Methods and systems for gathering and analyzing human biological signals

• US 9,981,107 B2
• Filed: 06/05/2015
• Issued: 05/29/2018
• Est. Priority Date: 06/05/2014
• Status: Active Grant

• Current Assignee: Eight Sleep, Inc
• Original Assignee: Eight Sleep, Inc
• Inventors: Franceschetti, Matteo, Bassi, Massimo Andreasi
• Primary Examiner(s): Alam, Mirza
• Application Number:
  US14/732,624
• Publication Number:
  US 20150355605A1
• Time in Patent Office: 1,089 Days
• CPC Class Codes: A47C 21/00 Attachments for beds, e.g. sheet holders, bed-cover holders bed tables supported on the bedstead A47B23/02; for children's beds A47D7/00, A47D15/00; Ventilating, cooling or heating means in connection with bedsteads or mattresses
• CPC Class Codes: A47C 21/044 with active means, e.g. by using air blowers or liquid pumps
• CPC Class Codes: A47C 21/048 for heating
• CPC Class Codes: A47C 31/123 for beds or mattresses
• CPC Class Codes: A47G 9/0238 Bed linen
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/0271 Thermal or temperature sensors
• CPC Class Codes: A61B 2562/063 in a linear array
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/117 Identification of persons methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00
• CPC Class Codes: A61B 5/4266 sweat secretion
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/6892 Mats
• CPC Class Codes: A61B 5/7455 : characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: A61M 16/161 : with means for measuring the humidity
• CPC Class Codes: A61M 2021/0022 : by the tactile sense, e.g. vibrations
• CPC Class Codes: A61M 2021/0066 : with heating or cooling
• CPC Class Codes: A61M 2021/0083 : especially for waking up
• CPC Class Codes: A61M 21/00 : Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
• CPC Class Codes: A61M 21/02 : for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia for massage A61H; electrotherapy A61N, e.g. applying alternating or intermittent electric currents for producing anaesthesia A61N1/36021
• CPC Class Codes: A61M 2205/0294 : Piezoelectric materials
• CPC Class Codes: A61M 2205/18 : with alarm
• CPC Class Codes: A61M 2205/3303 : Using a biosensor
• CPC Class Codes: A61M 2205/3306 : Optical measuring means
• CPC Class Codes: A61M 2205/3368 : Temperature
• CPC Class Codes: A61M 2205/3375 : Acoustical, e.g. ultrasonic, measuring means
• CPC Class Codes: A61M 2205/3584 : using modem, internet or bluetooth
• CPC Class Codes: A61M 2205/3592 : using telemetric means, e.g. radio or optical transmission
• CPC Class Codes: A61M 2205/3673 : thermo-electric, e.g. Peltier effect, thermocouples, semi-conductors
• CPC Class Codes: A61M 2205/50 : with microprocessors or computers
• CPC Class Codes: A61M 2205/505 : Touch-screens; Virtual keyboard or keypads; Virtual buttons; Soft keys; Mouse touches
• CPC Class Codes: A61M 2230/005 : Parameter used as control input for the apparatus
• CPC Class Codes: A61M 2230/06 : Heartbeat rate only
• CPC Class Codes: A61M 2230/42 : Rate
• CPC Class Codes: A61M 2230/50 : Temperature
• CPC Class Codes: A61M 2230/63 : Motion, e.g. physical activity
• CPC Class Codes: G05B 19/0428 : Safety, monitoring G05B19/0423 takes precedence
• CPC Class Codes: G05B 2219/2614 : HVAC, heating, ventillation, climate control
• CPC Class Codes: G05D 23/1393 : characterised by the use of electric means
• CPC Class Codes: G08B 6/00 : Tactile signalling systems, e.g. personal calling systems
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: H05B 1/0272 : For heating of fabrics

Abstract

Introduced are methods and systems for: gathering human biological signals, such as heart rate, breathing rate, or temperature; analyzing the gathered human biological signals; and controlling home appliances based on the analysis.

Claims

1. A method comprising:

obtaining a compound biological signal associated with a user from a sensor;

extracting a heart rate signal from said compound biological signal;

extracting a breathing rate signal from said compound biological signal;

obtaining a wake-up time specified by said user;

obtaining exercise information associated with said user, the exercise information comprising a daily exercise information associated with said user and a current day, the exercise information also comprising an average daily exercise information of said user;

comparing said daily exercise information and said average daily exercise information to determine a difference between said daily exercise information and said average daily exercise information;

determining sleep phase associated with said user based on said heart rate signal, and said breathing rate signal; and

activating an alarm based on said sleep phase, said difference between said daily exercise information and said average daily exercise information, and said wake-up time specified by said user.

2. The method of claim 1, wherein activating the alarm comprises:

checking that said sleep phase is light sleep; and

checking that current time is at most one hour before said wake-up time specified by said user.

3. The method of claim 1, wherein said alarm comprises a vibrating alarm attached to a cover, or a vibrating alarm attached to a mattress associated with said user.

4. The method of claim 1, wherein said alarm comprises a wearable alarm.

5. The method of claim 1, further comprising storing said heart rate signal and said breathing rate signal associated with said sleep phase associated with said user in a database.

6. The method of claim 1, further comprising identifying said user based on at least one of:

said heart rate signal, or said breathing rate signal.

7. The method of claim 1, wherein said sleep phase comprises light sleep, deep sleep, or REM sleep.

8. The method of claim 1, wherein extracting said breathing rate signal from said compound biological signal comprises performing low-pass filtering of said compound biological signal.

9. The method of claim 1, wherein extracting said heart rate signal from said compound biological signal comprises performing bandpass filtering of said compound biological signal.

10. A method comprising:

obtaining a compound biological signal associated with a user;

extracting a heart rate signal from said compound biological signal;

extracting a breathing rate signal from said compound biological signal;

obtaining an environment property;

obtaining exercise information associated with said user, the exercise information comprising an average daily exercise information of said user and a daily exercise information of said user associated with a current day;

comparing said daily exercise information and said average daily exercise information to determine a difference between said daily exercise information and said average daily exercise information;

determining a sleep phase associated with said user based on said heart rate signal, said breathing rate signal, and said environment property; and

determining a time to control an appliance based on said sleep phase, said difference between said daily exercise information and said average daily exercise information, and said environment property.

11. The method of claim 10, wherein determining the time to control said appliance comprises determining that said user is asleep.

12. The method of claim 10, wherein said appliance comprises a light, an entertainment device, or a thermostat.

13. The method of claim 10, further comprising identifying said user based on at least one of:

said heart rate signal, or said breathing rate signal.

14. The method of claim 10, wherein said sleep phase comprises a light sleep, a deep sleep, or a REM sleep.

15. The method of claim 10, wherein said environment property comprises a temperature, a humidity, a light intensity, a sound, or a current time.

16. The method of claim 10, wherein extracting said breathing rate signal from said compound biological signal comprises performing low-pass filtering of said compound biological signal.

17. The method of claim 10, wherein extracting said heart rate signal from said compound biological signal comprises performing bandpass filtering of said compound biological signal.

18. A method comprising:

obtaining a history of sleep phase information associated with a user from a database, the history of sleep phase information comprising an average bedtime associated with said user;

obtaining exercise information associated with said user, the exercise information comprising an average daily exercise information of said user and a daily exercise information associated with the user and a current day;

comparing said daily exercise information and said average daily exercise information to determine a difference between said daily exercise information and said average daily exercise information; and

when the difference indicates that the daily exercise information associated with the current day is greater than the average daily exercise information, recommending to said user a bedtime earlier than the average bedtime associated with said user.

19. The method of claim 18, wherein said exercise information comprises daily exercise information associated with said user.

20. The method of claim 18, wherein said history of sleep phase information comprises an amount of time said user spent in light sleep during each day of a week, an amount of time said user spent in deep sleep during each day of a week, or an amount of time said user spent in REM sleep during each day of a week.

21. The method of claim 18, wherein said history of sleep phase information comprises a plurality of average bedtimes associated with said user, wherein each average bedtime of said plurality of average bedtimes corresponds to a day of a week.

22. A system comprising:

a non-transitory storage medium, communicatively coupled to a computer processor; and

said computer processor configured to;

obtain a history of sleep phase information associated with a user from a database, the history of sleep phase information comprising an average bedtime associated with said user;

obtain exercise information associated with said user, the exercise information comprising an average daily exercise information of said user and a daily exercise information associated with said user and a current day; and

when the daily exercise information associated with the current day is greater than the average daily exercise information, recommend to said user a bedtime earlier than the average bedtime associated with said user.

23. The system of claim 22, wherein said exercise information comprises daily exercise information associated with said user.

24. The system of claim 22, wherein said history of sleep phase information comprises an amount of time said user spent in light sleep during each day of a week, an amount of time said user spent in deep sleep during each day of a week, or an amount of time said user spent in REM sleep during each day of a week.

25. The system of claim 22, wherein said history of sleep phase information comprises a plurality of average bedtimes associated with said user, wherein each average bedtime of said plurality of average bedtimes corresponds to a day of a week.