SYSTEM FOR DETERMINING THE QUALITY OF SLEEP

• US 20160213309A1
• Filed: 01/15/2016
• Published: 07/28/2016
• Est. Priority Date: 01/27/2015
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Beddit Oy (Apple Inc.)
• Inventors: SANNHOLM, Fredrik, PAALASMAA, Joonas, LEPPAKORPI, Lasse
• Granted Patent: US 10,368,799 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0257 Proximity sensors
• CPC Class Codes: A61B 2562/0261 Strain gauges
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/6892 Mats
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7271 Specific aspects of physiological measurement analysis A61B5/316, A61B5/349 and A61B5/374 take 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: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Embodiments of the present invention provide a distributed computer system and a method for determining the quality of sleep of a sleeping user 340. The distributed computer system will determine the quality of sleep by detecting the changes in body posture of the sleeping user 340. The quality of sleep will be determined by the feature vectors associated with each posture of the sleeping user 340. The posture of the user will be determined by recording a body motion signal 200 caused by the mechanical and muscle movements of the body of the user.

Claims

1. A method of determining quality of sleep of at least one user (340), the method comprising:

recording (100) a body motion signal (200) using a peripheral device (300);

arranging (110) a communication connection between said peripheral device (300) and a mobile subscriber terminal (320);

communicating (120) said body motion signal (200) to a third party software application executing in said mobile subscriber terminal (320);

characterized in that,

determining (140) body posture changes of said at least one user (340) using both said body motion signal (200) and a respiration cycle amplitude (210) signal of the user, wherein the determination of body posture changes of said at least one user (340) is used in determining the quality of sleep.

2. The method as claimed in claim 1, characterized in that, one or more of the following physiological properties is determined from said body motion signal (200) and/or used instead of or in combination with said respiration cycle amplitude signal:

respiration cycle shape,

heartbeat cycle shape,

heartbeat cycle amplitude, and

value of presence signals.

3. The method as claimed in claim 1, characterized in that, one or more spikes in said body motion signal (200) are interpreted as body movements of said at least one user (340), and/or

one or more spikes in said body motion signal (200) and a step change in said respiration cycle amplitude (210) occurring simultaneously is determined to correspond to a body posture change of said at least one user (340), and/or

one or more spikes in said body motion signal (200) lacking a simultaneous step change in said respiration cycle amplitude (210) is determined to correspond to body movements but not a static body posture change of said at least one user (340).

4. The method as claimed in claim 1, characterized in that, the posture of said at least one user (340) is determined by classifying feature vectors describing the body posture, said classifier being arranged to know how the features of said body motion signal (200) look like in said at least one user'"'"'s (340) different body postures based on reference data in data storage, wherein parameters of said feature vector include the at least one physiological property determined from the body motion signal (respiration cycle shape, respiration cycle amplitude, heartbeat cycle shape, heartbeat cycle amplitude, presence signal value).

5. The method as claimed in claim 1, characterized in that, said third party software application stores images and/or data of body postures of said at least one user (340) during sleep into data storage, and displays said images and/or data later on a screen of said mobile subscriber terminal (320).

6. The method as claimed in claim 1, characterized in that, said body posture change is determined based on the respiration cycle amplitude of said body motion signal (200) of said at least one user (340), and said respiration cycle amplitude signal is analyzed with a step recognition method.

7. The method as claimed in claim 1, characterized in that, said peripheral device (300) comprises at least one sensor.

8. The method as claimed in claim 1, characterized in that, said peripheral device (300) comprises at least one dynamic sensor (310) selected from one or more of a piezoelectric sensor and a piezo thin film sensors, and/or

the peripheral device (300) comprises at least one static sensor (360) selected from one or more of a strain gauge, a capacitive sensor, a proximity sensor, a thermocouple, and an inductive sensor, and/or

the peripheral device (300) comprises at least one dynamic sensor (310) and at least one static sensor (360).

9. The method as claimed in claim 1, characterized in that, the peripheral device (300) comprises at least one sensor that is placed at multiple sensitive areas for determining presence signals, and/or further determining position of a person on the bed.

10. A distributed computer system for determining quality of sleep of at least one user (340), the distributed computer system comprising:

a mobile subscriber terminal (320);

a peripheral device (300) arranged to record a body motion signal (200) and to communicate said body motion signal (200) to a third party software application;

characterized in that,

the mobile subscriber terminal (320) comprises a processor (470) to execute the third party software application, wherein the third party application is arranged to determine body posture changes of the at least one user (340) based on said body motion signal (200) and a respiration cycle amplitude (210) signal of the user, wherein the determination of the body posture changes of said at least one user is configured to be used in determining the quality of sleep.

11. The distributed computer system as claimed in claim 10, characterized in that, one or more of the following physiological properties is configured to be determined from said body motion signal (200) and/or configured to be used instead of or in combination with said respiration cycle amplitude signal:

respiration cycle shape,

heartbeat cycle shape,

heartbeat cycle amplitude, and

value of presence signals.

12. The distributed computer system as claimed in claim 10, characterized in that, one or more spikes in said body motion signal (200) are configured to be interpreted as body movements of the at least one user (340), and/or

one or more spikes in said body motion signal (200) and a step change in the respiration cycle amplitude (210) occurring simultaneously is configured to correspond to the body posture change of at least one user (340), and/or

one or more spikes in said body motion signal (200) lacking a simultaneous step change in the respiration cycle amplitude (210) is configured to correspond to the body movement but no static body posture change of the at least one user (340).

13. The distributed computer system as claimed in claim 10, characterized in that, the posture of the at least one user (340) is arranged to be determined by classifying feature vectors describing the body posture;

said classifier being arranged to know how the features of body motion signal (200) are known to look like in different user (340) body postures based on reference data in data storage, wherein parameters of said feature vector include the at least one physiological property determined from the body motion signal (respiration cycle shape, respiration cycle amplitude, heartbeat cycle shape, heartbeat cycle amplitude, presence signal value).

14. The distributed computer system as claimed in claim 10, characterized in that, said third party software application is configured to store images and/or data of body postures of the at least one user (340) during sleep into data storage, and is configured to display said images and/or data later on screen of said mobile subscriber terminal (320).

15. The distributed computer system as claimed in claim 10, characterized in that, said body posture change is arranged to be determined based on the respiration cycle amplitude of the body motion signal (200) of the at least one user (340), and said respiration cycle amplitude signal is configured to be analyzed with a step recognition method.

16. The distributed computer system as claimed in claim 10, characterized in that, the peripheral device configured to comprise at least one sensor.

17. The distributed computer system as claimed in claim 10, characterized in that, the peripheral device (300) is arranged to comprise at least one dynamic sensor (310) selected from one or more of a piezoelectric sensor and a piezo thin film sensors.

18. The distributed computer system as claimed in claim 10, characterized in that, the peripheral device (300) is configured to comprise at least one static sensor (360) selected from one or more of a strain gauge, a capacitive sensor, a proximity sensor, a thermocouple, and an inductive sensor.

19. The distributed computer system as claimed in claim 10, characterized in that, the peripheral device (300) is arranged to comprise at least one dynamic sensor (310) and at least one static sensor (360).

20. The distributed computer system as claimed in claim 10, characterized in that, said the peripheral device (300) is arranged to comprise at least one sensor that is configured to be placed in multiple sensitive areas for determining presence signals, and/or further configured to determine the position of a person on the bed.

21. A computer program product for determining the quality of sleep of at least one user (340), the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, said computer-readable program code portions comprising instructions for:

recording (100) a body motion signal (200) using a peripheral device;

arranging (110) a communication connection between the peripheral device (300) and a mobile subscriber terminal (320);

communicating (120) said body motion signal (200) to a third party software application executing in said mobile subscriber terminal (320);

characterized in that,

determining (140) body posture changes of the at least one user based on said body motion signal (200) and a respiration cycle amplitude signal of the user, wherein the determination of the body posture changes of said at least one user is used to determine the quality of sleep.

22. The computer program product as claimed in claim 21, characterized in that, one or more of the following physiological properties is configured to be determined from said body motion signal (200) and/or configured to be used instead of or in combination with said respiration cycle amplitude signal:

respiration cycle shape,

heartbeat cycle shape,

heartbeat cycle amplitude, and

value of presence signals.

23. The computer program product as claimed in claim 21, characterized in that, one or more spikes in said body motion signal (200) are configured to be interpreted as body movements of the at least one user (340).

24. The computer program product as claimed in claim 21, characterized in that, one or more spikes in said body motion signal (200) and a step change in the respiration cycle amplitude (210) occurring simultaneously is configured to correspond to the body posture change of at least one user (340).

25. The computer program product as claimed in claim 21, characterized in that, one or more spikes in said body motion signal (200) lacking a simultaneous step change in the respiration cycle amplitude (210) is configured to correspond to the body movement but no static body posture change of the at least one user (340).

26. The computer program product as claimed in claim 21, characterized in that,

the posture of the at least one user (340) is configured to be determined by classifying feature vectors describing the body posture;

said classifier is arranged to know how the features of body motion signal (200) are known to look like in different user (340) body postures based on reference data in data storage;

parameters of said feature vector include the at least one physiological property determined from the body motion signal (respiration cycle shape, respiration cycle amplitude, heartbeat cycle shape, heartbeat cycle amplitude, presence signal value).

27. The computer program product as claimed in claim 21, characterized in that, said third party software application is configured to store images and/or data of body postures of the at least one user (340) during sleep into data storage, and configured to display said images and/or data later on screen of said mobile subscriber terminal.

28. The computer program product as claimed in claim 21, characterized in that, said body posture change is determined based on the respiration cycle amplitude of the body motion signal (200) of the at least one user (340), and said respiration cycle amplitude signal is configured to be analyzed with a step recognition method.

29. The computer program product as claimed in claim 21, characterized in that, the peripheral device is arranged to comprise at least one sensor.

30. The computer program product as claimed in claim 21, characterized in that, the peripheral device (300) is configured to comprise at least one dynamic sensor (310) selected from one or more of piezoelectric sensor and a piezo thin film sensors, and/or

the peripheral device (300) is configured to comprise at least one static sensor (360) selected from one or more of a strain gauge, a capacitive sensor, a proximity sensor, a thermocouple, and an inductive sensor, and/or

the peripheral device (300) is arranged to comprise at least one dynamic sensor (310) and at least one static sensor (360), and/or

said the peripheral device (300) is arranged to comprise at least one sensor that is configured to be placed on multiple sensitive areas for determining presence signals, and/or further configured to determine the position of a person on the bed.