC Class Codes: A61B 5/6892 Mats
• CPC Class Codes: A61B 5/6893 Cars
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B

Abstract

Apparatus and methods are described for monitoring a female subject. A sensor is configured to monitor the subject without contacting the subject or clothes the subject is wearing, and without viewing the subject or clothes the subject is wearing, and to generate a sensor signal in response to the monitoring. A computer processor is configured to receive the sensor signal, and to analyze the sensor signal. In response to the analyzing, the computer processor is configured to automatically identify a menstrual state of the subject, and to generate an output in response thereto. Other applications are also described.

Claims

1. Monitoring apparatus for monitoring a female subject, the apparatus monitoring comprising:

a sensor, configured to monitor the subject without the monitoring apparatus contacting the subject or clothes the subject is wearing, and without viewing the subject or clothes the subject is wearing, and to generate a sensor signal in response to the monitoring; and

a computer processor, configured to;

receive the sensor signal,

analyze the sensor signal,

in response to the analyzing, identify a menstrual state of the subject, and

generate an output in response thereto.

2. The monitoring apparatus according to claim 1, wherein the computer processor is configured to identify the subject'"'"'s menstrual state without determining a temperature of the subject.

3. The monitoring apparatus according to claim 1, wherein the computer processor is configured to identify the subject'"'"'s menstrual state by identifying a menstrual state of the subject that is currently occurring.

4. The monitoring apparatus according to claim 1, wherein the computer processor is configured to identify the subject'"'"'s menstrual state by predicting an occurrence of a future menstrual state of the subject.

5. The monitoring apparatus according to claim 1, wherein the computer processor is configured to identify the menstrual state of the subject, using a machine-learning algorithm.

6. The monitoring apparatus according to claim 1, wherein the computer processor is configured to identify the menstrual state of the subject by identifying that the subject is likely to ovulate in less than 10 days.

7. The monitoring apparatus according to claim 1,

wherein the computer processor is further configured, in response to identifying the menstrual state of the subject, to identify that the subject is likely to experience premenstrual syndrome (PMS) in less than three days, the computer processor being configured to generate the output in response thereto.

8. The monitoring apparatus according to claim 1, wherein the computer processor is configured:

in response to the analyzing, to identify an aspect of the sensor signal selected from the group consisting of;

a cardiac-related aspect of the sensor signal, and a respiration-related aspect of the sensor signal, and to identify the menstrual state of the subject, in response to the identified aspect.

9-17. -17. (canceled)

18. Monitoring apparatus for monitoring a female subject, the apparatus comprising:

a sensor, configured to monitor the subject without the monitoring apparatus contacting the subject or clothes the subject is wearing, and without viewing the subject or clothes the subject is wearing, and to generate a sensor signal in response to the monitoring; and

a computer processor, configured to;

receive the sensor signal,

derive a cardiac-related aspect of the sensor signal by analyzing the sensor signal,

based upon the derived cardiac-related aspect of the sensor signal, identify a menstrual state of the subject, and

generate an output in response thereto.

19. The monitoring apparatus according to claim 18, wherein the computer processor is configured to identify the subject'"'"'s menstrual state without determining a temperature of the subject.

20. The monitoring apparatus according to claim 18, wherein the computer processor is configured to identify the subject'"'"'s menstrual state by identifying a menstrual state of the subject that is currently occurring.

21. The monitoring apparatus according to claim 18, wherein the computer processor is configured to identify the subject'"'"'s menstrual state by predicting an occurrence of a future menstrual state of the subject.

22. The monitoring apparatus according to claim 18, wherein the computer processor is configured to identify the menstrual state of the subject, using a machine-learning algorithm.

23. The monitoring apparatus according to claim 18, wherein the computer processor is configured to identify the menstrual state of the subject by identifying that the subject is likely to ovulate in less than 10 days.

24. The monitoring apparatus according to claim 18,

wherein the computer processor is further configured, in response to identifying the menstrual state of the subject, to identify that the subject is likely to experience premenstrual syndrome (PMS) in less than three days,

the computer processor being configured to generate the output in response thereto.

25. The monitoring apparatus according to claim 18, wherein the cardiac-related aspect of the sensor signal includes a heart rate variability (HRV) signal, the computer processor being configured to identify the menstrual state of the subject in response to the HRV signal.

26-30. -30. (canceled)