Classifications

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Definitions

• the present disclosure pertains to a system and method for facilitating sleep improvement for a user.
• one or more aspects of the present disclosure relate to a method for facilitating sleep improvements for a user.
• the method includes receiving, from one or more sensors, first user data associated with a first sleep session of a user. Based on the first user data, one or more first sleep metrics associated with the sleep session are determined.
• the method includes determining one or more reference sleep metrics, the one or more reference sleep metrics being based on prior user data obtained from one or more prior sleep sessions.
• One or more first immediate values related to the first sleep session is determined, the determination of the one or more first immediate values being based on a comparison of the one or more first sleep metrics with the one or more reference sleep metrics.
• a first sleep session score value is generated based on the one or more immediate values related to the first sleep session, and the first sleep session score value and the one or more first sleep metrics are caused to be presented via an output device.
• Another aspect of the present disclosure relates to a sleep score assessment system for facilitating sleep improvement of a user.
• the system includes memory, communications circuitry, and one or more processors configured by machine-readable instructions stored by the memory to receive, from one or more sensors, first user data associated with a first sleep session of a user. Based on the first user data, one or more first sleep metrics associated with the sleep session are determined.
• the one or more processors are configured by the machine-readable instructions to determine one or more reference sleep metrics, the one or more reference sleep metrics being based on prior user data obtained from one or more prior sleep sessions, and determine one or more first immediate values related to the first sleep session, the determination of the one or more first immediate values being based on a comparison of the one or more first sleep metrics with the one or more reference sleep metrics.
• the one or more processors are configured by the machine- readable instructions to generate a first sleep session score value based on the one or more immediate values related to the first sleep session, and cause the first sleep session score value and the one or more first sleep metrics are caused to be presented via an output device.
• the sleep score assessment system includes means for receiving, from one or more sensors, first user data associated with a first sleep session of a user, means for determining, based on the first user data, one or more first sleep metrics associated with the first sleep session, means for determining one or more reference sleep metrics, the one or more reference sleep metrics being based on prior user data obtained from one or more prior sleep sessions, means for determining one or more first immediate values related to the first sleep session, the determination of the one or more first immediate values being based on a comparison of the one or more first sleep metrics with the one or more reference sleep metrics, means for generating a first sleep session score value based on the one or more first immediate values related to the first sleep session, and means for causing the first sleep session score value and the one or more first sleep metrics to be presented via an output device.
• FIG. 1 is a schematic illustration of an exemplary system configured to facilitate sleep improvement for an individual, in accordance with various embodiments
• FIG. 2 is an illustrative graph of a technique for determining a total sleep time deduction, in accordance with various embodiments
• FIG. 3 is an illustrative graph of a technique for determining a deduction associated with a total time spent awake, in accordance with various embodiments
• FIG. 4 is an illustrative graph of a deduction capable of being applied to determining a sleep session score value based on a number of sleep disruptions detected, in accordance with various embodiments;
• FIG. 5 is an illustrative graph of a deduction capable of being applied to determining a sleep session score value based on a sleep routine of an individual, in accordance with various embodiments;
• FIG. 6 is an illustrative graph illustrating how deductions are related to a difference in bedtime regularity, in accordance with various embodiments
• FIG. 7 is an illustrative graph of a deduction capable of being applied to determining a sleep session score value based on sleep onset latency, in accordance with various embodiments;
• FIG. 8 is an illustrative graph of a deduction capable of being applied to determining a sleep session score value based on a duration of N3 sleep, in accordance with various embodiments;
• FIG. 9 is an illustrative diagram of an exemplary graphical user interface including a sleep session score and the various deductions capable of being applied, in accordance with various embodiments;
• FIGS. lOA-C are illustrative graphs of a cumulative slow wave activity parameter for different age ranges, in accordance with various embodiments.
• FIG. 1 1 is an illustrative graph of an amount of bonus capable of being applied for a sleep intervention, in accordance with various embodiments
• FIG. 12 is an illustrative flowchart of an exemplary process for generating a first sleep session score value, in accordance with various embodiments.
• FIG. 13 is an illustrative flowchart of an exemplary process for determining a sleep intervention to be provided and sending an instruction for that sleep intervention to be provided at a particular time, in accordance with various embodiments.
• the word "unitary” means a component is created as a single piece or unit. That is, a component that includes pieces that are created separately and then coupled together as a unit is not a “unitary” component or body.
• the statement that two or more parts or components "engage” one another shall mean that the parts exert a force against one another either directly or through one or more intermediate parts or components.
• the term “number” shall mean one or an integer greater than one (i.e., a plurality).
• FIG. 1 is a schematic illustration of an exemplary system 100 configured to facilitate sleep improvement for an individual, in accordance with various embodiments.
• System 100 includes a user device 1 10, a sleep score assessment system 120, and one or more intervention devices 130.
• Each of user device 110, sleep score assessment system 120, and intervention device(s) 130 are capable of communicating with one another via one or more network(s) 160.
• user device 1 10 and sleep score assessment system 120 may communicate over an intranet and/or the Internet.
• system 100 includes a user history database 140 and a reference database 150.
• user device 1 10 is configured to monitor activity of a user 170 and store inputs detected by one or more sensors 108.
• Sensor(s) 108 correspond to any suitable sensor capable of measuring one or more parameters of user 170.
• sensor(s) 108 may correspond to one or more accelerometers, one or more gyroscopes, one or more pulse rate monitors, one or more breath monitoring devices, and/or one or more electroencephalography (“EEG”) devices.
• EEG electroencephalography
• user device 1 10 may include any additional type of sensor, or any combination thereof.
• sensor(s) 108 are configured to take measurements at predefined temporal intervals.
• sensor(s) 108 may be configured (e.g., by processor(s) 102 using instructions stored by memory 104) to take a "sample” measurement every second.
• a sampling rate - how often sensor(s) 108 take a measurement - is configurable by user device 110 and can depend on a type of sensor that sensor(s) 108 correspond to, as well as a type of measurement that sensor(s) 108 is attempting to obtain.
• sensor(s) 108 are capable of being configured by user 170 and/or by one or more other devices of system 100 (e.g., sleep score assessment system 120) to modify a sample rate for sensor(s) 108 depending on a particular functionality desired.
• User device 1 10 in one embodiment, corresponds to any suitable type of electronic device including, but are not limited to, desktop computers, mobile computers (e.g., laptops, ultrabooks, etc.), mobile phones, smart phones, tablets, personal digital assistants ("PDAs"), and/or wearable devices (e.g., watches, pins/broaches, headphones, etc.).
• user device 1 10 includes one or more processors 102, memory 104, and communications circuitry 106.
• Processor(s) 102 include any suitable processing circuitry capable of controlling operations and functionality of user device 1 10, as well as facilitating communications between various components within user device 10.
• processor(s) 102 may include a central processing unit (“CPU"), a graphic processing unit (“GPU”), one or more microprocessors, a digital signal processor, or any other type of processor, or any combination thereof.
• the functionality of processor(s) 102 is performed by one or more hardware logic components including, but not limited to, field-programmable gate arrays (“FPGA”), application specific integrated circuits (“ASICs”), application-specific standard products (“ASSPs”), system-on-chip systems (“SOCs”), and/or complex programmable logic devices (“CPLDs").
• FPGA field-programmable gate arrays
• ASICs application specific integrated circuits
• ASSPs application-specific standard products
• SOCs system-on-chip systems
• CPLDs complex programmable logic devices
• each of processor(s) 102 is capable of including its own local memory, which may store program systems, program data, and/or one or more operating systems.
• processor(s) 102 are capable of running an operating system ("OS") for user device 1 10, and/or one or more firmware applications, media applications, and/or applications resident thereon.
• OS operating system
• processor(s) 102 runs a local client script for reading and rendering content received from one or more websites.
• processor(s) 102 may run a local JavaScript client for rendering HTML or XHTML content.
• Memory 104 includes one or more types of storage mediums such as any volatile or non-volatile memory, or any removable or non-removable memory implemented in any suitable manner to store data for user device 1 10. For example, information may be stored using computer-readable instructions, data structures, and/or program systems.
• Various types of storage/memory may include, but are not limited to, hard drives, solid state drives, flash memory, permanent memory (e.g., ROM), electronically erasable programmable read-only memory (“EEPROM”), CD-ROM, digital versatile disk (“DVD”) or other optical storage medium, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, RAID storage systems, or any other storage type, or any combination thereof.
• memory 104 may be implemented as computer-readable storage media ("CRSM”), which may be any available physical media accessible by processor(s) 102 to execute one or more instructions stored within memory 104.
• CRSM computer-readable storage media
• one or more applications e.g., gaming, music, video, calendars, lists, etc.
• processor(s) 102 are run by processor(s) 102, and may be stored in memory 104.
• Communications circuitry 106 corresponds to any circuitry allowing or enabling one or more components of user device 1 10 to communicate with one another, and/or with one or more additional devices, servers, and/or systems (e.g., sleep score assessment system 120 and/or intervention device(s) 130).
• user data corresponding to readings obtained by sensor(s) 108 may be transmitted over a network 160, such as the Internet, to sleep score assessment system 120 using any number of communications protocols.
• network(s) 160 are capable of being accessed using Transfer Control Protocol and Internet Protocol (“TCP/IP”) (e.g., any of the protocols used in each of the TCP/IP layers), Hypertext Transfer Protocol (“HTTP”), WebRTC, SIP, and wireless application protocol (“WAP”), are some of the various types of protocols that may be used to facilitate communications between user device 110, sleep score assessment system 120, and/or intervention device(s) 130.
• TCP/IP Transfer Control Protocol and Internet Protocol
• HTTP Hypertext Transfer Protocol
• WebRTC WebRTC
• SIP Session Transfer Protocol
• WAP wireless application protocol
• user device 1 10, sleep score assessment system 130, and/or intervention device(s) 130 communicate with one another via a web browser using HTTP.
• Various additional communication protocols used to facilitate communications between one or more devices of system 100 include, but not limited to, Wi-Fi (e.g., 802.1 1 protocol), Bluetooth, radio frequency systems (e.g., 900 MHz, 1.4 GHz, and 5.6 GHz communication systems), cellular networks (e.g., GSM, AMPS, GPRS, CDMA, EV-DO, EDGE, 3GSM, DECT, IS-136/TDMA, iDen, LTE or any other suitable cellular network protocol), infrared, BitTorrent, FTP, RTP, RTSP, SSH, and/or VOIP.
• Communications circuitry 106 is capable of using communications protocol, such as any of the previously mentioned exemplary communications protocols.
• user device 1 10 includes one or more antennas to facilitate wireless communications with a network using various wireless technologies (e.g., Wi-Fi, Bluetooth, radio frequency, etc.).
• user device 110 includes one or more universal serial bus ("USB") ports, one or more Ethernet or broadband ports, and/or any other type of hardwire access port so that communications circuitry 106 allows user device 1 10 to communicate with one or more communications networks (e.g., network(s) 160).
• USB universal serial bus
• user device 1 10 As illustrated by FIG. 1 , as user 170 sleeps, user device 1 10 - and in particular sensor(s) 108 - monitor sleep activity.
• user device 1 10 is configured to send user data associated with a first sleep session of user 170 to sleep score assessment system 120.
• user data indicating an activity of user 170 during a first sleep session may be sent to sleep score assessment system 170 via network(s) 160.
• the user data is provided to sleep score assessment system 120 at an end of the sleep session, however this is merely exemplary as the user data is capable of being provided to sleep score assessment system 120 periodically (e.g., every 5-10 minutes), and persons of ordinary skill in the art will recognize that the aforementioned is merely exemplary.
• Sleep score assessment system 120 in the non-limiting embodiment, includes processor(s) 102, memory 104, communications circuitry 106, and an I/O (input/output) interface 118.
• processor(s) 102, memory 104, and communications circuitry 106 of sleep score assessment system 120 are, in one embodiment, substantially similar to processor(s) 102, memory 104, and communications circuitry 106 of user device 110, and the previous description may apply.
• I/O interface 1 18 is capable of corresponding to any suitable input and/or output component such as, and without limitation, one or more microphones or other audio input devices, one or more speakers or other audio output devices, one or more input mechanisms (e.g., buttons, knobs, switches, etc.), one or more cameras or other image capture devices, and/or one or more display screens.
• sleep score assessment system 120 may include a display screen of any suitable size and/or shape.
• Various types of displays include, but are not limited to, liquid crystal displays (“LCD”), monochrome displays, color graphics adapter (“CGA”) displays, enhanced graphics adapter (“EGA”) displays, variable graphics array (“VGA”) display, or any other type of display, or any combination thereof.
• I/O interface 108 further is capable of including a touch screen, such as a touch screen including capacitive sensing panels capable of recognizing touch inputs thereon.
• a touch screen may correspond to a projected capacitive touch ("PCT"), screen include one or more row traces and/or driving line traces, as well as one or more column traces and/or sensing lines.
• PCT projected capacitive touch
• memory 104 includes instructions for determining a sleep score of user 170 for a particular sleep session.
• the sleep score is capable of being determined based on one or more sets of instructions or rules stored within memory 104 such as rules/instructions associated with one or more sleep score deductions 1 14 and/or rules/instructions associated with one or more sleep score interventions.
• Sleep score assessment system 120 is configured to receive the user data associated with a sleep session and determine one or more sleep metrics associated with the first sleep session. For example, sleep architecture, sleep continuity, a sleep onset, and/or a sleep wakeup time are all exemplary types of metrics capable of being determined for a particular sleep session.
• the sleep architecture for instance, includes duration of each sleep stage (e.g., N3, N2, Nl , REM), a latency of each sleep stage, and/or a survival curve of each sleep stage.
• sleep score assessment system 120 is configured, in one embodiment, to determine a type and amount of sleep score deductions user the rules/instructions associated with sleep score deductions 1 14.
• one or more reference sleep metrics are employed and compared with the sleep metrics determined using the user data.
• the reference sleep metrics are obtained from user history database 140 and/or reference database 150.
• User history database 140 stores information associated with a user's sleep history. For example, user history database 140 may store information indicating a time or times of sleep onset for a particular user, a time or times of sleep wakeup, and the like. In a non-limiting embodiment, historical sleep information for a particular user is stored by user history database 140, and is accessible by sleep score assessment system 120 via network 160.
• Reference database 150 stores information associated with referential data pertaining to user 170. For example, reference database 150 may store sleep information associated with an age of user 170, a gender of user 170, a chronotype of user 170, and the like. In a non-limiting embodiment, reference sleep information associated with user 170 is storedby reference database 150, and is accessible by sleep score assessment system 120 via network 160.
• sleep score assessment system 120 is configured to determine a number of deductions associated with a particular sleep session, and an amount (e.g., a severity/magnitude) of each deduction. To do this, as described in greater detail below, a comparison between the sleep metrics associated with a particular sleep session and the reference sleep metrics (e.g., user history sleep information, reference sleep information) that have previously been obtained may be performed. Sleep score assessment system 120 is configured to use these comparisons to determine one or more immediate values related to the first sleep session. For example, the immediate values may correspond to one or more deductions to be applied to a sleep score.
• the reference sleep metrics e.g., user history sleep information, reference sleep information
• the deductions correspond to one or more of a total sleep time, a total time spent away between a sleep onset time and a sleep offset time (e.g., a wake after sleep onset ("WASO"), a number of sleep disruptions that occurred during a sleep session (e.g., a period of wakefulness exceeding a threshold amount of time between sleep onset and sleep offset), a sleep routine that depends on a regularity of sleep onset and wakeup times, a sleep onset latency (e.g., a duration of time to sleep onset), and a deduction based on N3, also referred to as deep sleep.
• a sleep onset time e.g., a wake after sleep onset
• a sleep routine that depends on a regularity of sleep onset and wakeup times
• a sleep onset latency e.g., a duration of time to sleep onset
• N3 also referred to as deep sleep.
• sleep score assessment system 120 starts with an initial sleep session score value. For each deduction identified within a particular sleep session, the deduction is added to the initial sleep session score value. In one embodiment, the deductions are negative in value (e.g., ⁇ 0), however persons of ordinary skill in the art will recognize that this is merely exemplary. For example, each deduction may, alternatively, be subtracted from the initial sleep session score value. In this particular scenario, the deductions are positive in value (e.g., > 0). After applying (e.g., adding/subtracting) each deduction to the initial sleep session score value, a new sleep session score value is obtained. In one embodiment, the new sleep session score value is capable of being provided to user 170 via I/O interface 1 18.
• sleep score assessment system 120 is capable of causing the new sleep session score value to be presented on a graphical user interface via the display device.
• the sleep metrics associated with the sleep session are also presented such that user 170 is capable of viewing what factors caused his/her sleep session score to be a particular value. For example, user 170 may view a number of times that he/she was awake, which may have caused the sleep session score value to be the determined value.
• the new sleep session score value is presented via I/O interface 1 18 of sleep score system 120, this is merely exemplary as the sleep session score value may alternatively be provided to user device 110, or any other device, for consumption by user 170.
• system 100 further includes one or more intervention devices 130.
• intervention devices 130 includes processor(s) 102, memory 104, and communications circuitry 106, which are substantially similar to processors 102, memory 104, and communications circuitry 106 of user device 110, and the previous descriptions apply.
• each intervention device 130 includes one or more intervention output components 112.
• Intervention output component(s) 112 in one embodiment, correspond to any suitable sleep intervention component including, but not limited to, one or more auditory stimulation devices, one or more visual stimulation devices, one or more haptic stimulation devices, one or more closed-loop sleep induction devices, one or more smart wakeup devices, and the like.
• intervention output component 1 12 corresponds to a thermal connection component capable of adjusting a temperature of a local environment with which user 170 is located.
• intervention output component 1 12 is capable of adjusting a thermostat in a user's household.
• intervention devices 130 are configured to include two or more intervention output components 1 12 and although only a single intervention device 130 is displayed within system 110, two or more intervention devices may be included. Further still, intervention device(s) 130 are capable of communicating with one or more of user device 1 10 and/or sleep score assessment system 120 via network 160.
• one or more sleep interventions that occurred via intervention device(s) 130 during a sleep session are determined based on the user data that has been obtained. For instance, and as described in greater detail below, if one or more sleep interventions have occurred during the first sleep session, then the user data obtained by sensor(s) 108 may indicate this and therefore sleep score assessment system 120 may identify the intervention(s) within the user data. In one embodiment, intervention device(s) 130 are also capable of communicating the particular intervention that was performed to sleep score assessment system 120 via network(s) 160.
• the various types of interventions that may be performed include, but are not limited to, one or more audible tones provided during the sleep session, one or more visual signals provided during the sleep session, one or more haptic signals provided during the sleep session, and/or one or more environmental interventions.
• the audible tones may be output by an auditory stimulation device (e.g., an audible alarm clock, music player, etc.)
• the visual signals may be output by a visual stimulation device (e.g., white light-emitting devices, warm/calm light-emitting devices, etc.)
• the haptic signals may be output by a haptic stimulation device (e.g., vibration plate, a wearable tactile stimulator, etc.)
• the environmental interventions may be provided by an environmental control device.
• sleep score assessment system 120 may be configured to use the rules/instructions associated with sleep score interventions 1 16 to determine a number of sleep interventions and a magnitude/amount of those sleep interventions.
• the interventions are applied to improve an aspect, or aspects, of sleep. For example, an auditory stimulation may enhance deep sleep, and a sleep induction intervention shortens sleep onset latency.
• Each of the sleep interventions may be applied to the initial sleep session score value (or the sleep session score value including the deductions, as described above), to generate a new sleep session score value.
• the sleep interventions may be represented as a bonus applied to an initial sleep score to increase the sleep session score value in a manner akin to the sleep deductions being applied to the initial sleep score to decrease the sleep session score value.
• each intervention may be added to the sleep session score to obtain the new sleep session score value.
• a value associated with each sleep intervention is positive, such that the interventions contribute positively to the sleep session score value.
• sleep interventions may contribute positively or negatively to the sleep session score, and the aforementioned is merely exemplary.
• the sleep session score value is capable of being determined where:
• Initial Sleep Score corresponds to an exemplary initial sleep session score value, such as the number 100
• ⁇ Sleep Deductions corresponds to a sum of all of the sleep deduction values (e.g., where each deduction is negative in value)
• ⁇ Sleep_Interventions corresponds to a sum of all of the sleep intervention values (e.g., where each intervention is positive in value).
• sleep score assessment system 120 may, in one embodiment, include one or more of sensor(s) 1 18 and/or intervention output(s) 1 12.
• user history database 140 and/or reference database 150 may be stored locally within memory 104 of sleep score assessment system 120.
• FIG. 2 is an illustrative graph 200 of a technique for determining a total sleep time deduction, in accordance with various embodiments.
• a total sleep time (“TST") corresponds to an amount of time that user activity, detected by sensor(s) 108, is determined to be sleep. If the TST is greater than (or equal to) a predefined threshold value for TST, then sleep score assessment system 120 is configured to apply a deduction equal to zero. For example, if a TST for a sleep session is determined to be greater than 8 hours in duration, then an amount of deductions for TST may be zero. This parameter may be configurable by user 170 and/or system 120.
• sleep score assessment system 120 is configured to apply a particular deduction.
• This deduction for instance, is associated with a total duration of the TST and thus is configured such that, depending on the total duration of the TST, the deduction differs.
• the second deduction in one embodiment, is linearly related to the TST duration, as described in greater detail below with reference to Equation 2.
• TST deduction If a TST duration is greater than or equal to second threshold 204 but less than a third threshold 206 (e.g., TSTi ong ), then a third deduction is to be applied.
• the third deduction similarly to the second deduction, is linearly related to the TST duration, as described in greater detail with reference to Equation 2.
• a fourth deduction Diong is to be applied.
• each of threshold 202, 204, and 206 may be configurable depending on the particular user, and furthermore may be updated overtime based on a user history, and the aforementioned and below are merely illustrative example.
• Table 1 is a table illustrating exemplary values for TSTshort, TSTD, TSTi ong . Dshort, DTST, and Di ong , in accordance with various embodiments. Persons of ordinary skill in the art will recognize that these values are merely illustrative, and may be configured by user 170 and/or system 120.
• FIG. 3 is an illustrative graph 300 of a technique for determining a total time spent awake deduction, in accordance with various embodiments.
• a total amount of time that an individual is awake after sleep onset (“WASO") is, in one embodiment, another measure with which deductions are capable of being applied to a sleep session score.
• WASO includes not only time awake, but also micro-arousals, and more generally any amount of time that a user spends awake (e.g., user activity detected by sensor(s) 108 exceeding a threshold value) after sleep onset is identified.
• a deduction based on WASO depends on an age of user 170, as seen below with reference to Table 2.
• the age of user 170 indicates a reference value, WASOref, which corresponds to a maximum WASO duration before deductions are to be applied.
• the deduction is maximal a detected WASO exceeds WASOref by a threshold amount DEVWASO.
• WASO and DEVWASO are defined in minutes, however persons of ordinary skill in the art will recognize that this is merely exemplary. Table 2.
• a deduction of zero is applied if the WASO amount of time is less than a first threshold 302, indicated by WASC . If, the WASO amount of time is greater than a second threshold 304, then a largest deduction -DWASO is applied. In one embodiment, if the WASO amount of time is less than second threshold 304 but greater than threshold 302, then Equation 3 is applied to determine a deduction value to be used.
• FIG. 4 is an illustrative graph 400 of a deduction capable of being applied to determining a sleep session score value based on a number of sleep disruptions detected, in accordance with various embodiments.
• a sleep disruption is characterized as a period of wakefulness between a sleep on-set and a sleep off-set that is at least a certain amount of time in duration. For example, a period of wakefulness in excess of five minutes may correspond to a sleep disruption.
• deductions are applied based on a number of sleep disruptions that occur during a sleep session.
• a largest amount of deduction e.g., -DINT
• a threshold number of disruptions 402 e.g., MINT
• a deduction related to the number of disruptions is determined using Equation 4 and as seen by graph 400.
• FIG. 5 is an illustrative graph 500 of a deduction capable of being applied to determining a sleep session score value based on a sleep routine of an individual, in accordance with various embodiments.
• a deduction based on user sleep regularity is applied. Regularity, for instance, is driven primarily by two factors: a time when a user goes to sleep (e.g., bedtime regularity), and a time when that user wakes up (wakeup time regularity).
• Bedtime regularity is, in one embodiment, approximated by a regularity with regard to a detected sleep intention time.
• Sleep intention time for instance, may be determined based on a difference between a detected sleep intention time and an average sleep intention time based all sleep sessions having a duration in excess of a first threshold in the last dH-days. For example, any sleep session occurring in the last 7 days that is at least two hours long may be used to calculate a detected sleep intention time.
• this sleep information is stored by user history database 140, and is accessible by sleep score assessment system 120 for determining the sleep intention time.
• the sleep intention is input by user 170 using user device 1 10 and/or sleep score assessment system 120.
• user 170 may input a sleep intention time for a particular sleep session into a mobile application running on sleep score assessment system 120.
• Graph 500 illustrates one example use of user history to determine a sleep intention time.
• SIt-j corresponds to a time stamp of a sleep intent for a j-th sleep session
• Sit corresponds to a time stamp of a current sleep session
• Wt-j corresponds to a time stamp of a wakeup time associated with the j-th sleep session.
• user data obtained from sensor(s) 108 may include temporal metadata indicating times associated with that user activity. After determining when sleep begins using the user data, a corresponding time associated with that sleep onset may be obtained from the sleep metadata associated with the user data.
• SIt-j is measured in minutes, however persons of ordinary skill in the art will recognize that this is merely exemplary. Sleep wakeup time Wt-j, therefore, is determined using Equation 5 :
• Equation 5 SOL corresponds to a sleep onset latency, which is described in greater detail below.
• a history span in one embodiment, to be used for determining an amount of user history sleep data to obtain from user history database 140 is described by Equation 6.
• SIt-j 3 ⁇ 4.
• the deviation from average is determined using a time stamp difference ASt-j with respect to a reference time (e.g., midnight), as seen below with reference to Equation 7, and using a deviation from the mean BRt, as seen below with reference to Equation 8.
• FIG. 6 is an illustrative graph 600 illustrating how deductions are related to a difference in bedtime regularity BRt, in accordance with various embodiments.
• a difference BRt is between a first threshold 602 (e.g., -TBI) and a second threshold 604 (e.g., +TBI)
• a difference between BRt is greater than or equal to a third threshold 606 or a fourth threshold 608 (e.g., ⁇ TB 2 )
• a maximum deduction -DREG is applied.
• BR t is between TBI and TB 2
• an amount of deduction to be applied is linearly related to BRt, for instance, as illustrated by Equation 10.
• Wakeup time regularity is determined, in one embodiment, using a similar technique as that of bedtime regularity. For example, a wakeup time difference is calculated using Equations 1 1 and 12:
• a difference WRt is between a first threshold 610 (e.g., -Twi) and a second threshold 612 (e.g., +Twi), then no deductions are applied.
• a highest deduction -DREG is applied if the difference is longer than a third threshold 616 (e.g., -Twi), or a fourth threshold 618 (e.g., +Tw 2 ).
• the deduction is linearly scaled with WRt, as described by Equation 13.
• Table 3 illustrates various example parameters that may be used for determining one or more of wakeup regularity deductions and bedtime regularity deductions.
• FIG. 7 is an illustrative graph 700 of a deduction capable of being applied to determining a sleep session score value based on sleep onset latency, in accordance with various embodiments.
• Sleep onset latency refers to an amount of time with which it takes for a user to fall asleep.
• a deduction associated with SOL in one embodiment, depends on an age range associated with user 170. For instance, various age-based SOL deductions are stored by reference database 150, and these values are accessible by sleep score assessment system 120 to determine a sleep score for a user's sleep session.
• the age range indicates a maximum SOL deduction 702 (e.g., SOLref), capable of being applied when a sleep session score value is being determined.
• SOL maximum SOL deduction
• a threshold 704 e.g., SOL re f plus an amount DEVSOL
• the maximum deduction -DSOL is applied. If the SOL is less than second threshold 704, but greater than first threshold 702, then the deduction is, in one embodiment, linearly related to the SOL detected, as illustrated by Equation 15.
• Table 4 includes various exemplary values that are applicable for SOL re f depending on an age of a user.
• reference database 150 stores values for SOLref included within Table 4.
• Table 5 includes various exemplary values for DSOL and DEVSOL, which, in one embodiment, are applied by sleep score assessment system 120 to determine a sleep score for a particular sleep session.
• FIG. 8 is an illustrative graph 800 of a deduction capable of being applied to determining a sleep session score value based on a duration of N3 sleep, in accordance with various embodiments.
• the deduction associated with N3 sleep duration also referred to as a duration of deep sleep or slow-wave sleep, considers the recuperative value of sleep as being primarily driven by the presence of deep sleep. If, during a given sleep session, the duration of deep sleep does not meet an age matched reference threshold 802 (e.g., N3 re f), as seen below by Table 6, a deduction is applied.
• the deduction is linearly related, in one embodiment, to a duration of the N3 deep sleep for that particular sleep session.
• DN3 corresponds to an N3 sleep duration of approximately 0 minutes. If, however, the detected duration of N3 sleep is greater than or equal to age matched reference threshold 802, no deductions are applied.
• the N3 based deductions are indicated below with reference to Equation 16. In Equation 10, N3 is the detected duration of N3 sleep and the default values for the parameters N3 re f and DN3 are listed in Table 6 and Table 7, respectively.
• a rescaling of the deductions needs to be applied when 100 + ⁇ deduction ⁇ M.
• the rescaling formulas ensure that the sleep session score value is at least equal to M, as well as ensuring that the sums of the deductions consistently add up, as described by Equation 1 1.
• the deduction values after rescaling are referred to using the suffix "resc”.
• FIG. 9 is an illustrative diagram of an exemplary graphical user interface 900 including a sleep session score and the various deductions capable of being applied, in accordance with various embodiments.
• graphical user interface 900 which is capable of being displayed by I/O interface 1 18 of sleep score assessment system 120, and/or any other suitable display device associated with sleep score assessment system 120, user device 1 10, and/or intervention device(s) 130, includes an initial sleep session score value 902, with which the deductions described above are capable of being applied to.
• the initial sleep session score value may be set as 100.
• graphical user interface 900 further includes a total sleep time deduction 904, a sleep routine deduction 906, a time to fall asleep deduction 908, a time spent awake deduction 910, a number of sleep interruptions deduction 912, and a duration of N3 sleep deduction 914.
• each of deductions 904-914 are less than or equal to zero in value such that, when applied to initial sleep session score value 802, they reduce an amount of the sleep score.
• a total base score 916 is determine by adding each deduction 904-914 to initial sleep session score value 902.
• one or more bonus values stemming from one or more sleep interventions, are also applied to the base/initial sleep session score value 902/ 818.
• the sleep interventions as described in greater detail below, positively impact a sleep session's score. For example, if deductions 904-914 are negative in that they reduce a sleep session score value, the bonus(es) 918 is/are positive such that they increase the sleep session score value.
• a total sleep session score value 920 accounting for any deductions and/or bonus identified for a particular user's sleep session, is then determined.
• graphical user interface 900 is displayed in a list type manner within FIG. 9, this is merely exemplary as alternative and/or additional display formats may be employed such as, but not limited to, graphs, tables, and/or any other suitable display mechanism, or any combination thereof.
• FIGS. lOA-C are illustrative graphs 1002-1006 of a cumulative slow wave activity ("CSWA") parameter for different age ranges, in accordance with various embodiments.
• interventions are capable of being provided to user 170 using intervention device(s) 130.
• audible tones are capable of being provided using an auditory stimulation device.
• EEG based detected non- rapid eye movement (“NREM”) sleep such audible tones are capable of being provided to enhance a restorative value of an individual's sleep by increasing an amplitude and amount of sleep slow waves, also referred to as delta waves, while not disturbing sleep.
• NREM non- rapid eye movement
• An amplitude and number of sleep slow waves correspond, in one embodiment, to slow wave activity ("SWA"), which corresponds to EEG power in the 0.5-4.0 Hz frequency band.
• SWA slow wave activity
• a SWA accumulated over NREM sleep is useful, for example to quantify a restorative value of sleep that is provided.
• a number of tones delivered, a ratio of detected NREM sleep/reference NREM sleep, and a ratio of the CSWA to a reference CSWA are all obtained.
• the ratio of detected NREM sleep to the reference NREM sleep, denoted by PNREM, is determined using age based values stored by reference database 150 and detailed below in Table 9.
• the ratio of CSWA to reference CSWA is determined by dividing the CSWA associated with a current sleep session by the CSWA accumulated up during the duration of the detected NREM for an age group associated with user 170.
• various example reference curves are shown for different age ranges.
• graph 1002 corresponds to CSWA values for an age range of 20 to 30 year old individuals
• graph 1004 corresponds to CSWA values for an age range of 30 to 40 year old individuals
• graph 1006 corresponds to CSWA values for an age range of 40 to 60 year old individuals.
• the information associated with graphs 1002, 1004, and 1006 are capable of being stored by reference database 150 and accessed by sleep score assessment system 120 for determining a sleep score for user 170.
• FIG. 1 1 is an illustrative graph 1 100 of an amount of bonus capable of being applied for a sleep intervention, in accordance with various embodiments.
• a product of the ratios PNREM and pswA is determined, and used within Equation 17 to determine an amount of bonus to be applied to the sleep session score value.
• Bonus is a product of the ratios PNREM and pswA.
• T c corresponds to a critical number of tones that need to be provided by intervention device 130 (e.g., an auditory stimulation device) in order to have a bonus be applied to the sleep score for a user's sleep session.
• B corresponds to a maximum amount of bonus that is allowed to be applied to the sleep score.
• the value of B in one embodiment, corresponds to a value less than or equal to a sum of the deductions such that the final sleep session score does not exceed the initial sleep session score.
• feedback provided by user 170 is also capable of being used to adjust an amount and magnitude of the deductions applied, as well as adjust an amount and magnitude of the bonuses applied.
• user 170 may be provided with questions regarding aspects of their sleep session(s), which maybe harnessed by sleep score assessment system 120 when determine subsequent sleep session scores.
• user 170 may be asked to provide daily activity tracking information to be used to determine biometric aspects associated with user 170 that may contribute to one or more aspects of his/her sleep.
• sleep score assessment system 120 may be prompted to ask one or more questions (e.g., via a graphical user interface displayed by a display screen), the answers of which may be used to adjust and/or calibrate the deduction parameters and/or the bonus parameters.
• FIG. 12 is an illustrative flowchart 1200 of an exemplary process for generating a first sleep session score value, in accordance with various embodiments.
• process 1200 begins at operation 1202.
• first user data associated with a first sleep session is received.
• first user data obtained by sensors 108 is obtained by user device 1 10, and the first user data is provided to sleep score assessment system 120 via network(s) 160.
• sleep score assessment system 120 may include some or all of the functionality of sleep score assessment system 120, or sleep score assessment system 120 may include one or more sensors 108, and the use of one device to capture the user data and another device to process the data is merely exemplary.
• the first user data corresponds to data captured by sensor(s) 108 during a first sleep session of user 170.
• sensor(s) 108 may monitor user activity during a period of time, which may include some periods of time associated with user 170 being asleep, and/or may include some periods time associated with user 170 being awake.
• one or more first sleep metrics is/are determined.
• the sleep metrics are associated with the first sleep session.
• a sleep metric corresponds to a deduction that is capable of being applied to determine a sleep score.
• the sleep metrics may correspond to deductions associated with a total sleep time, a total time spent away between a sleep onset time and a sleep offset time, a number of sleep disruptions that occurred during a sleep session, a sleep routine that depends on a regularity of sleep onset and wakeup times, a sleep onset latency, and/or a duration of N3 sleep.
• the first sleep metrics are determined by sleep score assessment system 120 based on rules/instructions associated with sleep deductions 114 stored in memory 104, which are executed by processor(s) 102.
• data related to the sleep intervention(s) applied during a sleep session are stored such that sleep score assessment system 120 may analyze them.
• data related to a sensory stimulation delivered during a sleep session may be stored by sleep score assessment system 120.
• a number of tones provided by an auditory stimulation device e.g., intervention device 130
• data related to audio/visual stimulation and/or electric/magnetic stimulation to facilitate sleep may also be tracked, as well as wake -up alarms.
• one or more reference sleep metrics is/are determined.
• the sleep metrics are, in one embodiment, based on prior user data obtained from one or more prior sleep sessions.
• prior user data associated with prior sleep sessions may be stored by user history database 140.
• sleep score assessment system 120 may request prior user data from user history database 140, which in turn may provide sleep score assessment system 120 with the prior user data.
• the reference sleep metrics are alternatively stored by user history database 140, and therefore sleep score assessment system 120 is then provided with the reference sleep metric(s) in response to providing a request to user history database 140.
• the reference sleep metrics are associated with dH days prior to a current sleep session of user 170, as described in greater detail above with reference to FIG. 5.
• one or more immediate values related to the first sleep session is/are determined using the first sleep metric(s) and the reference sleep metric(s).
• the immediate values are determined based on a comparison of the sleep metrics with the reference sleep metrics. For instance, a total time of sleep ("TST") deduction is determined based on a comparison between a current TST for the first sleep session and one or more thresholds 202, 204, and 206. As an illustrative example, if the TST for the first sleep session is less than second threshold 204 and greater than first threshold 202, then the TST deduction is linearly related to the TST for the first sleep session, as seen in greater detail with reference to FIG. 2.
• a wake after sleep onset (“WASO”) deduction is determined based on a total WASO for the first sleep session. If the WASO for the first sleep session is greater than a first threshold 302, identified by an age associated with user 170, and stored by reference database 150, while also being less than a second threshold 304, then the WASO deduction is linearly related to the WASO, as described above with reference to FIG. 3.
• a first threshold 302 identified by an age associated with user 170, and stored by reference database 150, while also being less than a second threshold 304.
• a first sleep session score value is generated.
• the first sleep session score value is generated based on the immediate values related to the first sleep session. For instance, the deductions identified for the first sleep session are summed and applied to an initial sleep score value. As an illustrative example, an initial sleep score value is determined (e.g., the value 100), and each deduction determined during the first sleep session is applied to the initial sleep score value.
• an initial sleep score value is determined (e.g., the value 100), and each deduction determined during the first sleep session is applied to the initial sleep score value.
• the first sleep session score, the user data, the sleep metrics, and/or the reference sleep metrics are capable of being stored by user history database 140.
• user history database 140 is capable of updating the reference sleep metrics to include a recently obtained sleep metric, or metrics, such that the previous sleep history (e.g., dii) includes the recent sleep session information.
• This allows for the reference sleep metrics to be updated to include the most recent values, and therefore be dynamically reconfigurable based on newly available data.
• this allows system 100 to learn how user 170 behaves over time, and update its models (e.g., instructions 114 and/or 116) based on recent metrics of user 170.
• the first sleep session score value and the first sleep metric(s) are presented on a graphical user interface.
• the first sleep session score value and the first sleep metric(s) are displayed via a display device (e.g., I/O interface 1 18) of sleep score assessment system 120, however a graphical user interface including the first sleep session score value and the first sleep metric(s) alternatively are capable of being displayed on any suitable display device.
• graphical user interface 900 is displayed via a display screen (e.g., I/O interface 118), and includes a sleep session score value 918, as well as deductions 904-912, which are used to determine sleep session score value 918.
• additional user data representing at least one sleep metric associated with the first sleep session is also received.
• the additional user data is used to determine at least one sleep intervention that occurred during the first sleep session.
• sleep interventions such as one or more audible tones provided during the sleep session by an auditory stimulation device (e.g., intervention device 130 where intervention output component 1 12 corresponds to an auditory output device), one or more visual signals provided during the sleep session by a visual stimulation device (e.g., intervention device 130 where intervention output component 112 corresponds to a visual output component), and/or one or more environment interventions provided by an environment control device (e.g., intervention device 130 where intervention output component 1 12 corresponds to an environment output component), may have been provided.
• an auditory stimulation device e.g., intervention device 130 where intervention output component 1 12 corresponds to an auditory output device
• visual signals e.g., intervention device 130 where intervention output component 112 corresponds to a visual output component
• environment interventions provided by an environment control device (e.g., intervention device 130 where intervention output component
• sleep score assessment system 120 is configured to apply a second amount of measurements, such as a bonus, to the sleep score value.
• a second amount of measurements such as a bonus
• the second amount of measurements is applied to an initial sleep score value, however persons of ordinary skill in the art will recognize that the measurements alternatively can be applied to the first sleep session score value.
• FIG. 13 is an illustrative flowchart 1300 of an exemplary process for determining a sleep intervention to be provided and sending an instruction for that sleep intervention to be provided at a particular time, in accordance with various embodiments.
• process 1300 begins at operation 1302.
• a first sleep session score threshold value is received.
• sleep session threshold values are capable of being stored by user history database 140 and/or reference database 150.
• the sleep session score threshold value is a parameter than indicates a quality of a sleep session score value.
• a sleep session score value exceeds the sleep session score threshold value, then that particular sleep session may be classified as being "good” sleep, whereas sleep session score values less than the sleep session score threshold value may be classified as being “poor” sleep.
• the particular value attributed to the threshold is capable of being set by user 170 and/or sleep score assessment system 120, and alternatively, the value is capable of being determined based on a history of sleep sessions score values of a user. For example, an average sleep session score value for a particular amount of time (e.g., one week, one month, etc.) maybe used as the sleep session score threshold value, however persons of ordinary skill in the art will recognize that this is merely exemplary.
• a first intervention to be provided to improve a subsequent sleep session is determined.
• the intervention that is determined is based, at least in part, on the first sleep session score value that was generated and/or the first sleep sessions score threshold value. For example, if the sleep session score value for a sleep session is determined to be less than the sleep session score threshold value, then sleep score assessment system 120 may determine that an intervention is needed.
• the particular type of intervention e.g., audible tones, visual signals, environment controls, etc.
• a sleep session score value may indicate that a cooler temperature in the environment where the user sleeps would likely produce a higher sleep session score, and therefore may indicate that an environmental control intervention is to be selected.
• a sleep intervention device capable of providing the first intervention is determined.
• the specific sleep intervention device 130 that is determined is based on, amongst other factors, the type of sleep intervention to be provided. For example, if audible tones are to be provided then an auditory stimulation device is to be determined as the intervention device 130 to provide the intervention. As another example, if a change of a temperature is to be provided, then an environmental control device may be determined as the intervention device 130.
• a first instruction for the first intervention to be performed at a first time is generated.
• the first instruction for instance, is generated by sleep score assessment system 120.
• the particular time, or temporal window, with which the instruction is to be performed may be based on the user data associated with the first sleep session. For example, if sleep score assessment system 120 determines that the intervention is to be provided at a particular time after sleep onset, then the instruction that is generated thereby may indicate that the time to perform the intervention should likely occur at a particular time, or during a particular temporal window, after a subsequent sleep session's sleep onset is detected.
• the instruction may be generated such that it causes the sleep intervention device (e.g., intervention device 130) to adjust the temperature in the local environment (e.g., by adjusting the temperature settings of a thermostat or other environmental control device) prior to a sleep onset time of user 170 based on a prior sleep session's sleep onset time and/or an average sleep onset time for user 170.
• the first instruction is sent to the sleep intervention device such that the intervention is performed at prior to the prescribed time with which the intervention is to occur.
• FIG. 14 is an illustrative graph 1400 of a deduction capable of being applied for determining a sleep session score value based on adjacent sleep sessions grouped together, in accordance with various embodiments.
• Graph 1400 includes N sleep sessions being consolidated together. The condition for grouping two or more adjacent sleep sessions, in one embodiment, that a timestamp of a sleep intent time of the "n+1 " session SI n +i minus a timestamp of a wake time of the "n" session W n is smaller than At.
• Equation 18 the number of interruptions, as described by Equation 21 , the detected N3 sleep, as described by Equation 22, the slow wave activity, as described by Equation 23, and the number of tones, as described by Equation 24.
• the minimum across all grouped sleep sessions is used, as described by Equation 20.
• the wake after sleep onset duration is obtained by adding the WASO from grouped sleep sessions and adding the interval between wake and sleep onset of adjacent sleep sessions, as described by Equation 19.
• a threshold ⁇ ( ⁇ ) is used, in one embodiment, to limit the amount of WASO added for contiguous consolidated sleep sessions. Periods of wakefulness in between grouped sessions also may contribute to WASO, but these may not contribute to the number of interruptions in order to prevent double deductions being applied.
• the grouping of adjacent sleep sessions in one embodiment, considers sleep sessions of a same nature (i.e. either baseline or therapy). This means that a baseline sleep session and a therapy sleep session should not be grouped together even if they are separated by an amount of time less than the threshold At.
• any reference signs placed between parentheses shall not be construed as limiting the claim.
• the word “comprising” or “including” does not exclude the presence of elements or steps other than those listed in a claim.
• several of these means may be embodied by one and the same item of hardware.
• the word "a” or “an” preceding an element does not exclude the presence of a plurality of such elements.
• any device claim enumerating several means several of these means may be embodied by one and the same item of hardware.
• the mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination.

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