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WO2017213732A1 - Cadre de lit réglable et procédés de fonctionnement - Google Patents

Cadre de lit réglable et procédés de fonctionnement Download PDF

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Publication number
WO2017213732A1
WO2017213732A1 PCT/US2017/024370 US2017024370W WO2017213732A1 WO 2017213732 A1 WO2017213732 A1 WO 2017213732A1 US 2017024370 W US2017024370 W US 2017024370W WO 2017213732 A1 WO2017213732 A1 WO 2017213732A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
breathing rate
adjustable
computer processor
configuring
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2017/024370
Other languages
English (en)
Inventor
Matteo Franceschetti
Massimo Andreasi Bassi
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Eight Sleep Inc
Original Assignee
Eight Sleep Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/178,132 external-priority patent/US20170135883A1/en
Application filed by Eight Sleep Inc filed Critical Eight Sleep Inc
Publication of WO2017213732A1 publication Critical patent/WO2017213732A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F5/00—Orthopaedic methods or devices for non-surgical treatment of bones or joints; Nursing devices ; Anti-rape devices
    • A61F5/56—Devices for preventing snoring
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb occurring during breathing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4815—Sleep quality
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6892—Mats
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G7/00—Beds specially adapted for nursing; Devices for lifting patients or disabled persons
    • A61G7/002—Beds specially adapted for nursing; Devices for lifting patients or disabled persons having adjustable mattress frame
    • A61G7/015—Beds specially adapted for nursing; Devices for lifting patients or disabled persons having adjustable mattress frame divided into different adjustable sections, e.g. for Gatch position
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G7/00—Beds specially adapted for nursing; Devices for lifting patients or disabled persons
    • A61G7/002—Beds specially adapted for nursing; Devices for lifting patients or disabled persons having adjustable mattress frame
    • A61G7/018—Control or drive mechanisms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0242—Operational features adapted to measure environmental factors, e.g. temperature, pollution
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/117—Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4812—Detecting sleep stages or cycles
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6891—Furniture
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G2203/00—General characteristics of devices
    • A61G2203/30—General characteristics of devices characterised by sensor means
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G2203/00—General characteristics of devices
    • A61G2203/30—General characteristics of devices characterised by sensor means
    • A61G2203/34—General characteristics of devices characterised by sensor means for pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G2203/00—General characteristics of devices
    • A61G2203/30—General characteristics of devices characterised by sensor means
    • A61G2203/36—General characteristics of devices characterised by sensor means for motion
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G2203/00—General characteristics of devices
    • A61G2203/30—General characteristics of devices characterised by sensor means
    • A61G2203/46—General characteristics of devices characterised by sensor means for temperature

Definitions

  • Various embodiments relate generally to home automation devices, and human biological signal gathering and analysis.
  • REM sleep rapid eye movement
  • non-REM sleep First comes non-REM sleep, followed by a shorter period of REM sleep, and then the cycle starts over again.
  • stage one a person's eyes are closed, but the person is easily woken up. This stage may last for 5 to 10 minutes.
  • stage two a person is in light sleep. A person's heart rate slows and the person's body temperature drops. The person's body is getting ready for deep sleep.
  • Stage three is the deep sleep stage. A person is harder to rouse during this stage, and if the person was woken up, the person would feel disoriented for a few minutes. During the deep stages of non-REM sleep, the body repairs and regrows tissues, builds bone and muscle, and strengthens the immune system.
  • REM sleep happens 90 minutes after a person falls asleep. Dreams typically happen during REM sleep. The first period of REM typically lasts 10 minutes. Each of later REM stages gets longer, and the final one may last up to an hour. A person's heart rate and breathing quickens. A person can have intense dreams during REM sleep, since the brain is more active. REM sleep affects learning of certain mental skills.
  • an electric blanket is a blanket with an integrated electrical heating device that can be placed above the top bed sheet or below the bottom bed sheet.
  • the electric blanket may be used to pre-heat the bed before use or to keep the occupant warm while in bed.
  • turning on the electric blanket requires the user to remember to manually turn on the blanket, and then manually turn it on. Further, the electric blanket provides no additional functionality besides warming the bed.
  • the system includes a sensor strip, database, adjustable bed frame, and computer processor.
  • the sensor strip is configured to measure the biological signal associated with the user.
  • the sensor strip comprises a piezo sensor.
  • the biological signal comprises a breathing rate associated with the user, a heart rate associated with the user, and a motion associated with the user.
  • the database is configured to store the biological signal associated with the user.
  • the adjustable bed frame includes a plurality of zones corresponding to a plurality of users.
  • a zone in the plurality of zones comprises a plurality of adjustable sections.
  • a position associated with an adjustable section in the plurality of adjustable sections can be adjusted independently, the adjustable bed frame configured to receive a control signal, and to adjust the position associated with the adjustable section, based on the control signal.
  • the computer processor is communicatively coupled to the sensor strip, the adjustable bed frame, and database.
  • the computer processor is configured to identify the user based on at least one of: the heart rate associated with the user, the breathing rate associated with the user, or the motion associated with the user. Based on the identification, the computer processor retrieves from the database an average biological signal associated with the user, the average biological signal comprising an average heart rate associated with the user, an average breathing rate associated with the user, and an average motion associated with the user. Based on the biological signal and the average biological signal, the computer processor determines whether the user is having a sleep problem. When the user is having a sleep problem, the computer processor sends the control signal to the adjustable bed frame, the control signal comprising an identification associated with the adjustable section, and a position associated with the adjustable section. BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 is a diagram of a bed device, according to one embodiment.
  • FIG. 2A illustrates an example of a bed device, according to one embodiment.
  • FIG. 2B is an adjustable bed frame associated with the bed device of FIG. 2A, according to one embodiment.
  • FIG. 2C is an adjustable bed frame that includes a plurality of zones, according to one embodiment.
  • FIG. 3 illustrates an example of layers comprising a bed pad device, according to one embodiment.
  • FIG. 4A illustrates a user sensor placed on a sensor strip, according to one embodiment.
  • FIG. 4B illustrates a user sensor placed on a sensor strip, according to another embodiment.
  • FIGS. 5A, 5B, 5C, and 5D show different configurations of a sensor strip, to fit different size mattresses, according to one embodiment.
  • FIG. 6A illustrates the division of the heating coil into zones and subzones, according to one embodiment.
  • FIGS. 6B and 6C illustrate the independent control of the different subzones, according to one embodiment.
  • FIG. 7 is a flowchart of the process for deciding when to heat or cool the bed device, according to one embodiment.
  • FIG. 8 is a flowchart of the process for recommending a bed time to a user, according to one embodiment.
  • FIG. 9 is a flowchart of the process for activating the user's alarm, according to one embodiment.
  • FIG. 10 is a flowchart of the process for turning off an appliance, according to one embodiment.
  • FIG. 11 is a diagram of a system capable of automating the control of the home appliances, according to one embodiment.
  • FIG. 12 is an illustration of the system capable of controlling an appliance and a home, according to one embodiment.
  • FIG. 13 is a flowchart of the process for controlling an appliance, according to one embodiment.
  • FIG. 14 is a flowchart of the process for controlling an appliance, according to another embodiment.
  • FIG. 15 is a diagram of a system for monitoring biological signals associated with a user, and providing notifications or alarms, according to one embodiment.
  • FIG. 16 is a flowchart of a process for generating a notification based on a history of biological signals associated with a user, according to one embodiment.
  • FIG. 17 is a flowchart of a process for generating a comparison between a biological signal associated with a user and a target biological signal, according to one embodiment.
  • FIG. 18 is a flowchart of a process for detecting the onset of a disease, according to one embodiment.
  • FIG. 19 is a diagrammatic representation of a machine in the example form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies or modules discussed herein, may be executed.
  • biological signal and “bio signal” are synonyms, and are used interchangeably.
  • Reference in this specification to "sleep phase” means light sleep, deep sleep, or REM sleep.
  • Light sleep comprises stage one and stage two (non-REM sleep).
  • references in this specification to "one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure.
  • the appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
  • various features are described that may be exhibited by some embodiments and not by others.
  • various requirements are described that may be requirements for some embodiments but not others.
  • the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements.
  • the coupling or connection between the elements can be physical, logical, or a combination thereof.
  • two devices may be coupled directly or via one or more intermediary channels or devices.
  • devices may be coupled in such a way that information can be passed there between, while not sharing any physical connection with one another.
  • the words "herein,” “above,” “below,” and words of similar import, when used in this application shall refer to this application as a whole and not to any particular portions of this application. Where the context permits, words in the Detailed Description using the singular or plural number may also include the plural or singular number respectively.
  • the word "or,” in reference to a list of two or more items covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list.
  • module refers broadly to software, hardware, or firmware components (or any combination thereof). Modules are typically functional components that can generate useful data or another output using specified input(s). A module may or may not be self- contained.
  • An application program also called an "application”
  • An application may include one or more modules, or a module may include one or more application programs.
  • FIG. 1 is a diagram of a bed device, according to one embodiment.
  • Any number of user sensors 140, 150 monitor the bio signals associated with a user, such as the heart rate, the breathing rate, the temperature, motion, or presence associated with the user.
  • Any number of environment sensors 160, 170 monitor environment properties, such as temperature, sound, light, or humidity.
  • the user sensors 140, 150 and the environment sensors 160, 170 communicate their measurements to the processor 100.
  • the environment sensors 160, 170 measure the properties of the environment that the environment sensors 160, 170 are associated with. In one embodiment, the environment sensors 160, 170 are placed next to the bed.
  • the processor 100 determines, based on the bio signals associated with the user, historical bio signals associated with the user, user-specified preferences, exercise data associated with the user, or the environment properties received, a control signal, and a time to send the control signal to a bed device 120.
  • the processor 100 is connected to a database 180, which stores the biological signals associated with a user. Additionally, the database 180 can store average biological signals associated with the user, history of biological signals associated with a user, etc. In one embodiment, the database 180 can store a user profile, which contains user preferences associated with an adjustable bed frame.
  • FIG. 2A illustrates an example of the bed device of FIG. 1, according to one embodiment.
  • a sensor strip 210 associated with a mattress 200 of the bed device 120, monitors bio signals associated with a user sleeping on the mattress 200.
  • the sensor strip 210 can be built into the mattress 200, or can be part of a bed pad device. Alternatively, the sensor strip 210 can be a part of any other piece of furniture, such as a rocking chair, a couch, an armchair etc.
  • the sensor strip 210 comprises a temperature sensor, or a piezo sensor.
  • the environment sensor 220 measures environment properties such as temperature, sound, light or humidity. According to one embodiment, the environment sensor 220 is associated with the environment surrounding the mattress 200.
  • the processor 230 can be similar to the processor 100 of FIG. 1.
  • a processor 230 can be connected to the sensor strip 210 or the environment sensor 220 by a computer bus, such as an I2C bus.
  • the processor 230 can be connected to the sensor strip 210, or the environment sensor 220 by a communication network.
  • the communication network connecting the processor 230 to the sensor strip 210 or the environment sensor 220 includes one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof.
  • the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof.
  • LAN local area network
  • MAN metropolitan area network
  • WAN wide area network
  • a public data network e.g., the Internet
  • short range wireless network e.g., a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof.
  • the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof.
  • EDGE enhanced data rates for global evolution
  • GPRS general packet radio service
  • GSM global system for mobile communications
  • IMS Internet protocol multimedia subsystem
  • UMTS universal mobile telecommunications system
  • WiMAX worldwide interoperability for microwave access
  • LTE Long Term Evolution
  • CDMA code division multiple
  • the processor 230 is any type of microcontroller or any processor in a mobile terminal, fixed terminal, or portable terminal including a mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, cloud computer, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal navigation device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, television receiver, radio broadcast receiver, electronic book device, game device, the accessories and peripherals of these devices, or any combination thereof.
  • PCS personal communication system
  • PDAs personal digital assistants
  • audio/video player digital camera/camcorder
  • positioning device television receiver, radio broadcast receiver, electronic book device, game device, the accessories and peripherals of these devices, or any combination thereof.
  • FIG. 2B is an adjustable bed frame 250 associated with the bed device, according to one embodiment.
  • the adjustable bed frame includes a plurality of adjustable sections 240- 246.
  • the adjustable bed frame has a rest position, as seen in FIG. 2A, where all the adjustable sections 240-246 are at 0 height, and at 0° angle.
  • the rest position corresponds to the horizontal position of a regular bed.
  • the position associated with each adjustable section 240-246 includes a height relative to the rest position, and an angle relative to the rest position.
  • Adjustable section 240 corresponds to the head
  • adjustable section 242 corresponds to the back
  • adjustable section 244 corresponds to the legs
  • adjustable section 246 corresponds to the feet.
  • the position of each adjustable section 240-246 can be adjusted
  • the adjustable bed frame 250 is coupled to the processor 230.
  • the processor 230 is configured to identify the user based on at least one of: the heart rate associated with the user, the breathing rate associated with the user, or the motion associated with the user, because each user has a unique heart rate, breathing rate, and motion.
  • the processor 230 can also identify the user by receiving from a user device associated with the user an
  • the processor 230 retrieves from the database 180 a history of biological signals associated with a user, where the history of biological signals comprises a history of breathing rate signals, a history of heart rate signals, and a history of motion signals.
  • the history of biological signals comprises a normal biological signal range, such as a normal heart rate range associated with said user, a normal breathing rate range associated with said user, and a normal motion range associated with said user.
  • the normal biological signal range includes an average heart rate associated with the user, an average breathing rate associated with the user, and an average motion associated with the user.
  • the average biological signal includes an average high signal and an average low signal.
  • the average high signal includes the average high heart rate associated with the user, the average high breathing rate associated with a user, or the average high rate of motion associated with the user.
  • the average low signal includes the average low heart rate associated with the user, the average low breathing rate associated with a user, or the average low rate of motion associated with a user.
  • the history of biological signals can also include a normal frequency range associated with the breathing rate, the heart rate, and/or motion.
  • the normal frequency range includes a low frequency and a high frequency.
  • the normal frequency range can also include a probability of occurrence associated with each frequency detected in the breathing rate, the heart rate, and/or the motion.
  • the probability of occurrence includes information that a frequency of 50 Hz occurs with probability 0.03, frequencies within 100 Hz to 200 Hz occur with probability 1, frequency of 500 Hz occurs with probability 0.5, etc.
  • the processor 230 determines the sleep phase associated with the user. The processor 230 can then calculate the normal bio signal range associated with a particular sleep phase.
  • the bio signals associated with a user include an amplitude and a frequency.
  • the processor 230 determines a normal range of frequencies associated with the heart rate, the breathing rate, or the motion. In other words, the processor 230 determines an average high frequency and an average low frequency associated with the heart rate, the breathing rate, or the motion.
  • the processor 230 determines a normal range of amplitudes corresponding to the frequencies associated with the heart rate, the breathing rate or the motion, such as an average low amplitude and an average high amplitude.
  • the processor 230 determines the current amplitude and the current frequency associated with the current biological signal. When the current frequency associated with a biological signal is outside of the normal frequency range, the processor 230 detects a discrepancy.
  • the processor 230 determines which sleep problem the discrepancy is indicative of, such as snoring, sleep apnea, or restless leg. For example, the processor 230 can determine whether the breathing rate contains frequencies outside of the normal breathing rate frequency range and determine that the user is snoring. Similarly, the processor 230 can determine that the motion rate contains a frequency outside of the normal motion frequency range and determine that the user is suffering from restless leg.
  • the processor 230 transforms the breathing rate received from the piezo sensor to obtain a transformed breathing rate in the frequency domain, which includes frequencies and their corresponding amplitudes.
  • the processor 230 removes all frequencies below a certain threshold amplitude, such as 3 decibels (dB), from the transformed breathing rate.
  • a certain threshold amplitude such as 3 decibels (dB)
  • the processor 230 determines the probability of occurrence of the frequency. If the probability of occurrence is less than 0.2, the processor 230 determines that the user is snoring. In another embodiment, the processor 230 determines that the user is snoring when one or more frequencies in the transformed breathing rate are outside of the normal frequency range associated with the breathing rate.
  • the processor 230 uses machine-learning algorithms to determine whether the user is snoring.
  • a classifier associated with the machine-learning algorithm receives from the processor 230 a plurality of breathing rates without snoring, i.e., a plurality of normal breathing rates and a plurality of breathing rates with snoring.
  • the breathing rates can come from the user and/or people other than the user.
  • the classifier Based on the plurality of breathing rates without snoring and the plurality of breathing rates with snoring, the classifier creates a training model.
  • the training model given a new breathing rate associated with the user, determines whether the user is snoring.
  • the processor 230 sends a control signal to the adjustable bed frame to heighten or to lower an adjustable section associated with the bed frame. For example, if the processor 230 detects that the user is snoring or has sleep apnea, the processor 230 sends a control signal to the adjustable bed frame to heighten the adjustable section 240, corresponding to the head. If the processor 230 detects that the user has restless leg, the processor 230 sends a control signal to the adjustable bed frame to heighten the adjustable section 246, corresponding to the feet.
  • the processor 230 determines whether the user has fallen asleep while the bed is in the upright position, for example, whether the user has fallen asleep while watching a TV. If the user has fallen asleep and the bed is not in the rest position, the processor 230 sends a control signal to the adjustable frame to assume the rest position.
  • the user can specify the preferred position of the adjustable bed frame when a bio signal discrepancy is detected.
  • the user's preferred position is stored in a user profile in the database 180.
  • the user can specify the height and inclination of each of the adjustable sections 240-246 for each detected problem.
  • the user-specified height and inclination of each of the adjustable sections 240-246 when snoring is detected can be different from the user-specified height and inclination of each of the adjustable sections 240-246 when sleep apnea is detected.
  • a user can specify a rest position for the adjustable bed frame that is different from the default horizontal rest position.
  • the user-specified rest position can also be associated with the user profile and stored in the database 180.
  • FIG. 2C is an adjustable bed frame including a plurality of zones, according to one embodiment.
  • the adjustable bed frame includes a plurality of zones 260, 265 corresponding to a plurality of users. Each includes a plurality of adjustable sections.
  • Zone 260 includes adjustable sections 270-276, and zone 265 includes adjustable sections 278-284. Each adjustable section can be adjusted independently.
  • the processor 230 detects a user in one of the zones, for example, zone 260, the processor 230 identifies the user based on the breathing rate, heart rate, or motion associated with a user. According to another
  • the computer processor receives the user ID associated with the user from a user device associated with the user. Based on the identification, the processor 230 retrieves from the database 180 the user profile. According to the user profile, the processor 230 adjusts the rest position of the zone 260 to match the user specified rest position. When a sleep problem is detected, the processor 230, sends a control signal to adjust the bed frame to match the user-specified position.
  • FIG. 3 illustrates an example of layers comprising the bed pad device of FIG. 1, according to one embodiment.
  • the bed device 120 is a pad that can be placed on top of the mattress.
  • the pad comprises a number of layers.
  • a top layer 350 comprises fabric.
  • a layer 340 comprises batting and a sensor strip 330.
  • a layer 320 comprises coils for cooling or heating the bed device.
  • a layer 310 comprises waterproof material.
  • FIG. 4A illustrates a user sensor 420, 440, 450, 470 placed on a sensor strip 400, according to one embodiment.
  • the user sensors 420, 440, 450, 470 can be similar to or part of the sensor strip 210 of FIG. 2.
  • Sensors 470 and 440 comprise a piezo sensor, which can measure a bio signal associated with a user, such as the heart rate and the breathing rate.
  • Sensors 450 and 420 comprise a temperature sensor.
  • sensors 450, and 470 measure the bio signals associated with one user, while sensors 420, 440 measure the bio signals associated with another user.
  • Analog-to-digital converter 410 converts the analog sensor signals into digital signals to be communicated to a processor 230.
  • Computer bus 430 and 460 communicates the digitized bio signals to a processor.
  • the analog-to-digital converter 410 can be placed anywhere on the strip, such as the middle of the strip, the side of the strip, etc. In various embodiments there can be a plurality of sensors strips 400.
  • FIG. 4B illustrates a user sensor placed on a sensor strip according to another embodiment.
  • the sensor strip 480 includes two sections 485, 490.
  • Each sensor strip section 485, 490 includes a temperature sensor 405, 445, respectively, and a piezo sensor 415, 425, respectively.
  • the temperature sensors 405, 445 and the piezo sensors 415, 425 are connected to the analog-to-digital converter 495 using wires 425, 435 respectively.
  • the analog-to- digital converter 495 is placed on the side of the strip.
  • FIGS. 5 A and 5B show different configurations of the sensor strip, to fit different size mattresses, according to one embodiment.
  • FIGS. 5C and 5D show how such different configurations of the sensor strip can be achieved.
  • sensor strip 400 comprises a computer bus 510, 530, and a sensor striplet 505.
  • the computer bus 510, 530 can be bent at predetermined locations 540, 550, 560, 570. Bending the computer bus 515 at location 540 produces the maximum total length of the computer bus 530.
  • Computer bus 530 combined with a sensor striplet 505 fits a king size mattress 520. Bending the computer bus 515 at location 570 produces the smallest total length of the computer bus 510.
  • Computer bus 510 combined with a sensor striplet 505 fits a twin size mattress 500. Bending the computer bus 515 at location 560, enables the sensor strip 400 to fit a full-size bed. Bending the computer bus 515 at location 550 enables the sensor strip 400 to fit a queen-size bed.
  • twin mattress 500, or king mattress 520 can be similar to the mattress 200 of FIG. 2.
  • FIG. 6A illustrates the division of the heating coil 600 into zones and subzones, according to one embodiment.
  • the heating coil 600 is divided into two zones 660 and 610, each corresponding to one user of the bed.
  • Each zone 660 and 610 can be heated or cooled independently of the other zone in response to the user's needs.
  • the power supply associated with the heating coil 600 is divided into two zones, each power supply zone corresponding to a single user zone 660, 610. Further, each zone 660 and 610 is further subdivided into subzones.
  • Zone 660 is divided into subzones 670, 680, 690, and 695.
  • Zone 610 is divided into subzones 620, 630, 640, and 650.
  • the distribution of coils in each subzone is configured so that the subzone is uniformly heated.
  • the subzones may differ among themselves in the density of coils.
  • the data associated with the user subzone 670 has lower density of coils than subzone 680. This will result in subzone 670 having lower temperature than subzone 680 when the coils are heated.
  • subzones 670 will have higher temperature than subzone 680. According to one
  • subzones 680 and 630 with highest coil density correspond to the user's lower back, and subzones 695 and 650 with highest coil density correspond to user's feet.
  • the system will correctly identify which user is sleeping in which zone by identifying the user based on any of the following signals alone or in combination: heart rate, breathing rate, body motion, or body temperature associated with the user.
  • the system can also identify the user by receiving from a user device associated with the user ID associated with the user. For example, the user can specify the user ID of the person sleeping on the sensor strip. If there are multiple sensor strips and/or multiple sensors, the user can specify the ID of the person associated with each sensor strip and/or each sensor.
  • the power supply associated with the heating coil 600 is divided into a plurality of zones, each power supply zone corresponding to a subzone 620, 630, 640, 650, 670, 680, 690, 695.
  • the user can control the temperature of each
  • each user can independently specify the temperature preferences for each of the subzones. Even if the users switch sides of the bed, the system will correctly identify the user, and the preferences associated with the user by identifying the user based on any of the following signals alone or in combination: heart rate, breathing rate, body motion, or body temperature associated with the user. According to another embodiment, if the users switch sides of the bed, the system receives the user ID of the new user from a user device associated with the user and retrieves the preferences associated with the user.
  • FIGS. 6B and 6C illustrate the independent control of the different subzones in each zone 610, 660, according to one embodiment.
  • Set of uniform coils 611 connected to power management box 601, uniformly heats or cools the bed.
  • Subzone 615 heats or cools the neck.
  • Subzone 625 heats or cools the back.
  • Subzone 635 heats or cools the legs, and subzone 645 heats or cools the feet. Power is distributed to the coils via duty cycling of the power management box 605.
  • FIG. 7 is a flowchart of the process for deciding when to heat or cool the bed device, according to one embodiment.
  • the process obtains a biological signal associated with a user, such as presence in bed, motion, breathing rate, heart rate, or a temperature.
  • the process obtains the biological signal from a sensor associated with a user.
  • the process obtains environment property, such as the amount of ambient light and the bed temperature.
  • the process obtains environment property from and environment sensor associated with the bed device.
  • the process determines the control signal and the time to send a control signal.
  • the process sends the control signal to the bed device.
  • the control signal comprises an instruction to heat the bed device to the average nightly temperature associated with the user.
  • the control signal comprises an instruction to heat the bed device to a user-specified temperature.
  • the process sends a control signal to the bed device to cool the bed device to the average nightly temperature associated with the user.
  • the control signal comprises an instruction to cool the bed device to a user- specified temperature.
  • the process obtains a history of biological signals associated with the user.
  • the history of biological signals can be stored in a database associated with the bed device, or in a database associated with a user.
  • the history of biological signals comprises the average bedtime the user went to sleep for each day of the week; that is, the history of biological signals comprises the average bedtime associated with the user on Monday, the average bedtime associated with the user on Tuesday, etc.
  • the process determines the average bedtime associated with the user for that day of the week, and sends the control signal to the bed device, allowing enough time for the bed to reach the desired temperature, before the average bedtime associated with the user.
  • the control signal comprises an instruction to heat, or cool the bed to a desired temperature.
  • the desired temperature may be automatically determined, such as by averaging the historical nightly temperature associated with a user, or the desired temperature may be specified by the user.
  • the technology disclosed here categorizes the sleep phase associated with a user as light sleep, deep sleep, or REM sleep.
  • Light sleep comprises stage one and stage two sleep.
  • the technology performs the categorization based on the breathing rate associated with the user, heart rate associated with the user, motion associated with the user, and body temperature associated with the user. Generally, when the user is awake, the breathing is erratic. When the user is sleeping, the breathing becomes regular. The transition between being awake and sleeping is quick and lasts less than one minute.
  • FIG. 8 is a flowchart of the process for recommending a bedtime to the user, according to one embodiment.
  • the process obtains a history of sleep phase information associated with the user.
  • the history of sleep phase information comprises an amount of time the user spent in each of the sleep phases (light sleep, deep sleep, or REM sleep).
  • the history of sleep phase information can be stored in a database associated with the user. Based on this information, the process determines how much light sleep, deep sleep, and REM sleep the user needs on average every day.
  • the history of sleep phase information comprises the average bedtime associated with the user for each day of the week (e.g., the average bedtime associated with the user on Monday, the average bedtime associated with the user on Tuesday, etc.).
  • the process obtains user- specified wake-up time, such as the alarm setting associated with the user.
  • the process obtains exercise information associated with the user, such as the distance the user ran that day, the amount of time the user exercised in the gym, or the amount of calories the user burned that day.
  • the process obtains the exercise information from a user phone, a wearable device, a fitbit bracelet, or a database storing the exercise information.
  • the process recommends a bedtime to the user. For example, if the user has not been getting enough deep and REM sleep in the last few days, the process recommends an earlier bedtime to the user. Also, if the user has exercised more than the average daily exercise, the process recommends an earlier bedtime to the user.
  • FIG. 9 is a flowchart of the process for activating a user' s alarm, according to one embodiment.
  • the process obtains the compound bio signal associated with the user.
  • the compound bio signal associated with the user comprises the heart rate associated with the user, and the breathing rate associated with the user.
  • the process obtains the compound bio signal from a sensor associated with the user.
  • the process extracts the heart rate signal from the compound bio signal. For example, the process extracts the heart rate signal associated with the user by performing low-pass filtering on the compound bio signal.
  • the process extracts the breathing rate signal from the compound bio signal. For example, the process extracts the breathing rate by performing bandpass filtering on the compound bio signal.
  • the breathing rate signal includes breath duration, pauses between breaths, as well as breaths per minute.
  • the process obtains user's wake-up time, such as the alarm setting associated with the user. Based on the heart rate signal and the breathing rate signal, the process determines the sleep phase associated with the user, and if the user is in light sleep, and current time is at most one hour before the alarm time, at block 940, the process activates an alarm. Waking up the user during the deep sleep or REM sleep is detrimental to the user's health because the user will feel disoriented, groggy, and will suffer from impaired memory. Consequently, at block 950, the process activates an alarm when the user is in light sleep and when the current time is at most one hour before the user specified wake-up time.
  • FIG. 10 is a flowchart of the process for turning off an appliance, according to one embodiment.
  • the process obtains the compound bio signal associated with the user.
  • the compound bio signal comprises the heart rate associated with the user and the breathing rate associated with the user.
  • the process obtains the compound bio signal from a sensor associated with the user.
  • the process extracts the heart rate signal from the compound bio signal by, for example, performing low- pass filtering on the compound bio signal.
  • the process extracts the breathing rate signal from the compound bio signal by, for example, performing bandpass filtering on the compound bio signal.
  • the process obtains an environment property comprising temperature, humidity, light, and sound from an environment sensor associated with the sensor strip.
  • the process determines whether the user is sleeping. If the user is sleeping, the process, at block 1050 turns an appliance off. For example, if the user is asleep and the environment temperature is above the average nightly temperature, the process turns off the thermostat. Further, if the user is asleep and the lights are on, the process turns off the lights. Similarly, if the user is asleep and the TV is on, the process turns off the TV. Smart home
  • FIG. 11 is a diagram of a system capable of automating the control of the home appliances, according to one embodiment.
  • Any number of user sensors 1140, 1150 monitor biological signals associated with the user, such as temperature, motion, presence, heart rate, or breathing rate.
  • Any number of environment sensors 1160, 1170 monitor environment properties, such as temperature, sound, light, or humidity. According to one embodiment, the environment sensors 1160, 1170 are placed next to a bed. The user sensors 1140, 1150 and the environment sensors 1160, 1170 communicate their measurements to the processor 1100.
  • the processor 1100 determines, based on the current biological signals associated with the user, historical biological signals associated with the user, user-specified preferences, exercise data associated with the user, and the environment properties received, a control signal, and a time to send the control signal to an appliance 1120, 1130.
  • the processor 1100 is any type of microcontroller or any processor in a mobile terminal, fixed terminal, or portable terminal including a mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, cloud computer, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal navigation device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, television receiver, radio broadcast receiver, electronic book device, game device, the accessories and peripherals of these devices, or any combination thereof.
  • PCS personal communication system
  • PDAs personal digital assistants
  • audio/video player digital camera/camcorder
  • positioning device television receiver, radio broadcast receiver, electronic book device, game device, the accessories and peripherals of these devices, or any combination thereof.
  • the processor 1100 can be connected to the user sensor 1140, 1150, or the environment sensor 1160, 1170 by a computer bus, such as an I2C bus. Furthermore, the processor 1100 can be connected to the user sensor 1140, 1150, or environment sensor 1160, 1170 by a communication network 1110.
  • the communication network 1110 connecting the processor 1100 to the user sensor 1140, 1150, or the environment sensor 1160, 1170 includes one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof.
  • the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), public data network (e.g., the Internet), short range wireless network, or any other suitable packet- switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof.
  • the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system
  • EDGE enhanced data rates for global evolution
  • GPRS general packet radio service
  • GSM global system for mobile communications
  • IMS Internet protocol multimedia subsystem
  • UMTS mobile ad-hoc network
  • any other suitable wireless medium e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof.
  • WiMAX worldwide interoperability for microwave access
  • LTE Long Term Evolution
  • CDMA code division multiple access
  • WCDMA wideband code division multiple access
  • WiFi wireless fidelity
  • WLAN wireless LAN
  • Bluetooth® Internet Protocol (IP) data casting
  • satellite mobile ad-hoc network
  • IP mobile ad-hoc network
  • FIG. 12 is an illustration of the system capable of controlling an appliance and a home, according to one embodiment.
  • the appliances that the system disclosed here can control comprise an alarm, a coffee machine, a lock, a thermostat, a bed device, a humidifier, or a light.
  • the system detects that the user has fallen asleep, the system sends a control signal to the lights to turn off, to the locks to engage, and to the thermostat to lower the temperature.
  • the system detects that the user has woken up and it is morning, the system sends a control signal to the coffee machine to start making coffee.
  • FIG. 13 is a flowchart of the process for controlling an appliance, according to one embodiment.
  • the process obtains history of biological signals, such as at what time the user goes to bed on a particular day of the week (e.g., the average bedtime associated with the user on Monday, the average bedtime associated with the user on Tuesday, etc.).
  • the history of biological signals can be stored in a database associated with the user or a database associated with the bed device.
  • the process obtains history of biological signals, such as at what time the user goes to bed on a particular day of the week (e.g., the average bedtime associated with the user on Monday, the average bedtime associated with the user on Tuesday, etc.).
  • the history of biological signals can be stored in a database associated with the user or a database associated with the bed device.
  • a database associated with the user e.g., the average bedtime associated with the user on Monday, the average bedtime associated with the user on Tuesday, etc.
  • the process also obtains user specified preferences, such as the preferred bed temperature associated with the user. Based on the history of biological signals and user-specified preferences, the process, at block 1320, determines a control signal and a time to send the control signal to an appliance. At block 1330, the process determines whether to send a control signal to an appliance. For example, if the current time is within half an hour of average bedtime associated with the user on that particular day of the week, the process, at block 1340, sends a control signal to an appliance.
  • the control signal comprises an instruction to turn on the bed device and the user-specified bed temperature.
  • the bed temperature is determined automatically, such as by calculating the average nightly bed temperature associated with a user.
  • the process obtains a current biological signal associated with a user from a sensor associated with the user.
  • the process also obtains environment data, such as the ambient light, from an environment sensor associated with a bed device. Based on the current biological signal, the process identifies whether the user is asleep. If the user is asleep and the lights are on, the process sends an instruction to turn off the lights. In another embodiment, if the user is asleep, the lights are off, and the ambient light is high, the process sends an instruction to the blinds to shut. In another embodiment, if the user is asleep, the process sends an instruction to the locks to engage.
  • the process obtains a history of biological signals, such as at what time the user goes to bed on a particular day of the week (e.g., the average bedtime associated with the user on Monday, the average bedtime associated with the user on Tuesday, etc.).
  • the history of biological signals can be stored in a database associated with the bed device or in a database associated with a user.
  • the user may specify a bedtime for the user for each day of the week.
  • the process obtains the exercise data associated with the user, such as the number of hours the user spent exercising, or the heart rate associated with the user during exercising.
  • the process obtains the exercise data from a user phone, a wearable device, fitbit bracelet, or a database associated with the user. Based on the average bedtime for that day of the week, and the exercise data during the day, the process, at block 1320, determines the expected bedtime associated with the user that night. The process then sends an instruction to the bed device to heat to a desired temperature, before the expected bedtime. The desired
  • temperature can be specified by the user or can be determined automatically, based on the average nightly temperature associated with the user.
  • FIG. 14 is a flowchart of the process for controlling an appliance, according to another embodiment.
  • the process receives a current biological signal associated with the user, such as the heart rate, breathing rate, presence, motion, or temperature, associated with the user. Based on the current biological signal, the process, at block 1410, identifies the current sleep phase(light sleep, deep sleep, or REM sleep).
  • the process at block 1420, also receives a current environment property value, such as the temperature, the humidity, the light, or the sound.
  • the process, at block 1430 accesses a database, which stores historical values associated with the environment property and the current sleep phase. That is, the database associates each sleep phase with an average historical value of the different environment properties.
  • the database may be associated with the bed device, may be associated with the user, or may be associated with a remote server.
  • the process calculates a new average of the environment property based on the current value of the environment property and the historical value of the environment property and assigns the new average to the current sleep phase in the database. If there is a mismatch between the current value of the environment property and the historical average, the process, at block 1450, regulates the current value to match the historical average.
  • the environment property can be the temperature associated with the bed device.
  • the database stores the average bed temperature corresponding to each of the sleep phases (light sleep, deep sleep, REM sleep). If the current bed temperature is below the historical average, the process sends a control signal to increase the temperature of the bed to match the historical average.
  • Biological signals associated with a person indicate the person's state of health. Changes in the biological signals can indicate an immediate onset of a disease, or a long-term trend that increases the risk of a disease associated with the person. Monitoring the biological signals for such changes can predict the onset of a disease, can enable calling for help when the onset of the disease is immediate, or can provide advice to the person if the person is exposed to a higher risk of the disease in the long-term.
  • FIG. 15 is a diagram of a system for monitoring biological signals associated with a user and providing notifications or alarms, according to one embodiment.
  • Any number of user sensors 1530, 1540 monitor bio signals associated with the user, such as temperature, motion, presence, heart rate, or breathing rate.
  • the user sensors 1530, 1540 communicate their measurements to the processor 1500.
  • the processor 1500 determines, based on the bio signals associated with the user, historical biological signals associated with the user, or user- specified preferences whether to send a notification or an alarm to a user device 1520.
  • the user device 1520 and the processor 1500 can be the same device.
  • the user device 1520 is any type of mobile terminal, fixed terminal, or portable terminal including a mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal navigation device, personal digital assistants (PDAs), audio/video player, digital
  • camera/camcorder positioning device, television receiver, radio broadcast receiver, electronic book device, game device, the accessories and peripherals of these devices, or any combination thereof.
  • the processor 1500 is any type of microcontroller, or any processor in a mobile terminal, fixed terminal, or portable terminal including a mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, cloud computer, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal navigation device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, television receiver, radio broadcast receiver, electronic book device, game device, the accessories and peripherals of these devices, or any combination thereof.
  • PCS personal communication system
  • PDAs personal digital assistants
  • audio/video player digital camera/camcorder
  • positioning device television receiver, radio broadcast receiver, electronic book device, game device, the accessories and peripherals of these devices, or any combination thereof.
  • the processor 1500 can be connected to the user sensor 1530, 1540 by a computer bus, such as an I2C bus. Also, the processor 1500 can be connected to the user sensor 1530, 1540 by a communication network 1510.
  • the communication network 1510 that connects the processor 1500 to the user sensor 1530, 1540 includes one or more networks such as a data network, a wireless network, a telephony network, or any
  • the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiberoptic network, and the like, or any combination thereof.
  • LAN local area network
  • MAN metropolitan area network
  • WAN wide area network
  • a public data network e.g., the Internet
  • short range wireless network e.g., a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiberoptic network, and the like, or any combination thereof.
  • the wireless network may be, for example, a cellular network, and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof.
  • EDGE enhanced data rates for global evolution
  • GPRS general packet radio service
  • GSM global system for mobile communications
  • IMS Internet protocol multimedia subsystem
  • UMTS universal mobile telecommunications system
  • WiMAX worldwide interoperability for microwave access
  • LTE Long Term Evolution
  • CDMA code division
  • FIG. 16 is a flowchart of a process for generating a notification based on a history of biological signals associated with a user, according to one embodiment.
  • the process obtains a history of biological signals, such as the presence history, motion history, breathing rate history, or heart rate history, associated with the user.
  • the history of biological signals can be stored in a database associated with a user.
  • the process determines if there is an irregularity in the history of biological signals within a timeframe. If there is an irregularity, at block 1620, the process generates a notification to the user.
  • the timeframe can be specified by the user, or it can be automatically determined based on the type of irregularity.
  • the heart rate associated with the user goes up within a one-day timeframe when the user is sick.
  • the process detects an irregularity, specifically, that a daily heart rate associated with the user is higher than normal. Consequently, the process warns the user that the user may be getting sick.
  • the process detects an irregularity, such as that an elderly user is spending at least 10% more time in bed per day over the last several days, than the historical average. The process generates a notification to the elderly user, or to the elderly user's caretaker, such as how much more time the elderly user is spending in bed.
  • the process detects an irregularity, such as an increase in resting heart rate, by more than 15 beats per minute, over a ten-year period. Such an increase in the resting heart rate doubles the likelihood that the user will die from heart disease, compared to those people whose heart rates remained stable. Consequently, the process warns the user that the user is at risk of heart disease.
  • FIG. 17 is a flowchart of a process for generating a comparison between a biological signal associated with a user and a target biological signal, according to one embodiment.
  • the process obtains a current biological signal associated with a user, such as presence, motion, breathing rate, temperature, or heart rate, associated with the user.
  • the process obtains the current biological signal from a sensor associated with the user.
  • the process at block 1710, then obtains a target biological signal, such as a user-specified biological signal, a biological signal associated with a healthy user, or a biological signal associated with an athlete.
  • a target biological signal such as a user-specified biological signal, a biological signal associated with a healthy user, or a biological signal associated with an athlete.
  • the process obtains the target biological signal from a user, or a database storing biological signals.
  • the process at block 1720, compares the current bio signal associated with the user and the target bio signal, and generates a notification based on the comparison 1730.
  • the comparison of the current bio signal associated with the user and the target bio signal comprises detecting a higher frequency in the current biological signal than in the target biological signal, detecting a lower frequency in the current biological signal than in the target biological signal, detecting higher amplitude in the current biological signal than in the target biological signal, or detecting lower amplitude in the current biological signal than in the target biological signal.
  • the process of FIG. 17 can be used to detect if an infant has a higher risk of sudden infant death syndrome ("SIDS").
  • SIDS victims less than one month of age heart rate is higher than in healthy infants of same age during all sleep phases.
  • SIDS victims greater than one month of age show higher heart rates during the REM sleep phase.
  • the process obtains the current bio signal associated with the sleeping infant, and a target biological signal associated with the heart rate of a healthy infant, where the heart rate is at the high end of a healthy heart rate spectrum.
  • the process obtains the current bio signal from a sensor strip associated with the sleeping infant.
  • the process obtains the target biological signal from a database of biological signals. If the frequency of the biological signal of the infant exceeds the target biological signal, the process generates a notification to the infant's caretaker, that the infant is at higher risk of SIDS.
  • the process of FIG. 17 can be used in fitness training.
  • a normal resting heart rate for adults ranges from 60 to 100 beats per minute.
  • FIG. 17 generates a comparison between the actual bio signal associated with the user and the target bio signal 1720, and based on the comparison, the process generates a notification whether the user has reached his target, or whether the user needs to exercise more, at block 1730.
  • FIG. 18 is a flowchart of a process for detecting the onset of a disease, according to one embodiment.
  • the process obtains the current bio signal associated with a user, such as presence, motion, temperature, breathing rate, or heart rate, associated with the user.
  • the process obtains the current bio signal from a sensor associated with the user.
  • the process obtains a history of bio signals associated with the user from a database.
  • the history of bio signals comprises the bio signals associated with the user accumulated over time.
  • the history of biological signals can be stored in a database associated with a user.
  • the discrepancy between the current bio signal and the history of bio signals comprises a higher frequency in the current bio signal than in the history of bio signals, or a lower frequency in the current bio signal than in the history of bio signals.
  • the process of FIG. 18 can be used to detect an onset of an epileptic seizure.
  • a healthy person has a normal heart rate between 60 and 100 beats per minute.
  • the median heart rate associated with the person exceeds 100 beats per minute.
  • the process of FIG. 18 detects that the heart rate associated with the user exceeds the normal heart rate range associated with the user. The process then generates an alarm to the user' s caretaker that the user is having an epileptic seizure.
  • the process of FIG. 18 detects if the current heart rate is below the normal heart rate range associated with the user. The process then generates an alarm to the user' s caretaker that the user is having an epileptic seizure.
  • FIG. 19 is a diagrammatic representation of a machine in the example form of a computer system 1900 within which a set of instructions for causing the machine to perform any one or more of the methodologies or modules discussed herein may be executed.
  • the computer system 1900 includes a processor, memory, non-volatile memory, and an interface device. Various common components (e.g., cache memory) are omitted for illustrative simplicity.
  • the computer system 1900 is intended to illustrate a hardware device on which any of the components described in the example of FIGS. 1-18 (and any other components described in this specification) can be implemented.
  • the computer system 1900 can be of any applicable known or convenient type.
  • the components of the computer system 1900 can be coupled together via a bus or through some other known or convenient device.
  • computer system 1900 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, or a combination of two or more of these.
  • SOC system-on-chip
  • SBC single-board computer system
  • COM computer-on-module
  • SOM system-on-module
  • computer system 1900 may include one or more computer systems 1900, be unitary or distributed, span multiple locations, span multiple machines, or reside in a cloud, which may include one or more cloud components in one or more networks.
  • one or more computer systems 1900 may perform, without substantial spatial or temporal limitation, one or more steps of one or more methods described or illustrated herein.
  • one or more computer systems 1900 may perform, in real time or in batch mode, one or more steps of one or more methods described or illustrated herein.
  • One or more computer systems 1900 may perform, at different times or at different locations, one or more steps of one or more methods described or illustrated herein, where appropriate.
  • the processor may be, for example, a conventional microprocessor such as an
  • machine-readable (storage) medium or
  • “computer-readable (storage) medium” include any type of device that is accessible by the processor.
  • the memory is coupled to the processor by, for example, a bus.
  • the memory can include, by way of example but not limitation, random access memory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM).
  • RAM random access memory
  • DRAM dynamic RAM
  • SRAM static RAM
  • the memory can be local, remote, or distributed.
  • the bus also couples the processor to the non-volatile memory and drive unit.
  • the non-volatile memory is often a magnetic floppy or hard disk, a magnetic-optical disk, an optical disk, a read-only memory (ROM), such as a CD-ROM, EPROM, or EEPROM, a magnetic or optical card, or another form of storage for large amounts of data. Some of this data is often written, by a direct memory access process, into memory during execution of software in the computer 1900.
  • the non-volatile storage can be local, remote, or distributed.
  • the non-volatile memory is optional because systems can be created with all applicable data available in memory.
  • a typical computer system will usually include at least a processor, memory, and a device (e.g., a bus) coupling the memory to the processor.
  • Software is typically stored in the non-volatile memory and/or the drive unit.
  • a software program is assumed to be stored at any known or convenient location (from non-volatile storage to hardware registers) when the software program is referred to as “implemented in a computer-readable medium.”
  • a processor is considered to be “configured to execute a program” when at least one value associated with the program is stored in a register readable by the processor.
  • the bus also couples the processor to the network interface device.
  • the interface can include one or more of a modem or network interface. It will be appreciated that a modem or network interface can be considered to be part of the computer system 1900.
  • the interface can include an analog modem, ISDN modem, cable modem, token ring interface, satellite transmission interface (e.g., "direct PC"), or other interfaces for coupling a computer system to other computer systems.
  • the interface can include one or more input and/or output devices.
  • the I/O devices can include, by way of example but not limitation, a keyboard, a mouse or other pointing device, disk drives, printers, a scanner, and other input and/or output devices, including a display device.
  • the display device can include, by way of example but not limitation, a cathode ray tube (CRT), liquid crystal display (LCD), or some other applicable known or convenient display device.
  • CTR cathode ray tube
  • LCD liquid crystal display
  • controllers of any devices not depicted in the example of FIG. 9 reside in the interface.
  • the computer system 1900 can be controlled by operating system software that includes a file management system, such as a disk operating system.
  • operating system software that includes a file management system, such as a disk operating system.
  • a file management system such as a disk operating system.
  • operating system software with associated file management system software is the family of operating systems known as Windows® from the Microsoft Corporation of Redmond, Washington, and their associated file management systems.
  • Windows® is the family of operating systems known as Windows® from the Microsoft Corporation of Redmond, Washington, and their associated file management systems.
  • Another example of operating system software with its associated file management system software is the family of operating systems known as Windows® from the Microsoft Corporation of Redmond, Washington, and their associated file management systems.
  • the file management system is typically stored in the non-volatile memory and/or drive unit and causes the processor to execute the various acts required by the operating system to input and output data and to store data in the memory, including storing files on the non-volatile memory and/or drive unit.
  • the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.
  • the machine may be a server computer, a client computer, a personal computer (PC), a tablet PC, a laptop computer, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, an iPhone, a Blackberry, a processor, a telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.
  • PC personal computer
  • PDA personal digital assistant
  • machine-readable medium or machine-readable storage medium is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” and “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.
  • the term “machine- readable medium” and “machine-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies or modules of the presently disclosed technique and innovation.
  • routines executed to implement the embodiments of the disclosure may be implemented as part of an operating system or a specific application, component, program, obj ect, module or sequence of instructions referred to as "computer programs.”
  • the computer programs typically comprise one or more instructions set at various times in various memory and storage devices in a computer that, when read and executed by one or more processing units or processors in a computer, cause the computer to perform operations to execute elements involving the various aspects of the disclosure.
  • machine-readable storage media machine-readable media, or computer-readable (storage) media
  • recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks, (DVDs), etc.), among others, and transmission-type media such as digital and analog communication links.
  • CD ROMS Compact Disk Read-Only Memory
  • DVDs Digital Versatile Disks
  • transmission-type media such as digital and analog communication links.
  • operation of a memory device may comprise a transformation, such as a physical transformation.
  • a physical transformation may comprise a physical transformation of an article to a different state or thing.
  • a change in state may involve an accumulation and storage of charge or a release of stored charge.
  • a change of state may comprise a physical change or transformation in magnetic orientation or a physical change or transformation in molecular structure, such as from crystalline to amorphous or vice-versa.
  • a storage medium typically may be non-transitory or comprise a non- transitory device.
  • a non-transitory storage medium may include a device that is tangible, meaning that the device has a concrete physical form, although the device may change its physical state.
  • non-transitory refers to a device remaining tangible despite this change in state.
  • the technology is capable of allowing multiple different users to use the same piece of furniture equipped with the presently disclosed technology. For example, different people can sleep in the same bed. In addition, two different users can switch the side of the bed that they sleep on, and the technology disclosed here will correctly identify which user is sleeping on which side of the bed.
  • the technology identifies the users based on any of the following signals alone or in combination: heart rate, breathing rate, body motion, or body temperature associated with each user.
  • the technology disclosed here identifies the user by receiving both the user ID and side of the bed associated with the user ID, from a device associated with the user.

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Abstract

L'invention concerne des procédés et des systèmes pour un cadre de lit réglable. Le cadre de lit réglable comprend une pluralité de sections réglables, chaque section pouvant être réglée séparément. Le cadre de lit réglable est couplé à un processeur configuré pour : recueillir des signaux biologiques associés à des utilisateurs multiples, tels que la fréquence cardiaque, la fréquence respiratoire ou la température ; analyser les signaux biologiques humains recueillis ; et régler, en fonction de l'analyse, la position des sections associées au cadre de lit réglable.
PCT/US2017/024370 2016-06-09 2017-03-27 Cadre de lit réglable et procédés de fonctionnement Ceased WO2017213732A1 (fr)

Applications Claiming Priority (2)

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US15/178,132 US20170135883A1 (en) 2015-11-16 2016-06-09 Adjustable bedframe and operating methods
US15/178,132 2016-06-09

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WO2017213732A1 true WO2017213732A1 (fr) 2017-12-14

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US11202517B2 (en) 2014-04-21 2021-12-21 Casper Sleep Inc. Mattress
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US11241100B2 (en) 2018-04-23 2022-02-08 Casper Sleep Inc. Temperature-regulating mattress
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CN112190419A (zh) * 2020-09-11 2021-01-08 深圳数联天下智能科技有限公司 一种睡眠管理的方法及装置

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