Techniques for providing physiological state-related insights associated with users

Abstract

A method, system, and device for providing user-associated physiological state insights is described. The method includes the steps of: receiving user-associated physiological data from a wearable device; obtaining user-associated physiological baselines based on user-associated baseline physiological data; receiving additional user-associated physiological data from a wearable device; obtaining current user arousal data based on the additional user-associated physiological data; identifying trigger conditions for providing user-associated physiological state insights related to vehicle-involved travel events based on a comparison between current user arousal data and baseline user arousal data; and causing a user device to provide user-associated physiological state insights.

Description

Technical Field

[0001] The following relates to wearable devices and data processing, including techniques for providing insights related to physiological states associated with a user.

Background Art

[0002] Some wearable devices may be configured to collect data from a user associated with activities such as exercise, sleep, etc. The collected data may provide further information regarding the user's level of arousal. For example, the user may have been sleep-deprived in the recent few days, which may affect the user's level of arousal and, in turn, the actions the user may take.

Summary of the Invention

[0003] This summary is provided to introduce, in simplified form, a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

[0004] According to a first embodiment, a method for providing user-associated physiological state-related insights includes: receiving user-associated baseline physiological data from at least one wearable device; obtaining user-associated physiological baselines at least in part on the user-associated baseline physiological data, wherein the physiological baselines provide reference user arousal data; receiving user-associated additional physiological data from at least one wearable device; obtaining current user arousal data at least in part on the user-associated additional physiological data; identifying trigger conditions for providing user-associated physiological state-related insights related to a travel event involving at least one vehicle, at least in part on a comparison between the current user arousal data and reference user arousal data; and causing a user device to provide user-associated physiological state-related insights.

[0005] In an exemplary embodiment of the first aspect, the step of identifying a trigger condition further includes identifying a trigger condition when the current user arousal data differs from reference user arousal data by a predetermined threshold amount.

[0006] In an exemplary embodiment of the first aspect, the movement event is a future movement event.

[0007] In an exemplary embodiment of the first aspect, the movement event is the current movement event.

[0008] In an exemplary embodiment of the first aspect, the physiological data includes acceleration sensor data, and the method further includes the steps of identifying that a user is driving at least one vehicle, at least in part on a comparison of acceleration sensor data with reference acceleration sensor data, and detecting a movement event, at least in part on the comparison.

[0009] In an exemplary embodiment of the first aspect, the method further includes the steps of establishing an active local communication link between a user device and at least one vehicle, and detecting a movement event, at least in part, based on the presence of the active local communication link between the user device and at least one vehicle.

[0010] In an exemplary embodiment of the first aspect, the method further includes the steps of receiving satellite positioning data from a user device and detecting a movement event based at least in part on the satellite positioning data from the user device.

[0011] In an exemplary embodiment of the first aspect, the method further includes the step of sending a command to at least one wearable device to apply a predetermined rate for transmitting physiological data in response to the detection of a movement event.

[0012] In an exemplary embodiment of the first aspect, the step of causing a user device to provide physiological state-related insights associated with the user includes transmitting the physiological state-related insights to at least one vehicle via an active local communication link between the user device and at least one vehicle.

[0013] In an exemplary embodiment of the first aspect, the step of causing a user device to provide physiological state-related insights associated with the user includes causing the user device's graphical user interface to display the physiological state-related insights.

[0014] In an exemplary embodiment of the first aspect, the step of causing a user device to provide physiological state-related insights associated with the user includes causing the user device to provide at least one of auditory, tactile, and visual alerts associated with the physiological state-related insights.

[0015] In an exemplary embodiment of the first aspect, the method includes the step of receiving user-associated vehicle data from at least one vehicle, wherein the user-associated vehicle data includes user behavior data collected during a travel event, and the step of identifying a trigger condition includes identifying a trigger condition based at least in part on the user-associated vehicle data.

[0016] In an exemplary embodiment of the first aspect, the method further includes the steps of receiving meteorological data associated with a travel event, determining, based on the meteorological data associated with the travel event, that the meteorological data associated with the travel event affects the travel event, wherein the step of identifying a trigger condition includes identifying a trigger condition based at least in part on the meteorological data.

[0017] In an exemplary embodiment of the first aspect, the method further includes the steps of receiving calendar data associated with a travel event, determining that the travel event is a future travel event based on the calendar data associated with the travel event, and identifying a trigger condition, which includes identifying a trigger condition based at least in part on the determination.

[0018] In an exemplary embodiment of the first aspect, the method further includes receiving satellite positioning data associated with a travel event from a user device, determining, based on the satellite positioning data associated with the travel event, that the travel event is related to an unfamiliar route for the user, wherein the step of identifying a trigger condition includes identifying a trigger condition based at least in part on the determination.

[0019] In an exemplary embodiment of the first aspect, the method further includes the step of receiving route schedule data associated with a travel event, and the step of identifying a trigger condition includes identifying a trigger condition based at least in part on the route schedule data.

[0020] According to a second embodiment, a device for providing user-associated physiological state-related insights comprises a processor, a memory coupled to the processor, and instructions stored in the memory and executable by the processor, the instructions causing the device to perform the following steps: receiving user-associated baseline physiological data from at least one wearable device; obtaining user-associated physiological baselines based at least in part on the user-associated baseline physiological data, wherein the physiological baselines provide reference user arousal data; receiving user-associated additional physiological data from at least one wearable device; obtaining current user arousal data based at least in part on the user-associated additional physiological data; identifying trigger conditions for providing user-associated physiological state-related insights related to a mobility event involving at least one vehicle, based at least in part on a comparison between the current user arousal data and reference user arousal data; and causing a user device to provide user-associated physiological state-related insights.

[0021] In an exemplary embodiment of the second aspect, when identifying a trigger condition, the instruction is further executable by the processor, causing the device to perform the step of identifying a trigger condition when the current user arousal data differs from reference user arousal data by a predetermined threshold amount.

[0022] In an exemplary embodiment of the second aspect, the movement event is a future movement event.

[0023] In an exemplary embodiment of the second aspect, the movement event is the current movement event.

[0024] In an exemplary embodiment of the second aspect, the additional physiological data includes accelerometer sensor data, and the instructions are further executable by a processor to cause the device to identify that the user is driving at least one vehicle based at least in part on a comparison of the accelerometer sensor data and reference accelerometer sensor data, and to detect a movement event based at least in part on the comparison.

[0025] In an exemplary embodiment of the second aspect, the instructions are further executable by a processor to cause the device to establish an active local communication link between the user device and at least one vehicle, and to detect a movement event based at least in part on the presence of the active local communication link between the user device and at least one vehicle.

[0026] In an exemplary embodiment of the second aspect, the instructions are further executable by a processor to cause the device to receive satellite positioning data from the user device, and to detect a movement event based at least in part on the satellite positioning data from the user device.

[0027] In an exemplary embodiment of the second aspect, the instructions are further executable by a processor to cause the device to send instructions to at least one wearable device to apply a predetermined rate for transmitting physiological data in response to the detection of a movement event.

[0028] In an exemplary embodiment of the second aspect, when causing the user device to provide insights related to the physiological state associated with the user, the instructions are further executable by a processor to cause the device to send the insights related to the physiological state to at least one vehicle via an active local communication link between the user device and at least one vehicle.

[0029] In an exemplary embodiment of the second aspect, when causing a user device to provide insights related to a physiological state associated with a user, the instructions are further executable by a processor, and cause the device to display the insights related to the physiological state on a graphical user interface of the user device.

[0030] In an exemplary embodiment of the second aspect, when causing a user device to provide insights related to a physiological state associated with a user, the instructions are further executable by a processor, and cause the device to provide at least one of an audible alert, a tactile alert, and a visual alert associated with the insights related to the physiological state to the user device.

[0031] In an exemplary embodiment of the second aspect, the instructions are further executable by a processor, and cause the device to receive vehicle data associated with the user from at least one vehicle, the vehicle data associated with the user includes user behavior data collected during a movement event, and the step of identifying a trigger condition includes identifying the trigger condition based at least in part on the vehicle data associated with the user.

[0032] In an exemplary embodiment of the second aspect, the instructions are further executable by a processor, and cause the device to receive weather data associated with a movement event and, based on the weather data associated with the movement event, determine that the weather data associated with the movement event affects the movement event, and the step of identifying a trigger condition for providing insights related to a physiological state associated with a user related to the movement event includes identifying the trigger condition based at least in part on the weather data.

[0033] In an exemplary embodiment of the second aspect, the instruction is further executable by the processor and causes the device to perform the steps of receiving calendar data associated with a travel event, determining that the travel event is a future travel event based on the calendar data associated with the travel event, and identifying trigger conditions for providing physiological state-related insights associated with a user related to the travel event, the steps of identifying trigger conditions, at least in part, based on that determination.

[0034] In an exemplary embodiment of the second aspect, instructions are further executable by the processor and cause the device to perform the steps of: receiving satellite positioning data associated with a travel event from a user device; determining, based on the satellite positioning data associated with the travel event, that the travel event is related to a route the user has not experienced; and identifying trigger conditions for providing physiological state-related insights associated with the user related to the travel event, which includes identifying trigger conditions, at least in part, based on that determination.

[0035] In an exemplary embodiment of the second aspect, the instruction is further executable by the processor and causes the device to perform the steps of receiving route schedule data associated with a travel event and identifying trigger conditions for providing physiological state-related insights associated with a user related to a travel event, the steps of identifying trigger conditions based at least in part on the route schedule data.

[0036] According to a third aspect, a non-temporary computer-readable medium stores code, and the code includes instructions executable by a processor to perform: receiving user-associated baseline physiological data from at least one wearable device; obtaining user-associated physiological baselines based at least in part on the user-associated baseline physiological data, wherein the physiological baselines provide reference user arousal data; receiving additional user-associated physiological data from at least one wearable device; obtaining current user arousal data based at least in part on the additional user-associated physiological data; identifying trigger conditions for providing user-associated physiological state-related insights related to a travel event involving at least one vehicle, based at least in part on a comparison between the current user arousal data and reference user arousal data; and causing a user device to provide user-associated physiological state-related insights.

[0037] According to a fourth aspect, an apparatus for providing user-associated physiological state-related insights includes means for performing the following steps: receiving user-associated baseline physiological data from at least one wearable device; obtaining user-associated physiological baselines based at least in part on the user-associated baseline physiological data, wherein the physiological baselines provide reference user arousal data; receiving user-associated additional physiological data from at least one wearable device; obtaining current user arousal data based at least in part on the user-associated additional physiological data; identifying trigger conditions for providing user-associated physiological state-related insights related to a travel event involving at least one vehicle, based at least in part on a comparison between the current user arousal data and reference user arousal data; and causing a user device to provide user-associated physiological state-related insights. [Brief explanation of the drawing]

[0038] (Brief explanation of the drawing)

[0039] [Figure 1] This disclosure provides an example of a system that supports techniques for providing physiological state-related insights associated with a user.

[0040] [Figure 2] This disclosure provides an example of a system that supports techniques for providing physiological state-related insights associated with a user.

[0041] [Figure 3] This disclosure provides an example of a system that supports techniques for providing physiological state-related insights associated with a user.

[0042] [Figure 4]A block diagram of an apparatus supporting a technique for providing physiological state-related insights associated with a user, according to aspects of this disclosure, is shown.

[0043] [Figure 5] A block diagram of a wearable application supporting techniques for providing user-associated physiological state-related insights, as described in this disclosure, is shown.

[0044] [Figure 6] The diagram shows a system including a device that supports techniques for providing physiological state-related insights associated with a user, as described in this disclosure.

[0045] [Figure 7] This disclosure provides a flowchart illustrating how to support techniques for providing physiological state-related insights associated with a user.

[0046] [Figure 8] A block diagram illustrating the relationship between measured data attributes and derived data attributes according to the aspects of this disclosure is shown. [Modes for carrying out the invention]

[0047] Wearable devices, such as wearable ring devices, may be used to collect, monitor, and track physiological data associated with the user based on sensor measurements performed by the wearable device. Examples of physiological data that may be collected by a wearable device may include body temperature data, heart rate data, photoplethysmography (PPG) data, blood oxygen saturation data, etc. Physiological data collected, monitored, and tracked via a wearable device may be used to obtain health insights about the user, such as the user's sleep patterns and activity patterns. However, health insights provided by many conventional wearable devices may not be relevant to future or current driving events involving a vehicle, for example, the user may not be able to take action based on health insights related to driving events, or, for example, share health information with the vehicle so that the vehicle can take health insights into account when monitoring the user during a driving event.

[0048] Accordingly, aspects of this disclosure relate to techniques that enable a user device to provide actionable guidance or insights regarding driving events involving a vehicle, and that enable a user to receive guidance proactively or reactively.

[0049] For example, a wearable device may acquire baseline physiological data from the user throughout the day, such as heart rate data and body temperature data. The baseline physiological data, including the measured physiological parameters, may include any physiological parameters known in the art, including daytime heart rate data (e.g., heart rate while the user is awake), nighttime heart rate data (e.g., heart rate while the user is asleep), recovery time (e.g., time spent in a relaxed state), temperature (e.g., body temperature, skin temperature), respiratory rate, blood oxygen saturation, activity / exercise, or any combination thereof. The baseline physiological data may be used to acquire a physiological baseline associated with the user, which provides reference user arousal data. Reference user arousal data may be derived from the baseline physiological data, i.e., from actual measurements taken by the wearable device, such as heart rate, skin temperature, blood oxygen saturation, activity / movement, etc.

[0050] The user device may receive additional physiological data associated with the user from at least one wearable device. For example, the additional physiological data associated with the user may be related to, for example, the most recent 24-hour, 48-hour, or 72-hour period, or any other applicable period.

[0051] The user device may obtain current user arousal data based at least in part on additional physiological data associated with the user. Current user arousal data may be derived from additional physiological data, i.e., actual measurements taken by the wearable device, such as heart rate, skin temperature, blood oxygen saturation, activity / movement, etc. This information may then be used when the user initiates a mobility event or is about to initiate one in the (near) future.

[0052] The user device may identify trigger conditions for providing physiological state-related insights associated with the user in relation to a travel event, at least in part, based on a comparison between current user arousal data and baseline user arousal data. For example, current user arousal data may indicate that the user has been sleep-deprived for the past three nights. This may cause the current user arousal data to fall below the baseline user arousal data, triggering physiological state-related insights associated with the user in relation to a travel event.

[0053] A user device may be configured to provide physiological state-related insights associated with the user. For example, the user device itself may provide physiological state-related insights associated with the user, for instance, using the user device's graphical user interface. For example, if a travel event is a future travel event, the user device may instruct the user to eat and / or sleep appropriately before the travel event. In another example, the user device may transmit physiological state-related insights associated with the user to a vehicle, such as an automobile, which may take these insights into account when monitoring the user. Thus, the vehicle may be provided with additional information about the user's current level of alertness, which may affect the user's performance while driving the vehicle, and the vehicle's driver monitoring system may take this information into account when monitoring the user.

[0054] For example, a user's current physiological state, i.e., user arousal data, may differ from their usual arousal data (i.e., be lower or weaker) due to a number of reasons, such as lack of sleep, excessive exercise, or irregular eating habits. Then, when a user initiates a travel event, or plans to initiate one soon, the user's current arousal data may not be optimal for the travel event. The solutions illustrated herein make it possible to take into account the user's current arousal data during a current travel event or before a future travel event, and to provide physiological state-related insights based on that current arousal data.

[0055] Some aspects of this disclosure relate to techniques for providing physiological state-related insights associated with a user during an ongoing travel event. For example, since the user device is known to the user's arousal data prior to the travel event, the user device may instruct the user to rest and / or eat at some point during the travel event. Some aspects of this disclosure relate to techniques for providing a vehicle with physiological state-related insights associated with a user so that the vehicle can determine an action based at least in part on the physiological state-related insights. This enables a solution in which the vehicle can receive and take into account additional information about the user's arousal data from the user device, in addition to driver monitoring data provided by the vehicle itself. Thus, a solution for providing the user with a response action (e.g., a command or warning message) is enabled by this disclosure.

[0056] Some aspects of this disclosure relate to techniques for providing physiological state-related insights associated with a user regarding future travel events. This makes it possible to provide solutions for providing physiological state-related insights, such as instructions or warning messages to the user regarding future travel events, even before the travel event has started. In other words, this makes it possible to provide solutions for providing preventative measures when the user is deemed to be overtired (e.g., "take a nap before starting the travel event" or "drink coffee before starting the travel event").

[0057] Some aspects of this disclosure relate to techniques for receiving user-related vehicle data from a vehicle. This data can then be used to identify trigger conditions for providing physiological state-related insights associated with the user. For example, a vehicle may collect various types of sensor data indicating how a user behaves while driving the car. For example, a car may detect how a user applies the brakes, how long a user has been driving, and collect lane assist data. This enables solutions in which vehicle data, in addition to physiological data collected by at least one wearable device, can also be effective in identifying trigger conditions for providing physiological state-related insights associated with the user.

[0058] Some aspects of this disclosure relate to techniques for providing physiological state-related insights through a graphical user interface of a user device. Additionally or alternatively, physiological state-related insights may be provided through auditory, tactile, and / or visual alerts (e.g., using lights). For example, an auditory or visual alert may instruct the user to take a break during a driving event. In some embodiments, tactile and / or visual alerts may be used to draw the user's attention, for example, when the user device is in the user's pocket or when the user is not otherwise paying attention to the user device. This enables a solution in which the user device can be used as a means to provide the user with physiological state-related insights.

[0059] The aspects of this disclosure will first be described in the context of a system that supports the collection of physiological data from a user via a wearable device. The aspects of this disclosure will be further illustrated and described with reference to apparatus diagrams, system diagrams, and flowcharts relating to techniques for providing physiological state-related insights associated with a user.

[0060] Figure 1 shows an example of a system 100 that supports a technique for providing user-associated physiological state-related insights, according to aspects of the present disclosure. The system 100 includes a plurality of electronic devices (e.g., wearable devices 104, user devices 106) that can be worn and / or operated by one or more users 102. The system 100 further includes a network 108 and one or more servers 110.

[0061] The electronic devices may include any electronic devices known in the art, including wearable devices 104 (e.g., ring wearable devices, watch wearable devices, etc.) and user devices 106 (e.g., smartphones, laptops, tablets). Each electronic device associated with a user 102 may include one or more of the following functions: 1) measuring physiological data, 2) storing the measured data, 3) processing the data, 4) providing output to the user 102 based on the processed data (e.g., via a GUI, auditory signals, tactile signals, visual signals), and 5) communicating data with each other and / or other computing devices. Different electronic devices may perform one or more of these functions.

[0062] An exemplary wearable device 104 may also include wearable computing devices such as a ring computing device (hereinafter, "ring") configured to be worn on the finger of user 102, a wrist computing device (e.g., a smartwatch, fitness band, or bracelet) configured to be worn on the wrist of user 102, and / or a head-mounted computing device (e.g., glasses / goggles). The wearable device 104 may also include bands, straps (e.g., flexible or non-flexible bands or straps), bands around the head (e.g., a forehead headband), arms (e.g., a forearm band and / or a biceps band), and / or legs (e.g., a thigh or calf band), stick-on sensors that can be positioned in other locations such as behind the ear or under the armpit. The wearable device 104 may also be attached to or included in clothing. For example, the wearable device 104 may be included in the pockets and / or pouches of clothing. As another example, the wearable device 104 may be clipped and / or pinned to clothing, or kept within the vicinity of the user 102. Examples of clothing include, but are not limited to, hats, shirts, gloves, trousers, socks, outerwear (e.g., jackets), and underwear. In some implementations, the wearable device 104 may be included in other types of devices, such as training / sports devices used during physical activity. For example, the wearable device 104 may be attached to or included in bicycles, skis, tennis rackets, golf clubs, and / or training weights.

[0063] Much of this disclosure can be explained in the context of the ring wearable device 104. Therefore, terms such as “ring 104” and “wearable device 104” may be used interchangeably unless otherwise specified herein. However, the use of the term “ring 104” should not be considered limiting, for it is intended herein that aspects of this disclosure may be performed using other wearable devices (e.g., watch wearable devices, necklace wearable devices, bracelet wearable devices, earring wearable devices, anklet wearable devices, etc.).

[0064] In some embodiments, the user device 106 may include handheld mobile computing devices such as smartphones and tablet computing devices. The user device 106 may also include personal computers such as laptops and desktop computing devices. Other exemplary user devices 106 may include server computing devices that can communicate with other electronic devices (e.g., via the Internet). In some implementations, the computing device may include medical devices such as external wearable computing devices (e.g., Holter monitors). The medical device may also include implantable medical devices such as pacemakers and defibrillators. Other exemplary user devices 106 may include home computing devices such as Internet of Things (IoT) devices (e.g., IoT devices), smart TVs, smart speakers, smart displays (e.g., video call displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and fitness equipment.

[0065] Several electronic devices (e.g., wearable device 104, user device 106) may measure the physiological parameters of each user 102, such as photoplethysmography waveforms, continuous skin temperature, pulse waveforms, respiratory rate, heart rate, heart rate variability (HRV), actigraphy, electrodermal response, pulse oximetry, and / or other physiological parameters. Some electronic devices that measure physiological parameters may also perform some / all of the calculations described herein. Some electronic devices may not measure physiological parameters but may perform some / all of the calculations described herein. For example, a ring (e.g., wearable device 104), a mobile device application, or a server computing device may process the received physiological data measured by other devices.

[0066] In some implementations, user 102 may operate or be associated with multiple electronic devices, some of which may measure physiological parameters, and some of which may process the measured physiological parameters. In some implementations, user 102 may have a ring (e.g., wearable device 104) for measuring physiological parameters. User 102 may also have or be associated with a user device 106 (e.g., a mobile device, smartphone), in which case the wearable device 104 and user device 106 are coupled to communicate with each other. In some cases, user device 106 may receive data from wearable device 104 and perform some / all of the calculations described herein. In some implementations, user device 106 may also measure physiological parameters described herein, such as exercise / activity parameters.

[0067] For example, as shown in Figure 1, a first user 102-a (user 1) may operate or be associated with a wearable device 104-a (e.g., a ring 104-a) and a user device 106-a, which may operate as described herein. In this example, the user device 106-a associated with user 102-a may process / store physiological parameters measured by the ring 104-a. In comparison, a second user 102-b (user 2) may be associated with a ring 104-b, a watch wearable device 104-c (e.g., a watch 104-c), and a user device 106-b, in which case the user device 106-b associated with user 102-b may process / store physiological parameters measured by the ring 104-b and / or the watch 104-c. Furthermore, the nth user 102-n (user N) may be associated with the configuration of the electronic devices described herein (e.g., ring 104-n, user device 106-n). In some embodiments, the wearable device 104 (e.g., ring 104, watch 104) and other electronic devices may be communicably coupled to the user device 106 of each user 102 via Bluetooth®, Wi-Fi, and other wireless protocols.

[0068] In some implementations, the ring 104 of system 100 (e.g., wearable device 104) may be configured to collect physiological data from each user 102 based on arterial blood flow in the user's finger. In particular, the ring 104 may utilize one or more light-emitting elements, such as LEDs (e.g., red LEDs, green LEDs), that emit light on the palmar side of the user's finger to collect physiological data based on arterial blood flow in the user's finger. Generally, the terms light-emitting elements, light-emitting elements, etc., may include, but are not limited to, LEDs, micro-LEDs, mini-LEDs, laser diodes (LDs), etc.

[0069] In some cases, the system 100 may be configured to collect physiological data from each user 102 based on blood flow diffused into the microvascular bed of the skin having capillaries and arterioles. For example, the system 100 may collect PPG data based on the measured amount of blood diffused into the microvascular system of capillaries and arterioles. In some implementations, the ring 104 may use a combination of both green and red LEDs to acquire physiological data. The physiological data may include, but are not limited to, temperature data, accelerometer data (e.g., motion / exercise data), heart rate data, HRV data, blood oxygen level data, or any combination thereof, any physiological data known in the art.

[0070] Red and green LEDs have been shown to have distinct advantages when acquiring physiological data through different parts of the body under different conditions (e.g., bright / dark, active / inactive), so using both green and red LEDs can offer several advantages over other solutions. For example, green LEDs have been shown to perform better during exercise. Furthermore, using multiple LEDs (e.g., green and red LEDs) dispersed around the ring 104 has been shown to perform better than wearable devices that utilize LEDs positioned in close proximity to each other, such as within a watch wearable device. In addition, blood vessels in the fingers (e.g., arteries, capillaries) are more easily accessible via LEDs than blood vessels in the wrist. In particular, since the arteries in the wrist are located at the bottom of the wrist (e.g., the palm side of the wrist), only the capillaries are accessible at the top of the wrist (e.g., the back of the wrist), where devices such as wearable watch devices are typically worn. Therefore, it has been found that utilizing LEDs and other sensors within the ring 104 offers superior performance compared to wearable devices worn on the wrist. This is because the ring 104 has easier access to arteries (than capillaries), resulting in stronger signals and more valuable physiological data.

[0071] The electronic devices of system 100 (e.g., user device 106, wearable device 104) may be communicatively coupled to one or more servers 110 via wired or wireless communication protocols. For example, as shown in Figure 1, the electronic device (e.g., user device 106) may be communicatively coupled to one or more servers 110 via network 108. Network 108 may implement a transport control protocol such as the Internet and the Internet Protocol (TCP / IP), or it may implement other network 108 protocols. The network connection between network 108 and each electronic device may facilitate the transfer of data via email, web, text messages, mail, or any other suitable form of interaction within the computer network 108. For example, in some implementations, a ring 104-a associated with a first user 102-a may be communicatively coupled to user device 106-a, in which case user device 106-a is communicatively coupled to server 110 via network 108. In addition or as an alternative, the wearable device 104 (e.g., ring 104, watch 104) may be coupled to the network 108 so as to be able to communicate directly with it.

[0072] System 100 may provide an on-demand database service between the user device 106 and one or more servers 110. In some cases, the server 110 may receive data from the user device 106 via the network 108, and store and analyze that data. Similarly, the server 110 may provide data to the user device 106 via the network 108. In some cases, the server 110 may be located in one or more data centers. The server 110 may be used for data storage, management, and processing. In some implementations, the server 110 may provide a web-based interface to the user device 106 via a web browser.

[0073] In some embodiments, the system 100 may detect periods in which user 102 is sleeping and classify these periods into one or more sleep stages (e.g., sleep stage classification). For example, as shown in Figure 1, user 102-a may be associated with a wearable device 104-a (e.g., a ring 104-a) and a user device 106-a. In this example, the ring 104-a may collect physiological data associated with user 102-a, including body temperature, heart rate, HRV, respiratory rate, etc. In some embodiments, the data collected by the ring 104-a may be input to a machine learning classifier, which is configured to determine periods in which user 102-a is sleeping (or has been sleeping). Furthermore, the machine learning classifier may be configured to classify periods into different sleep stages (including wakefulness, rapid eye movement (REM) sleep, light sleep (non-REM (NREM)), and deep sleep (NREM)). In some embodiments, the classified sleep stages may be displayed to user 102-a via the GUI of user device 106-a. The sleep stage classification may be used to provide user 102-a with feedback on the user's sleep patterns, such as a recommended bedtime and a recommended wake-up time. Furthermore, in some implementations, the sleep stage classification techniques described herein may be used to calculate scores for each user, such as a sleep score and a ready score. In addition, system 100 may detect, at least in part, whether user 102-a (user 1) is beginning to transition to a sleep state (e.g., deep sleep) from a set of sleep states (e.g., wakefulness, REM sleep, NREM sleep). Any component of the system 100, including a wearable device 104-a, a user device 106-a associated with user 102-a (user 1), one or more servers 110, or any combination thereof, may detect whether user 102-a (user 1) is beginning to transition to a sleep state (e.g., deep sleep) from a set of sleep states, at least in part based on collected physiological data.

[0074] In some embodiments, system 100 may further improve physiological data acquisition, data processing procedures, and other techniques described herein by utilizing features derived from circadian rhythms. The term circadian rhythm may refer to a natural internal process that regulates an individual's sleep-wake cycle, which repeats approximately every 24 hours. In this regard, the techniques described herein may improve physiological data acquisition, analysis, and data processing by utilizing a circadian rhythm adjustment model. For example, a circadian rhythm adjustment model may be input into a machine learning classifier along with physiological data collected from user 102-a via a wearable device 104-a. In this example, the circadian rhythm adjustment model may be configured to “weight” or adjust the physiological data collected over the user’s natural approximately 24-hour circadian rhythm. In some implementations, the system may initially start with a “baseline” circadian rhythm adjustment model and modify the baseline model using physiological data collected from each user 102 to generate a customized, personalized circadian rhythm adjustment model specific to each individual user 102.

[0075] In some embodiments, system 100 may utilize other biological rhythms to further improve the collection, analysis, and processing of physiological data by the phase of these other rhythms. For example, if weekly rhythms are detected within an individual's baseline data, the model may be configured to adjust the “weights” of the data by day of the week. Biological rhythms that may require adjustment to the model in this manner include: 1) hyperdiurnal (faster than a day) and rhythms, including sleep cycles in sleep states, and periodic oscillations of physiological variables measured during wakefulness from less than an hour to several hours; 2) circadian rhythms; 3) non-endogenous daily rhythms that have been shown to be imposed on top of circadian rhythms, such as in work schedules; 4) weekly rhythms, or other exogenously imposed artificial time periodicities (e.g., a 12-day rhythm can be used in a hypothetical culture with a 12-day “week”); 5) multi-day ovarian rhythms in women and spermatogenesis rhythms in men; 6) lunar rhythms (associated with individuals living in places with weak or no artificial lighting); and 7) seasonal rhythms.

[0076] Biological rhythms are not always stationary. For example, many women have variability in ovarian cycle length over the course of a cycle, and hyperdiurnal rhythms are not expected to occur at exactly the same time or with the same periodicity over the course of a day, even within a single user. Therefore, the detection of these rhythms may be improved by using signal processing techniques sufficient to quantify the frequency composition while maintaining the temporal resolution of these rhythms in physiological data, and by assigning the phase of each rhythm to each measured moment, thereby correcting the adjustment model and time interval comparison. Biological rhythm adjustment models and parameters can be added, in a combination of linear or nonlinear approaches, as needed, to more accurately capture the dynamic physiological baseline of an individual or population.

[0077] Those skilled in the art will understand that one or more aspects of the present disclosure may be implemented in System 100 to solve other problems not described above, either additionally or as alternatives. Furthermore, aspects of the present disclosure may provide technical improvements to “conventional” systems or processes as described herein. However, the description and accompanying drawings only include illustrative technical improvements resulting from implementing aspects of the present disclosure and therefore do not represent all of the technical improvements provided within the claims.

[0078] Figure 2 shows an example of a system 200 that supports a technique for providing user-associated physiological state-related insights according to aspects of this disclosure. System 200 may implement or be implemented by system 100. Specifically, system 200 shows an example of a ring 104 (e.g., a wearable device 104), a user device 106, and a server 110, as described with reference to Figure 1.

[0079] In some embodiments, the ring 104 may be configured to be worn around a user's finger, and when worn around a user's finger, it may determine the physiological parameters of one or more users. Exemplary measurements and determinations may include, but are not limited to, the user's skin temperature, pulse waveform, respiratory rate, heart rate, HRV, blood oxygen level, etc.

[0080] The system 200 further includes a user device 106 (e.g., a smartphone) that communicates with the ring 104. For example, the ring 104 may communicate with the user device 106 wirelessly and / or via a wired connection. In some implementations, the ring 104 may transmit measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, motion / accelerometer data, ring input data, etc.) to the user device 106. The user device 106 may also transmit data to the ring 104, such as firmware / configuration updates for the ring 104. The user device 106 may process the data. In some implementations, the user device 106 may transmit the data to the server 110 for processing and / or storage.

[0081] The ring 104 may include a housing 205 which may include an inner housing 205-a and an outer housing 205-b. In some embodiments, the housing 205 of the ring 104 may store or otherwise include, but not limited to, various components of the ring (including, but not limited to, device electronics, power supplies (e.g., battery 210 and / or capacitors), one or more substrates (e.g., printable circuit boards) interconnecting the device electronics and / or power supplies). The device electronics may include device modules (e.g., hardware / software) such as a processing module 230-a, a memory 215, a communication module 220-a, and a power module 225. The device electronics may also include one or more sensors. An exemplary sensor may include one or more temperature sensors 240, a PPG sensor assembly (e.g., a PPG system 235), and one or more motion sensors 245.

[0082] The sensor may include an association module (not shown) configured to communicate with each component / module of the ring 104 and generate a signal associated with each sensor. In some embodiments, each component / module of the ring 104 may be coupled to communicate with one another via a wired or wireless connection. Furthermore, the ring 104 may include additional and / or alternative sensors or other components (including optical sensors (e.g., LEDs), oximeters, etc.) configured to collect physiological data from the user.

[0083] The ring 104 illustrated and described with reference to Figure 2 is provided for illustrative purposes only. Therefore, the ring 104 may include additional or alternative components as shown in Figure 2. Other rings 104 that provide the functionality described herein may be manufactured. For example, a ring 104 having fewer components (e.g., sensors) may be manufactured. In a particular example, a ring 104 may be manufactured having a single temperature sensor 240 (or other sensor), a power supply, and a device electronics configured to read the single temperature sensor 240 (or other sensor). In another particular example, the temperature sensor 240 (or other sensor) may be attached to the user's finger (e.g., using a clamp, spring-loaded clamp, etc.). In this case, the sensor may be wired to another computing device, such as a wrist-worn wearable computing device that reads the temperature sensor 240 (or other sensor). In other examples, a ring 104 including additional sensors and processing functions may be manufactured.

[0084] The housing 205 may include one or more housing 205 components. The housing 205 may include an outer housing 205-b component (e.g., a shell) and an inner housing 205-a component (e.g., molding). The housing 205 may include additional components (e.g., additional layers) not explicitly shown in Figure 2. For example, in some configurations, the ring 104 may include one or more insulating layers that electrically insulate the device electronics and other conductive materials (e.g., electrical traces) from the outer housing 205-b (e.g., a metal outer housing 205-b). The housing 205 may provide structural support for the device electronics, battery 210, substrate, and other components. For example, the housing 205 may protect the device electronics, battery 210, and substrate from mechanical forces such as pressure and shock. The housing 205 may also protect the device electronics, battery 210, and substrate from water and / or other chemicals.

[0085] The outer housing 205-b may be manufactured from one or more materials. In some configurations, the outer housing 205-b may contain a metal such as titanium that is relatively lightweight and can provide strength and wear resistance. The outer housing 205-b may also be manufactured from other materials such as polymers. In some configurations, the outer housing 205-b may be protective and decorative.

[0086] The inner housing 205-a may be configured to interface with the user's finger. The inner housing 205-a may be formed from a polymer (e.g., a medical-grade polymer) or other material. In some implementations, the inner housing 205-a may be transparent. For example, the inner housing 205-a may be transparent to light emitted by a PPG light-emitting diode (LED). In some implementations, the inner housing 205-a components may be molded on the outer housing 205-b. For example, the inner housing 205-a may contain a polymer molded (e.g., injection-molded) to fit into the metal shell of the outer housing 205-b.

[0087] The ring 104 may include one or more substrates (not shown). The device electronics and battery 210 may be contained on one or more substrates. For example, the device electronics and battery 210 may be mounted on one or more substrates. Exemplary substrates may include one or more printed circuit boards (PCBs), such as flexible PCBs (e.g., polyimide). In some mounting configurations, the electronics / battery 210 may include surface mount devices (e.g., surface mount technology (SMT) devices) on the flexible PCB. In some mounting configurations, one or more substrates (e.g., one or more flexible PCBs) may include electrical traces that provide electrical communication between the device electronics. The electrical traces may also connect the battery 210 to the device electronics.

[0088] The device electronics, battery 210, and substrate may be arranged within the ring 104 in various ways. In some configurations, one substrate containing the device electronics may be mounted along the bottom (e.g., lower half) of the ring 104 so that sensors (e.g., PPG system 235, temperature sensor 240, motion sensor 245, and other sensors) interface with the underside of the user's fingers. In these configurations, the battery 210 may be included along the top portion of the ring 104 (e.g., on another substrate).

[0089] The various components / modules of ring 104 represent functions (e.g., circuits and other components) that may be included in ring 104. A module may include any individual and / or integrated electronic circuit components that implement analog and / or digital circuits capable of generating the functions resulting from the module herein. For example, a module may include analog circuits (e.g., amplifiers, filtering circuits, analog-to-digital converters, and / or other signal conditioning circuits). A module may also include digital circuits (e.g., combinational or sequential logic circuits, memory circuits, etc.).

[0090] The memory 215 (memory module) of ring 104 may include any volatile, non-volatile, magnetic, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other memory device. The memory 215 may store any of the data described herein. For example, the memory 215 may be configured to store data collected by the respective sensors and PPG system 235 (e.g., motion data, temperature data, PPG data). Furthermore, the memory 215 may include instructions that, when executed by one or more processing circuits, cause the module to perform various functions belonging to the module as described herein. The device electronics of ring 104 described herein are merely illustrative device electronics. Therefore, the types of electronic components used to implement the device electronics may vary based on design considerations.

[0091] The functions attributed to the modules of ring 104 described herein may be embodied as one or more processors, hardware, firmware, software, or any combination thereof. The descriptions of different characteristics of the modules are intended to highlight different functional aspects and do not necessarily imply that such modules must be implemented by separate hardware / software components. Rather, the functions associated with one or more modules may be performed by separate hardware / software components or integrated within a common hardware / software component.

[0092] The processing module 230-a of ring 104 may include one or more processors (e.g., processing units), microcontrollers, digital signal processors, system-on-a-chip (SOC), and / or other processing devices. The processing module 230-a communicates with modules included in ring 104. For example, the processing module 230-a may transmit / receive data to / from modules and other components of ring 104 such as sensors. As described herein, modules may be implemented by various circuit components. Thus, modules may also be referred to as circuits (e.g., communication circuits and power supply circuits).

[0093] The processing module 230-a may communicate with memory 215. Memory 215 may contain computer-readable instructions that, when executed by the processing module 230-a, cause the processing module 230-a to perform various functions attributed to the processing module 230-a as described herein. In some implementations, the processing module 230-a (e.g., a microcontroller) may include additional features related to other modules, such as communication functions provided by the communication module 220-a (e.g., an integrated Bluetooth® Low Energy transceiver) and / or additional onboard memory 215.

[0094] The communication module 220-a may include circuitry that provides wireless and / or wired communication with the user device 106 (e.g., the communication module 220-b of the user device 106). In some implementations, the communication modules 220-a and 220-b may include wireless communication circuits such as Bluetooth® and / or Wi-Fi circuits. In some implementations, the communication modules 220-a and 220-b may include wired communication circuits such as Universal Serial Bus (USB) communication circuits. The ring 104 and the user device 106 may be configured to communicate with each other using the communication module 220-a. The ring's processing module 230-a may be configured to send / receive data to and from the user device 106 via the communication module 220-a. Illustrative data may include, but is not limited to, exercise data, temperature data, pulse waveforms, heart rate data, HRV data, PPG data, and status updates (e.g., charge status, battery charge level, and / or ring 104 configuration settings). The ring processing module 230-a may also be configured to receive updates (e.g., software / firmware updates) and data from the user device 106.

[0095] The ring 104 may include a battery 210 (e.g., a rechargeable battery 210). An exemplary battery 210 may include a lithium-ion or lithium-polymer type battery 210, but a variety of battery options are possible. The battery 210 may be charged wirelessly. In some implementations, the ring 104 may include a power source other than the battery 210, such as a capacitor. The power source (e.g., the battery 210 or capacitor) may have a curved shape that conforms to the curve of the ring 104. In some embodiments, the charger or other power source may include additional sensors that can be used to collect data in addition to, or supplementing, the data collected by the ring 104 itself. Furthermore, the charger or other power source for the ring 104 may function as a user device 106, in which case the charger or other power source for the ring 104 may be configured to receive data from the ring 104, store and / or process the data received from the ring 104, and communicate data between the ring 104 and the server 110.

[0096] In some embodiments, the ring 104 includes a power module 225 that can control the charging of the battery 210. For example, the power module 225 may interface with an external wireless charger that charges the battery 210 when interfaced with the ring 104. The charger may include a reference structure that mates with the ring 104 reference structure to produce a specific orientation with the ring 104 during charging. The power module 225 may also regulate the voltage of the device electronics, regulate the power output to the device electronics, and monitor the charge state of the battery 210. In some implementations, the battery 210 may include a protection circuit module (PCM) that protects the battery 210 from high-current discharge, overvoltage during charging, and undervoltage during discharging. The power module 225 may also include electrostatic discharge (ESD) protection.

[0097] One or more temperature sensors 240 may be electrically coupled to the processing module 230-a. The temperature sensors 240 may be configured to generate a temperature signal (e.g., temperature data) indicating the temperature read or sensed by the temperature sensors 240. The processing module 230-a may determine the user's temperature at the location of the temperature sensors 240. For example, in the ring 104, the temperature data generated by the temperature sensors 240 may indicate the user's temperature (e.g., skin temperature) on the user's finger. In some implementations, the temperature sensors 240 may be in contact with the user's skin. In other implementations, a portion of the housing 205 (e.g., the inner housing 205-a) may form a barrier (e.g., a thin thermally conductive barrier) between the temperature sensors 240 and the user's skin. In some implementations, the portion of the ring 104 configured to contact the user's finger may have a thermally conductive portion and a thermally insulating portion. The thermally conductive portion may conduct heat from the user's finger to the temperature sensors 240. The heat-insulating portion may be the ring 104 (for example, the temperature sensor 240) which is insulated from the ambient temperature.

[0098] In some implementations, the temperature sensor 240 may generate a digital signal (e.g., temperature data) that the processing module 230-a can use to determine the temperature. As another example, if the temperature sensor 240 includes a passive sensor, the processing module 230-a (or the temperature sensor 240 module) may measure the current / voltage generated by the temperature sensor 240 and determine the temperature based on the measured current / voltage. The exemplary temperature sensor 240 may include a thermistor such as a negative temperature coefficient (NTC) thermistor, or other types of sensors including resistors, transistors, diodes, and / or other electrical / electronic components.

[0099] The processing module 230-a may sample the user's body temperature over time. For example, the processing module 230-a may sample the user's body temperature according to a sampling rate. An exemplary sampling rate may include 1 sample / second, but the processing module 230-a may be configured to sample the temperature signal at other sampling rates greater than or less than 1 sample / second. In some implementations, the processing module 230-a may continuously sample the user's body temperature throughout the day and night. Sampling at a sufficient rate (e.g., 1 sample / second) over the course of a day can provide sufficient body temperature data for the analysis described herein.

[0100] The processing module 230-a may store the sampled temperature data in memory 215. In some implementations, the processing module 230-a can process the sampled temperature data. For example, the processing module 230-a may determine the average temperature value over a period of time. In one example, to determine the average temperature value per minute, the processing module 230-a may sum up all the temperature values ​​collected during that minute and divide by the number of samples during that minute. In a particular example where temperature is sampled at 1 sample / second, the average temperature would be the sum of all temperatures sampled in one minute divided by 60 seconds. Memory 215 may store the average temperature value over time. In some implementations, to conserve memory, memory 215 may store the average temperature (e.g., one per minute) instead of the sampled temperatures.

[0101] The sampling rate that can be stored in memory 215 may be configurable. In some implementations, the sampling rate may be the same throughout the day and night. In other implementations, the sampling rate may be varied throughout the day / night. In some implementations, ring 104 may filter / reject temperature readings such as large temperature spikes that do not indicate physiological changes (e.g., temperature spikes from a hot shower). In some implementations, ring 104 may filter / reject temperature readings that may be unreliable due to other factors such as excessive motion during motion 104 (e.g., as indicated by motion sensor 245).

[0102] Ring 104 (e.g., a communication module) may transmit the sampled temperature data and / or average temperature data to user device 106 for storage and / or further processing. User device 106 may transfer the sampled temperature data and / or average temperature data to server 110 for storage and / or further processing.

[0103] Although the ring 104 is shown as containing a single temperature sensor 240, the ring 104 may contain multiple temperature sensors 240 in one or more locations, such as along the inner housing 205-a near the user's finger. In some implementations, the temperature sensor 240 may be a standalone temperature sensor 240. As an addition or alternative, one or more temperature sensors 240 may be included together with other components (for example, packaged together with other components), such as an accelerometer and / or a processor.

[0104] The processing module 230-a may acquire and process data from multiple temperature sensors 240 in a manner similar to that described for a single temperature sensor 240. For example, the processing module 230 may individually sample, average, and store temperature data from each of the multiple temperature sensors 240. In other examples, the processing module 230-a may sample the sensors at different rates and average / store different values ​​from different sensors. In some implementations, the processing module 230-a may be configured to determine a single temperature based on the average of two or more temperatures determined by two or more temperature sensors 240 located at different locations on a finger.

[0105] The temperature sensors 240 on the ring 104 may acquire distal temperature at the user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the ring 104 may acquire the user's temperature from the underside of the finger or at different locations on the finger. In some implementations, the ring 104 may continuously acquire distal temperature (e.g., at a sampling rate). While the distal temperature measured by the ring 104 on the finger is described herein, other devices may measure temperature at the same / different locations. In some cases, the distal temperature measured on the user's finger may differ from the temperature measured at the user's wrist or other external body locations. In addition, the distal temperature measured on the user's finger (e.g., "shell" temperature) may differ from the user's core temperature. Thus, the ring 104 may provide a useful temperature signal that cannot be acquired at other internal / external locations of the body. In some cases, continuous temperature measurement on the finger may capture temperature fluctuations (e.g., small or large fluctuations) that may not be apparent in the core temperature. For example, continuous temperature measurements on the finger may capture minute-by-minute or hourly temperature fluctuations that provide additional insights that cannot be provided by other temperature measurements on other parts of the body.

[0106] The ring 104 may include a PPG system 235. The PPG system 235 may include one or more light transmitters that transmit light. The PPG system 235 may also include one or more light receivers that receive light transmitted by one or more light transmitters. The light receivers may generate a signal (hereinafter referred to as the "PPG" signal) indicating the amount of light received by the light receivers. The light transmitters may illuminate an area of ​​the user's fingers. The PPG signal generated by the PPG system 235 may indicate blood perfusion in the illuminated area. For example, the PPG signal may indicate a change in blood volume in the illuminated area caused by the user's pulse pressure. The processing module 230-a may sample the PPG signal and determine the user's pulse waveform based on the PPG signal. The processing module 230-a may determine various physiological parameters based on the user's pulse waveform, such as the user's respiratory rate, heart rate, HRV, oxygen saturation, and other circulatory parameters.

[0107] In some implementations, the PPG system 235 may be configured as a reflective PPG system 235, where the optical receiver receives transmitted light reflected through the user's finger area. In some implementations, the PPG system 235 may be configured as a transmissive PPG system 235, where the optical transmitter and optical receiver are positioned facing each other so that light is transmitted directly to the optical receiver through a portion of the user's finger.

[0108] The number and ratio of transmitters and receivers included in the PPG system 235 may vary. An exemplary optical transmitter may include a light-emitting diode (LED). The optical transmitter may transmit light in the infrared spectrum and / or other spectra. An exemplary optical receiver may include, but is not limited to, an optical sensor, a phototransistor, and a photodiode. The optical receiver may be configured to generate a PPG signal in response to the wavelength received from the optical transmitter. The locations of the transmitters and receivers may vary. In addition, a single device may include reflective and / or transmissive PPG systems 235.

[0109] The PPG system 235 shown in Figure 2 may include a reflective PPG system 235 in some implementations. In these implementations, the PPG system 235 may include a centrally located optical receiver (e.g., at the bottom of the ring 104) and two optical transmitters located on each side of the optical receiver. In this implementation, the PPG system 235 (e.g., the optical receiver) may generate a PPG signal based on light received from one or both of the optical transmitters. In other implementations, other arrangements, combinations, and / or configurations of one or more optical transmitters and / or optical receivers are contemplated.

[0110] The processing module 230-a may control one or both of the optical transmitters to transmit light while sampling the PPG signal generated by the optical receiver. In some implementations, the processing module 230-a may cause the optical transmitter with the stronger received signal to transmit light while sampling the PPG signal generated by the optical receiver. For example, the selected optical transmitter may continuously emit light while the PPG signal is being sampled at a sampling rate (e.g., 250 Hz).

[0111] Sampling the PPG signal generated by the PPG system 235 may result in a pulse waveform that may be referred to as "PPG". The pulse waveform may represent blood pressure versus time for multiple cardiac cycles. The pulse waveform may include peaks indicating cardiac cycles. In addition, the pulse waveform may include respiratory-induced variations that can be used to determine the respiratory rate. In some implementations, the processing module 230-a may store the pulse waveform in memory 215. The processing module 230-a may process the generated pulse waveform and / or the pulse waveform from memory 215 to determine the user physiological parameters described herein.

[0112] The processing module 230-a may determine the user's heart rate based on the pulse waveform. For example, the processing module 230-a may determine the heart rate (e.g., beats / minute) based on the time between peaks in the pulse waveform. The time between peaks is sometimes called the heart rate interval (IBI). The processing module 230-a may store the determined heart rate value and IBI value in the memory 215.

[0113] The processing module 230-a may determine the HRV over time. For example, the processing module 230-a may determine the HRV based on the variation in IBI. The processing module 230-a may store the HRV values ​​over time in memory 215. Furthermore, the processing module 230-a may determine the user's respiratory rate over time. For example, the processing module 230-a may determine the respiratory rate based on frequency modulation, amplitude modulation, or baseline modulation of the user's IBI value over time. The respiratory rate may be calculated as breaths per minute or as another respiratory rate (e.g., breaths per 30 seconds). The processing module 230-a may store the user respiratory rate values ​​over time in memory 215.

[0114] The ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6D accelerometers) and / or one or more gyroscopes (gyro). The motion sensors 245 may generate motion signals indicating the motion of the sensors. For example, the ring 104 may include one or more accelerometers that generate acceleration signals indicating the acceleration of the accelerometers. As another example, the ring 104 may include one or more gyro sensors that generate gyro signals indicating angular motion (e.g., angular velocity) and / or changes in orientation. The motion sensors 245 may be included in one or more sensor packages. An exemplary accelerometer / gyro sensor is the Bosch BMI160 inertial microelectromechanical system (MEMS) sensor capable of measuring angular velocity and acceleration in three vertical axes.

[0115] The processing module 230-a may sample the motion signal at a sampling rate (e.g., 50 Hz) and determine the motion of the ring 104 based on the sampled motion signal. For example, the processing module 230-a may sample the acceleration signal to determine the acceleration of the ring 104. As another example, the processing module 230-a may sample the gyro signal to determine the angular motion. In some implementations, the processing module 230-a may store the motion data in the memory 215. The motion data may include the sampled motion data and motion data calculated based on the sampled motion signal (e.g., acceleration and angular values).

[0116] Ring 104 may store various types of data as described herein. For example, ring 104 may store temperature data such as raw sampled temperature data and calculated temperature data (e.g., mean temperature). As another example, ring 104 may store pulse waveforms and PPG signal data such as data calculated based on the pulse waveforms (e.g., heart rate values, IBI values, HRV values, and respiratory values). Ring 104 may also store motion data such as sampled motion data showing linear and angular motion.

[0117] The ring 104 or other computing device may calculate and store additional values ​​based on sampled / calculated physiological data. For example, the processing module 230 may calculate and store various metrics such as sleep metrics (e.g., sleep score), activity metrics, and ready-to-go metrics. In some implementations, these additional values / metrics may be referred to as “derived values.” The ring 104 or other computing / wearable device may calculate various values / metrics related to exercise. Exemplary derived values ​​of exercise data may include, but are not limited to, exercise count values, regularity values, intensity values, metabolic equivalents (METs) of task values, and orientation values. The exercise count, regularity values, intensity values, and METs may indicate the amount of user exercise over time (e.g., velocity / acceleration). The orientation value may indicate how the ring 104 is oriented on the user’s finger and whether the ring 104 is worn on the left or right hand.

[0118] In some implementations, motion counts and regularity values ​​may be determined by counting the number of acceleration peaks within one or more time periods (e.g., one or more periods of 30 seconds to 1 minute). Intensity values ​​may indicate the number of movements and the associated intensity of the movements (e.g., acceleration values). Intensity values ​​may be classified as low, medium, and high depending on the associated threshold acceleration value. MET may be determined based on the intensity of movements, the regularity / irregularity of movements, and the number of movements associated with different intensities during a given time period (e.g., 30 seconds).

[0119] In some implementations, the processing module 230-a may compress the data stored in memory 215. For example, the processing module 230-a may delete the sampled data after performing calculations based on it. As another example, the processing module 230-a may average the data over a longer period to reduce the number of stored values. In a particular example, if the average temperature of a user over a one-minute period is stored in memory 215, the processing module 230-a may calculate the average temperature over a five-minute period for storage and then erase the average temperature data for the one minute. The processing module 230-a may compress the data based on various factors, such as the total amount of used / available memory 215 and / or the elapsed time since ring 104 last sent data to the user device 106.

[0120] The user's physiological parameters may be measured by sensors included on the ring 104, but other devices may also measure the user's physiological parameters. For example, the user's body temperature may be measured by a temperature sensor 240 included on the ring 104, but other devices may also measure the user's body temperature. In some examples, other wearable devices (e.g., wrist devices) may include sensors for measuring the user's physiological parameters. In addition, medical devices such as external medical devices (e.g., wearable medical devices) and / or implantable medical devices may measure the user's physiological parameters. The techniques described herein may be implemented using one or more sensors on any type of computing device.

[0121] Physiological measurements may be performed continuously throughout the day and / or night. In some implementations, physiological measurements may be performed between the 104 parts of the day and / or the night. In some implementations, physiological measurements may be performed in response to the user determining that they are in a particular state, such as active, resting, and / or sleeping. For example, ring 104 may perform physiological measurements in the resting / sleeping state to obtain a cleaner physiological signal. In one example, ring 104 or another device / system may detect when the user is resting and / or sleeping and obtain physiological parameters (e.g., temperature) of the detected state. The device / system may use the resting / sleeping physiological data and / or other data when the user is in other states in order to implement the techniques of this disclosure.

[0122] In some implementations, as previously described herein, the ring 104 may be configured to collect, store, and / or process data, and may transfer any of the data described herein to the user device 106 for storage and / or processing. In some embodiments, the user device 106 includes a wearable application 250, an operating system (OS), a web browser application (e.g., a web browser 280), one or more additional applications, and a GUI 275. The user device 106 may further include other modules and components, including sensors, audio devices, haptic feedback devices, etc. The wearable application 250 may include examples of applications (e.g., “apps”) that can be installed on the user device 106. The wearable application 250 may be configured to acquire data from the ring 104, store the acquired data, and process the acquired data, as described herein. For example, a wearable application 250 may include a user interface (UI) module 255, an acquisition module 260, a processing module 230-b, a communication module 220-b, and a storage module (e.g., a database 265) configured to store application data.

[0123] The various data processing operations described herein may be performed by the ring 104, the user device 106, the server 110, or any combination thereof. For example, in some cases, data collected by the ring 104 may be preprocessed and sent to the user device 106. In this example, the user device 106 may perform some data processing operations on the received data, send the data to the server 110 for data processing, or both. For example, in some cases, the user device 106 may perform processing operations that require relatively low processing power and / or operations that require relatively low latency, while the user device 106 may send data to the server 110 for processing operations that require relatively high processing power and / or operations that may allow for relatively high latency.

[0124] In some embodiments, the ring 104 of system 200, the user device 106, and the server 110 may be configured to evaluate the user's sleep patterns. Specifically, each component of system 200 may be used to collect data from the user via the ring 104 and generate one or more scores for the user (e.g., a sleep score, a ready score) based on the collected data. For example, as described herein, the user may wear the ring 104 of system 200 to collect data from the user, including body temperature, heart rate, HRV, etc. The data collected by the ring 104 may be used to determine when the user is sleeping in order to evaluate the user's sleep on a given "sleep day". In some embodiments, a score may be calculated for the user for each sleep day such that the first sleep day is associated with a first set of scores, and the second sleep day is associated with a second set of scores. The scores may be calculated for each sleep day based on the data collected by the ring 104 during each sleep day. The scores may include, but are not limited to, a sleep score, a ready score, etc.

[0125] In some cases, a “sleep day” may be aligned with a conventional calendar day, such that a given sleep day runs from midnight to midnight on each calendar day. In other cases, a sleep day may be offset relative to a calendar day. For example, a sleep day may run from 6:00 PM (18:00) on one calendar day to 6:00 PM (18:00) on the following calendar day. In this example, 6:00 PM may function as a “cutoff time,” in which case data collected from the user before 6:00 PM is counted for the current sleep day, and data collected from the user after 6:00 PM is counted for the next sleep day. Due to the fact that most individuals sleep most at night, offsetting the sleep day relative to the calendar day allows the system 200 to assess the user’s sleep pattern to match the user’s sleep schedule. In some cases, the user may be allowed to selectively adjust the timing of the sleep day relative to the calendar day (e.g., via a GUI) to align the sleep day with the duration of sleep that each user typically sleeps.

[0126] In some implementations, each overall score for a user each day (e.g., sleep score, ready score) may be determined / calculated based on one or more “contributing factors,” “factors,” or “contributing elements.” For example, a user’s overall sleep score may be calculated based on a set of contributing factors (including total sleep, efficiency, rest, REM sleep, deep sleep, latency, timing, or any combination thereof). The sleep score may include any number of contributing factors. The “total sleep” contributing factor may refer to the sum of all sleep periods in a sleep day. The “efficiency” contributing factor may reflect the ratio of time spent sleeping to time spent awake during sleep and may be calculated using the efficiency average of the longer sleep periods (e.g., main sleep periods) in a sleep day, weighted by the duration of each sleep period. The “rest” contributing factor may indicate how restful the user’s sleep is and may be calculated using the average of all sleep periods in a sleep day, weighted by the duration of each period. The resting factor may also be based on "wake-up counts" (e.g., the sum of all wake-ups detected during different sleep periods when the user gets up), excessive movement, and "got-up counts" (e.g., the sum of all get-ups detected during different sleep periods when the user gets out of bed).

[0127] The "REM sleep" contributor may refer to the sum of REM sleep durations across the entire sleep period of a sleep day that includes REM sleep. Similarly, the "deep sleep" contributor may refer to the sum of deep sleep durations across the entire sleep period of a sleep day that includes deep sleep. The "latency" contributor may indicate the time it takes for a user to fall asleep (e.g., mean, median, longest) and may be calculated using the average of long sleep periods across the entire sleep day, weighted by the duration of each period and the number of such periods (e.g., a given sleep stage or a combination of sleep stages may be its own contributor or a weight of other contributors). Finally, the "timing" contributor may refer to the relative timing of sleep periods within a sleep day and / or calendar day and may be calculated using the average of the entire sleep period of a sleep day, weighted by the duration of each period.

[0128] As another example, a user's overall ready score may be calculated based on a set of contributing factors (including sleep, sleep balance, heart rate, HRV balance, recovery index, body temperature, activity, activity balance, or any combination thereof). The ready score may include any number of contributing factors. The "sleep" contributing factor may refer to the combined sleep score for all sleep periods within a sleep day. The "sleep balance" contributing factor may refer to the cumulative duration of all sleep periods within a sleep day. In particular, sleep balance may indicate to the user whether the sleep they have had over a certain period (e.g., the past two weeks) is balanced with their needs. Typically, adults need 7-9 hours of sleep per night to maintain health, enhance alertness, and perform at their best mentally and physically. However, it is normal to occasionally experience poor sleep during the night, and therefore, sleep balance contributing factors and long-term sleep patterns should be taken into account to determine whether each user's sleep needs are being met. The “resting heart rate” contributing factor may represent the lowest heart rate from the longest sleep period of the sleep day (e.g., the main sleep period), and / or the lowest heart rate from a nap that occurred after the main sleep period.

[0129] Continuing with the "Contributing Factors" (e.g., Factors, Contributing Factors) of the Ready Score, the "HRV Balance" contributing factor may represent the best average HRV from the main sleep period and naps that occurred after the main sleep period. The HRV Balance contributing factor may help the user track their recovery status by comparing the HRV trend over a first period (e.g., 2 weeks) to the average HRV over a longer second period (e.g., 3 months). The "Recovery Index" contributing factor may be calculated based on the longest sleep period. The Recovery Index measures the time it takes for the user's resting heart rate to stabilize at night. A sign of very good recovery is that the user's resting heart rate stabilizes during the first half of the night, at least 6 hours before the user wakes up, leaving enough physical time to recover the following day. The "Body Temperature" contributing factor may be calculated based on the longest sleep period (e.g., main sleep period) or based on naps that occurred after the longest sleep period, if the user's best body temperature during sleep is at least 0.5°C higher than the best body temperature during the longest period. In some embodiments, the ring may measure the user's body temperature while the user is sleeping, and the system 200 may display the user's average body temperature relative to the user's baseline body temperature. If the user's body temperature is outside the normal range (e.g., clearly above or below 0.0), the body temperature contributor may be highlighted (e.g., transition to a “caution” state) or otherwise generate an alert to the user.

[0130] In some implementations, some characteristics can be measured using the wearable device 104 and / or the user device 106, some attributes can be derived from the measurements ("derived attributes"), and some attributes can be further derived from the measured and / or derived attributes ("further derived attributes"). The measured values ​​may include at least one of the following: heart rate interval (IBI), physical activity (intensity, duration, time), skin temperature, current time and time of day, ambient light exposure, mental load, and feeding. The derived attributes may include at least one of the following: resting heart rate, respiratory rate, sleep phase, sleep duration, bedtime, and activity preference. The further derived attributes may include at least one of the following: mid-sleep, sleep onset latency, voluntary wake-up time, circadian type (morning / evening), circadian rhythm, circadian arousal curve, sleep-driven curve, synchronization effect index, sleepiness index, and readiness.

[0131] The incidental benefits of this disclosure may be further shown and explained with reference to Figure 3.

[0132] Figure 3 shows an example of a system 300 that supports techniques for providing user-associated physiological state-related insights according to an aspect of this disclosure. An aspect of system 300 may implement an aspect of system 100, system 200, or both, or be implemented by both. For example, system 300 may support techniques for providing user-associated physiological state-related insights as described herein.

[0133] The system 300 includes a user 102, a wearable device 104 (e.g., a wearable ring device 104), and a user device 106, which may be an example of a corresponding device as described in Figures 1 and 2. In some embodiments, the system 300 may also include a vehicle 302 that can be connected to the user device 106 via a wired or wireless connection. The vehicle 302 may be any vehicle that the user 102 can drive or travel on, such as a car, bicycle, train, airplane, bus, boat or other vessel, electric scooter, etc. The user device 106 may run a wearable application 250. In some embodiments, the user 102 may use several separate vehicles during a single travel event.

[0134] System 300 may provide physiological state-related insights associated with user 102. As used herein, the term “physiological state-related insights associated with user” may refer, for example, to messages, commands, instructions, user guidance information, user arousal information, or user status information representing the user’s current arousal data or physiological state, or any combination thereof, which may be output by user device 106 or transmitted to vehicle 302.

[0135] The wearable application 250 may receive baseline physiological data associated with user 102 from the wearable device 104. The physiological baseline data, including measured physiological parameters, may include any physiological data known in the art (including, but not limited to, temperature data, accelerometer data (e.g., motion / exercise data), sleep data, heart rate data, HRV data, blood oxygen level data, respiratory rate data, or any combination thereof). Figure 3 shows only one wearable device 104, but in other embodiments, there may be multiple wearable devices associated with user 102.

[0136] The wearable application 250 may obtain a user-associated physiological baseline based at least in part on baseline physiological data associated with the user, the physiological baseline providing reference user arousal data. The user-associated physiological baseline may be determined by the wearable device 104, the user wearable application 250, or an external processing device, or any combination thereof. Reference user arousal data may be derived from baseline physiological data, i.e., actual measurements taken by the wearable device, e.g., daytime heart rate data (e.g., heart rate while the user is awake), nighttime heart rate data (e.g., heart rate while the user is sleeping), training heart rate data (e.g., heart rate during training), recovery time (e.g., time spent in a relaxed state), body temperature, respiratory rate, blood oxygen saturation activity / movement, or any combination thereof.

[0137] The wearable application 250 may receive additional physiological data associated with the user from at least one wearable device. For example, the additional physiological data associated with the user may be related to, for example, the most recent 24-hour, 48-hour, or 72-hour period, or any other applicable period.

[0138] The wearable application 250 may acquire current user arousal data based at least in part on additional physiological data associated with the user. Current user arousal data may be derived from additional physiological data, i.e., actual measurements taken by the wearable device, such as heart rate, skin temperature, blood oxygen saturation, activity / movement, etc. In other words, current user arousal data represents the user's current state, derived from the most recent measurements provided by the wearable device 104 acquired over a specific period, e.g., the most recent 24, 48, or 72 hours, or any other applicable period. Current user arousal data may then be taken into consideration when the user initiates a movement event or is about to initiate a movement event in the (near) future.

[0139] In this context, the term “alertness data” may encompass not only the user’s level of alertness or readiness, or lack thereof, but also physical and mental recovery from various types of stress. Alertness may be an estimate of the user’s physiological and / or mental state in which they function well at a given moment. Alertness may, for example, summarize both the physical and mental prerequisites for having a good day. Essentially, alertness may encompass the effects of previous physical activity, sleep the previous night, and each measured bodily response. Bodily responses may mean, for example, how much body temperature, resting heart rate relative to the user’s own normative value, or the response to the previous day’s physical activity has changed. The terms “alertness data,” “alertness score,” “alertness level,” “recovery data,” “readiness data,” “readiness level,” and “readiness score” are interchangeable herein.

[0140] In one embodiment, the user device is configured to calculate a baseline and / or arousal level / arousal score for evaluating the user's arousal level. Specifically, a ready score may be calculated based on cross-correlation analysis of long-term data, trends, and deep data analysis (i.e., heart rate variability, hypnogram, stress level, etc.). Furthermore, the long-term data, trends, and cross-correlation analysis may be associated with the period over which the deep data analysis is performed (e.g., days, weeks, or months). Thus, the measured user movement is correlated with biosignals such as heart rate, electrocardiogram, heart rate variability, and stress level during such a period to calculate a ready score, thereby evaluating the user's readiness.

[0141] The Readiness Score indicates the user's level of readiness and their recovery from mental and physical stress. As used herein, the terms “Readiness” or “Arousal” may also describe the return to a normal state of mental and physical strength (or energy level) after mental and physical stress. For example, if physical stress is associated with a period of activity (e.g., physical exercise), the Readiness Score may be based on the user’s heart rate, heart rate variability, and stress level. For instance, if the user’s heart rate, heart rate variability, and stress level return to normal after exercise, the Readiness Score may be good or high (e.g., approximately 90%). Similarly, if mental stress is associated with a period of rest (e.g., sleep), the Readiness Score may be based on the user’s movement, heart rate, and sleep chart. For example, if the user is not very active, their heart rate is within the desired level (70–40 beats / min), and their sleep chart shows a good amount of deep sleep, the Readiness Score may be good or high (e.g., approximately 90%).

[0142] In one embodiment, user history data is also used to calculate the ready score. For example, the history data may include information related to the user's medical history, but it may also include history data collected by the system itself. For example, the history data may include data showing how the user recovers from a load. Alternatively, the history data may include information about past work life, eating habits, etc. Therefore, it may be apparent to those skilled in the art that the history data may have a substantial impact on the measurement of the user's ready score.

[0143] The solutions described herein may use different computational parameters and may be designed to learn from previous measurements. For example, data from the past week, two weeks, one month, or two months (or any other time period) may be used to set personalized calibrations, averages, and / or limits that are typical of the user. Furthermore, the collected physiological data may be used to vary the weighting given to different aspects measured or obtained from the user.

[0144] When a user's movements are measured or acquired, movement data is obtained, and when at least one biosignal is measured, biosignal or biosignal data is obtained (these terms can be used interchangeably). User movement data acquired during rest periods can be used for a variety of purposes. For example, the data is typically used to determine whether a given moment belongs to an active period, a rest period, or, if one or more further types of periods are defined, probably to one of those further types. Furthermore, it can be used as part of the starting data for determining a rest summary. Biosignals or some biosignals may be determined during a rest period, but may also be determined during an active period. For example, if a user's body temperature rises during a rest period, it may be continued to be monitored during the next active period to ensure that the rise is due to fever or other reasons. Furthermore, elevated body temperature readings, such as fever, can be programmed to adjust readiness / arousal levels over a longer period, i.e., not just the next day. In practice, it is beneficial to have rest days after being ill for more than one day, in proportion to the length of the illness.

[0145] User movements may be measured or retrieved from a wearable device and / or a separate device. User movements may include, for example, actual movements such as raising an arm or hand, walking, running, or cumulative steps, activity time, or distance covered by the user.

[0146] In one embodiment, the wearable device may include at least one motion sensor, such as an accelerometer, gyroscope, magnetic field sensor, or a combination thereof, to measure the user's movement. The motion sensor is configured to generate motion data indicating the user's movement. For example, the motion sensor may be configured to determine linear motion information, rotational motion information, etc. Furthermore, such information (linear motion or rotational motion) may be combined or correlated to generate motion data indicating the user's movement. As described above, the motion data may also be generated by a separate device and retrieved or acquired by a ring or server.

[0147] The wearable application 250 may identify trigger conditions for providing physiological state-related insights associated with a user in relation to a movement event, at least in part, based on a comparison between current user arousal data and baseline user arousal data. For example, the comparison may reveal that the user's current arousal data is reduced compared to baseline user arousal data. Reduced user arousal data means that several factors are influencing the user as shown in the physiological data measured by the wearable device 104. For example, a trigger condition may be identified when the current user arousal data differs from the baseline user arousal data by a predetermined threshold amount or level. In some embodiments, the user arousal data and baseline user arousal data can be expressed as percentage values. Therefore, in some embodiments, the predetermined threshold amount may also be expressed as a percentage value. For example, the user arousal data may currently be 40%, and the baseline user arousal data may be 75%. If the predetermined threshold amount is, for example, 30%, the trigger condition may be identified as the difference between the baseline user arousal data and the baseline user arousal data being greater than 30%.

[0148] For example, current user arousal data may indicate that the user has been sleep-deprived for the past three nights. This may cause the current user arousal data to fall below baseline user arousal data, triggering a physiological state-related insight associated with the user in relation to a travel event. In some embodiments, different threshold levels may be used to identify trigger conditions. For example, different physiological state-related insights associated with the user in relation to a travel event may be triggered depending on the comparison result. If the comparison yields a first comparison result, a first physiological state-related insight associated with the user in relation to a vehicle-involved travel event may be provided. If the comparison yields a second comparison result, a second physiological state-related insight associated with the user in relation to a vehicle-involved travel event may be provided. For example, the first comparison result may suggest that the user should take short breaks during the driving event. The second comparison result may suggest that the user should take longer breaks during the driving event.

[0149] The wearable application 250 may cause the user device 106 to provide physiological state-related insights associated with the user. For example, the user device 106 itself may be made to output physiological state-related insights associated with the user. In some embodiments, the graphical user interface (GUI) of the user device 106 may be configured to display physiological state-related insights. The GUI may, for example, display messages or notifications to the user, such as "Take a short break," "Drive slowly," "Maintain a greater distance from the vehicle in front," "Drink some coffee," etc. In some embodiments, the user device 106 may be configured to provide at least one of auditory alerts, tactile alerts, and visual alerts associated with the physiological state-related insights. For example, the user device 106 may provide the user with auditory messages via a speaker, such as "Take a short break," "Drive slowly," "Maintain a greater distance from the vehicle in front," or "Drink some coffee." In another example, the auditory alert may be a predetermined audio signal provided via a speaker, such as a buzzer signal to warn the user. The audio signal may also indicate that a more detailed message is displayed on the GUI. In another example, the user device 106 may provide a predetermined tactile pattern to alert the user. In yet another example, the wearable application 250 may instruct the user device 106 to instruct the wearable device 104 to provide a tactile alert.

[0150] In some embodiments, a travel event may be a future travel event. A future travel event may be determined, for example, based on a calendar entry in a calendar associated with user 102. The calendar entry may indicate, for example, that user 106 will start a long-distance car drive in the morning, or that user 106 will take a long-distance intercontinental flight. In another example, the calendar entry may indicate a client to meet in a location that requires a long-distance car trip or long-distance flight. In yet another example, the calendar may indicate a busy day followed by a long-distance trip. In yet another example, the timing of the trip may be difficult (very early or very late travel time). In yet another example, after a long-distance drive, another drive may follow only within a short recovery period in between. The wearable application 250 may cause the user device 106 to provide instructions to user 102 regarding, for example, the amount of sleep needed (e.g., "Tomorrow you will be on a long drive, so go to bed by 10 p.m. at the latest and have a hearty breakfast in the morning," or "Tomorrow you will be on a plane, so go to bed by 10 p.m. at the latest and have a hearty breakfast in the morning") and the number of meals and breaks to take while driving (e.g., "Today you will be on a long drive after a busy day, so take several breaks while driving," or "You will be on another long drive soon, so eat and take a break before you start driving"). Furthermore, the sleep stages, sleep scores, and sleep states associated with user 102 have been described above with respect to Figure 1. This sleep information may be used when determining and providing physiological state-related insights to the user.

[0151] For example, when a wearable application 250 decides what instructions or insights to provide to the user, it may take into account the results of a comparison between current user arousal data and baseline user arousal data. For instance, if the comparison between current user arousal data and baseline user arousal data shows a first difference that is greater than a second difference, then, due to that first difference, a longer sleep instruction may be provided than in the case of the second difference. In other words, the amount of sleep required may depend on the comparison result.

[0152] In some embodiments, the movement event may be a current movement event. For example, physiological data received from the wearable device 104 may include accelerometer data. The wearable application 250 may detect a current movement event based at least in part on the accelerometer data. For example, the accelerometer data may indicate that user 102 is driving a car and that user 102's one or both hands make a rotational motion when user 106 turns the car's steering wheel. If user 102 is wearing the wearable device 104, which is, for example, a ring, the ring moves when the user moves their hand including the ring. At the same time, the actual movement of the user's hand when turning the steering wheel is restricted, so this movement produces a specific repetitive signal component in the accelerometer data. When the accelerometer data is analyzed, these signal components can be identified and it may be determined that user 102 is currently driving a car. In some embodiments, the determination may use reference accelerometer data to identify that the user is driving a car. Reference accelerometer data may refer to data stored in the user device 106 and accessible by the wearable application 250. Reference acceleration data may, for example, identify signal waveforms related to hand movements when the steering wheel is rotated. These waveforms may then be compared with acceleration data acquired from the wearable device 102.

[0153] In some embodiments, the user device 106 may establish an active local communication link (e.g., a wireless Bluetooth® link) between the user device 106 and the vehicle 302, and a movement event may be detected at least in part on the existence of the active local communication link. When the wearable application 250 identifies that an active local communication link has been established between the user device 106 and the vehicle 302, for example, due to the user device 106 being detected by the vehicle 302, or vice versa, the existence of the active local communication link between the user device 106 and the vehicle 302 provides an indication that a user has initiated a movement event. Detection of a movement event may also mean that a process for identifying trigger conditions can be initiated. In other words, since there has been no prior indication of a movement event and future movement events are unknown, the process for identifying trigger conditions may, in one example, be initiated when a movement event is detected.

[0154] In some embodiments, the user device 106 may receive satellite positioning data from the user device 106, and movement events may be detected at least in part based on the satellite positioning data received from the user device 106. For example, if the speed of user 102 determined based on the satellite positioning data exceeds a predetermined threshold, or if user 106's location is constantly changing and acceleration sensor data received from the wearable device 104 indicates that user 102 is driving a car, it can be determined that user 106 is indeed driving a car and that there is a current movement event. The acceleration data may, for example, show repetitive hand movements of user 106 indicating that user 106 is touching and turning the steering wheel while driving. In some embodiments, the user device 106 may store reference acceleration data accessible by the wearable application 250. The reference acceleration data may, for example, identify signal waveforms associated with hand movements when the steering wheel is turned. These waveforms can then be compared with acceleration data obtained from the wearable device 102. If a match or a close match is found, it can be determined that user 102 is driving a car.

[0155] In some embodiments, in response to the detection of a movement event, a command may be sent to the wearable device 104 to apply a predetermined rate for transmitting physiological data. The wearable application 250 may instruct the wearable device 104 to transmit physiological data more frequently when a movement event is detected. This ensures that physiological data is transmitted frequently from at least one wearable device 104 to monitor the user 102 during the movement event. When physiological data is received more frequently, possible changes in the user 102 or the user 102's behavior reflected in the physiological data may be identified quickly, and necessary actions may be taken quickly. If the user's current arousal data is determined based on more recent physiological data, the trigger condition may be identified as soon as a change in the current user arousal data is detected. When it is detected that the movement event has ended, the wearable application 250 may instruct the wearable device 104 to transmit physiological data again at a normal rate, for example, to conserve the battery of the wearable device 104.

[0156] In some embodiments, providing the user device 106 with physiological state-related insights associated with user 102 may include transmitting the physiological state-related insights to the vehicle 302 via an active local communication link between the user device 106 and the vehicle 302. This enables a solution in which the vehicle can at least partially use the physiological state-related insights associated with the user, received from the user device 106, when deciding whether or not to alert user 102. For example, the physiological state-related insights associated with user 102 may include one or more predefined messages related to identified trigger conditions in order to provide the physiological state-related insights associated with the user in relation to a movement event involving the vehicle. For example, there may be an agreed-upon messaging structure between the wearable application 250 and the vehicle 302 or vehicle system. The messaging structure may define messages and information that can be transmitted between the wearable application 250 and the vehicle 302. For example, simple numerical or binary values ​​may be transmitted from the wearable application 250 to the vehicle 302, each numerical or binary value having a predetermined meaning. For example, a bit value of "0001" may indicate that the physiological state-related insight associated with user 102 means "take a coffee break," and a bit value of "0010" may indicate that the physiological state-related insight associated with user 102 means "stop immediately and take a longer break." A bit value of "0011" may indicate that the physiological state-related insight associated with user 102 means "slow down," etc. As another example, a particular bit value may indicate to vehicle 302 that the user is too fatigued to operate vehicle 302 safely. In response to this instruction, vehicle 302 may be configured not to start because the user cannot drive it safely. These are just examples of possible physiological state-related insights, and it is clear that any other insights related to user arousal data can be applied.Since the wearable device 104 provides physiological data about user 102, and current user alertness data can be determined based on this, it is possible to estimate or recognize the user's physical and / or mental load before or during a travel event. For example, in response to receiving physiological state-related insights from user device 106, the driver monitoring system may lower the temperature inside the vehicle to increase user 102's alertness, warn user 102 to take a break, or automatically reduce the speed of vehicle 102, depending on whether the physiological state-related insights indicate a decrease in user 106's alertness. In a further example, in response to physiological state-related insights received from wearable application 250, vehicle 302 may, for example, apply different minimum following distance limits to vehicles ahead of vehicle 302.

[0157] In some embodiments, the wearable application 250 may provide a secure application programming interface (API) to the vehicle 302 via the user device 106. This may enable the driver monitoring system of the vehicle 302 to receive additional information from the user device 106 in a secure manner via a secure communication connection for its decision-making.

[0158] In some embodiments, the wearable application 250 may cause the graphical user interface of the user device 106 to display physiological state-related insights associated with the user. The GUI may, for example, display messages or notifications to the user, such as “Take a little rest,” “Slow down,” “Maintain more distance between vehicles,” or “Drink some coffee.” As another example, if the travel event is a future travel event (e.g., tomorrow), the GUI may be configured to display a notification to user 102, for example, to ensure that user 102 gets enough sleep the following night and / or eats a proper meal at the appropriate time. For example, this notification may instruct the user to “Go to bed by 9:00 p.m. no later” if the travel event starts at 7:00 a.m. the following morning. As another example, the notification may instruct the user in the evening to “Eat a proper breakfast in the morning” if the travel event starts at 9:00 a.m. the following morning. As another example, physiological data acquired from the wearable device 104 may include glucose level measurements, and the wearable application 250 may monitor the user's glucose level based on these measurements. Based on the monitored glucose level, the GUI may be configured to inform the user if their blood glucose level is low / high and to instruct the user to eat before starting to move.

[0159] In some embodiments, the user device 106 may be configured to provide at least one of auditory, tactile, and visual alerts related to physiological state-related insights. For example, the user device 106 may provide auditory messages to the user 102 via a speaker, such as "Take a short break," "Slow down," "Maintain a greater distance," or "Have some coffee." In another example, the auditory alert may be a predetermined voice signal provided via a speaker, such as a buzzer signal to warn the user 102. The voice signal may also indicate that a more detailed message is provided on the GUI. In yet another example, the user device 106 may provide a predetermined tactile pattern to warn the user 102. In yet another example, the wearable application 250 may instruct the wearable device 104 to provide a tactile alert indicating that a more detailed message is provided on the GUI.

[0160] In some embodiments, when the user device 106 provides physiological state-related insights associated with user 102, the wearable application 250 may transmit the physiological state-related insights to a network entity, for example, a call service. For example, in response to the transmitted physiological state-related insights, the call service may establish a call to the user device 106 to notify the user about user 102's state. The call may inform the user, for example, that "you are too tired to continue driving and should stop immediately." The call service may be used in situations where the user's attention cannot be attracted in any other way, for example, via a GUI, auditory alerts, tactile alerts, and / or visual alerts. In other words, the call service may be used as a "last resort" to attract user 102's attention.

[0161] In some embodiments, the wearable application 250 may receive vehicle data associated with user 102 from vehicle 302. The vehicle data associated with the user includes mobile user behavior data collected during a mobile event. Trigger conditions may be identified based at least in part on the vehicle data. For example, there may be an agreed-upon messaging structure between the wearable application 250 and vehicle 302 or vehicle system. The messaging structure may define messages or information that can be transmitted between the wearable application 250 and vehicle 302. For example, a simple numerical or binary value may be transmitted from vehicle 302 to the wearable application 250. The driver monitoring system of vehicle 302 may monitor the user and the user's actions and behavior using, for example, various sensor and driving data provided by vehicle 302 (e.g., speed, driving duration, lane assist data, steering operation data, brake data, etc.). Each piece of information may then be transmitted to the wearable application 250, for example, as a coded message in the messaging structure. For example, a specific binary field may identify steering wheel handling data in coded form, such as the number of sudden steering wheel operations performed by the user within the last X minutes, or the number of times the user applied the brakes suddenly within the last X minutes. The vehicle 302 may transmit vehicle data to the wearable application 250, for example, via a secure API. Since the wearable application 250 knows the current user arousal level data based on physiological data received from the wearable device 104, the vehicle data received from the vehicle 302 may be used to complement the current user arousal level data. For example, the vehicle data received from the vehicle 302 may provide an indication that the user is showing symptoms of fatigue based on data collected by the vehicle. For example, the user may be performing too many sudden (corrective) steering wheel operations within a specified time, at which point the lane assist data indicates that the user is having difficulty staying in the lane.When vehicle data received from vehicle 302 is combined with current user arousal data determined by the wearable application 250, the combination may trigger a trigger condition to provide physiological state-related insights associated with the user in relation to a travel event involving vehicle 302. Therefore, data received from vehicle 302 may also be used in identifying the trigger condition. For example, current user arousal data alone may not yet trigger the identification of a trigger condition, but current user arousal data together with vehicle data may trigger the identification. Based on the identification, the wearable application 250 may, for example, prompt the user to stop vehicle 302 and take a break.

[0162] In some embodiments, the wearable application 250 may receive weather data associated with a travel event. Based on the weather data associated with the travel event, it may determine that the weather data associated with the travel event will affect the travel event. For example, the user may have just started a travel event in a car. The weather data may indicate, for example, that a storm is approaching and will coincide with the user's driving route. Identifying trigger conditions may then include identifying trigger conditions based at least in part on the weather data. For example, if current user arousal data indicates that the user is already tired, the wearable application 250 may also use the weather data when determining physiological state-related insights associated with the user. The provided physiological state-related insights may, for example, instruct the user to change their route due to the storm, or to take a longer break so that the storm passes the user's location during the interruption, or to instruct user 102 to take more breaks than usual during the travel event. Alternatively, if the user has not yet started a travel event and a storm is approaching, the wearable application 250 may, for example, ask the user 102 to delay the start of the travel event or to start the travel event earlier.

[0163] In some embodiments, the wearable application 250 may receive satellite positioning data associated with a travel event from the user device 106. For example, it may identify that the travel event relates to a route the user has not driven before, i.e., an unfamiliar route. The wearable application 250 may have access to route data, including routes the user has previously traveled. This information can be used to determine when the user will travel on a route they have never traveled before, or a route they rarely travel. Thus, a travel event may be determined to relate to an unfamiliar route for the user, which can be taken into consideration when identifying trigger conditions. The wearable application 250 may, for example, ask the user to drive more slowly than usual and / or to take breaks during the travel event.

[0164] In some embodiments, the wearable application 250 may receive route schedule data associated with a travel event. The route schedule data may specify, for example, the duration and start time of the travel event. For example, the wearable application 250 may detect, based on information obtained from a map application or navigation application, that user 102 is starting or intending to start a long-duration and / or long-lasting travel event, for example, by car. This information can be taken into consideration when identifying trigger conditions. For example, current user arousal data may indicate that the user is tired. When user 102 starts or intends to start a long-duration and / or long-lasting travel event, this can take into account what physiological state-related insights are provided to the user. For example, a long-duration and / or long-lasting car driving event may provide the user with both rest and meal instructions during the driving event, while a short-duration car driving event may provide the user with only rest instructions during the driving event.

[0165] In some embodiments, multiple vehicles may be involved in a single travel event. For example, user 102 may, or has, initiated a travel event that involves first boarding an airplane and then driving a car to a final destination. In the case of multiple vehicles involved in a single travel event, the physiological state-related insights associated with the user may take into account the fact that multiple vehicles are involved in one travel event. For example, the physiological state-related insights provided associated with a user may differ when the user first boards an airplane for six hours and then drives for five hours compared to when the user does not first board an airplane and then drives for five hours. For example, the user's behavior before and / or during the flight (e.g., whether the user sleeps during the flight) may influence the physiological state-related insights provided associated with the user.

[0166] Figure 4 shows a block diagram of a device 400 supporting a technique for providing user-associated physiological state-related insights, according to aspects of this disclosure. The device 400 may include an input module 405, an output module 410, and a wearable application 250. The device 400 may also include a processor. Each of these components may communicate with one another (for example, via one or more buses).

[0167] The input module 405 may provide means for receiving information such as packets related to various information channels (e.g., control channels, data channels, information channels related to disease detection technology), user data, control information, or any combination thereof. The information may be passed to other components of device 400. The input module 405 may utilize a single antenna or a set of multiple antennas.

[0168] The output module 410 may provide means for transmitting signals generated by other components of the device 400. For example, the output module 410 may transmit information such as packets related to various information channels (e.g., control channels, data channels, information channels related to disease detection technology), user data, control information, or any combination thereof. In some examples, the output module 410 may be located in the same place as the input module 405 in the transceiver module. The output module 410 may utilize a single antenna or a set of multiple antennas.

[0169] For example, the wearable application 250 may include a data acquisition component 420, a physiological baseline acquisition component 425, a current user arousal data acquisition component 430, a trigger condition identification component 435, and a physiological state-related insights providing component 440, or any combination thereof. In some examples, the wearable application 250 or its various components may be configured to perform various operations (e.g., receiving, monitoring, transmitting) using, or otherwise cooperating with, an input module 405, an output module 410, or both. For example, the wearable application 250 may receive information from the input module 405, transmit information to the output module 410, or be integrated with the input module 405, the output module 410, or both to receive information, transmit information, or perform various other operations as described herein.

[0170] The data acquisition component 420 may be configured as a means for receiving user-associated baseline physiological data from at least one wearable device, or may otherwise support it. The physiological baseline acquisition component 425 may be configured as a means for acquiring user-associated physiological baselines based at least in part on user-associated baseline physiological data, or may otherwise support it, where the physiological baseline provides reference user arousal data. The data acquisition component 420 may be configured as a means for receiving additional user-associated physiological data from at least one wearable device, or may otherwise support it. The current user arousal data acquisition component 430 may be configured as a means for acquiring current user arousal data based at least in part on additional user-associated physiological data, or may otherwise support it. The trigger condition identification component 435 may be configured as a means for identifying trigger conditions to provide user-associated physiological state-related insights related to vehicle-related mobility events, based at least in part on a comparison between current user arousal data and reference user arousal data, or may otherwise support it. The physiological state-related insights providing component 440 may be configured as a means for a user device to provide physiological state-related insights associated with the user, or it may support this otherwise.

[0171] Figure 5 shows a block diagram of a wearable application 500 supporting a technique for providing user-associated physiological state-related insights, as described herein. The wearable application 500 may be an example of a wearable application or wearable application 250, or both, as described herein. The wearable application 500 or its various components may be examples of means for performing various aspects of the technique for providing user-associated physiological state-related insights, as described herein. For example, the wearable application 500 may include a data acquisition component 505, a physiological baseline acquisition component 510, a current user arousal data acquisition component 515, a trigger condition identification component 520, a physiological state-related insights providing component 525, a communication link establishment component 530, a data transmission component 535, or any combination thereof. Each of these components may communicate with one another directly or indirectly (e.g., via one or more buses).

[0172] The data acquisition component 505 may be configured as a means for receiving user-associated baseline physiological data from at least one wearable device, or may otherwise support it. The physiological baseline acquisition component 510 may be configured as a means for acquiring user-associated physiological baselines based at least in part on user-associated baseline physiological data, or may otherwise support it, where the physiological baseline provides reference user arousal data. The data acquisition component 505 may be configured as a means for receiving additional user-associated physiological data from at least one wearable device, or may otherwise support it. The current user arousal data acquisition component 515 may be configured as a means for acquiring current user arousal data based at least in part on additional user-associated physiological data, or may otherwise support it. The trigger condition identification component 520 may be configured as a means for identifying trigger conditions to provide user-associated physiological state-related insights related to vehicle-related mobility events, based at least in part on a comparison between current user arousal data and reference user arousal data, or may otherwise support it. The physiological state-related insights providing component 525 may be configured as a means to cause a user device to provide physiological state-related insights associated with the user, or may otherwise support it. The communication link establishment component 530 may be configured as a means to establish a local communication link between the user device and the vehicle, or may otherwise support it. The data transmission / reception component 535 may be configured as a means to transmit physiological state-related insights to the vehicle and / or receive vehicle data associated with the user from the vehicle via the local communication link between the user device and the vehicle, or may otherwise support it.

[0173] In some examples, the trigger condition identification component 520 may be configured as a means for identifying a trigger condition when the current user arousal data differs from the reference user arousal data by a predetermined threshold amount, or it may support this otherwise.

[0174] In some examples, the movement event is a future movement event.

[0175] In some examples, the movement event is the current movement event.

[0176] In some examples, the physiological data received from the wearable device 104 includes acceleration sensor data, and the trigger condition identification component 520 may be configured, or otherwise support, as a means for identifying that the user is driving a vehicle, at least in part on a comparison between acceleration sensor data and reference acceleration sensor data, and for detecting a movement event, at least in part on the comparison.

[0177] In some examples, the physiological state-related insight-providing component 525 may be configured as a means for establishing an active local communication link between the user device and the vehicle, or otherwise support it, and the trigger condition identification component 520 may be configured as a means for detecting a movement event, or otherwise support it, at least in part on the presence of an active local communication link between the user device and the vehicle.

[0178] In some examples, the data acquisition component 505 may be configured as a means for receiving satellite positioning data from a user device, or may support it otherwise. In some examples, the trigger condition identification component 520 may be configured as a means for detecting a movement event, or may support it otherwise, based at least in part on satellite positioning data from a user device.

[0179] In some examples, the data transmission / reception component 525 may be configured, or otherwise support, for sending a command to at least one wearable device to apply a predetermined rate for transmitting physiological data in response to the detection of a movement event.

[0180] In some examples, the physiological state-related insights providing component 525 may be configured as a means for displaying physiological state-related insights on the graphical user interface of a user device, or may otherwise support it.

[0181] In some examples, the physiological state-related insights providing component 525 may be configured as a means to cause a user device to provide at least one of the auditory, tactile, and visual alerts associated with physiological state-related insights, or may otherwise support it.

[0182] In some examples, the data transmission / reception component 535 may be configured as a means for receiving vehicle data associated with a user from the vehicle, or otherwise supporting it, where the vehicle data associated with the user includes user behavior data collected during a travel event. In some examples, the trigger condition identification component 520 may be configured as a means for identifying a trigger condition, or otherwise supporting it, which includes identifying a trigger condition based at least in part on the vehicle data associated with the user.

[0183] In some examples, the data transmission / reception component 535 may be configured as a means for receiving meteorological data related to a travel event, or may otherwise support it. In some examples, the trigger condition identification component 520 may be configured as a means for determining, based at least in part, that meteorological data related to a travel event affects the travel event, and for identifying a trigger condition based at least in part on the meteorological data.

[0184] In some examples, the data transmission / reception component 535 may be configured as a means for receiving calendar data associated with a travel event, or may support it otherwise. In some examples, the trigger condition identification component 520 may be configured as a means for identifying a trigger condition, or may support it otherwise, including determining that a travel event is a future travel event based on calendar data associated with a travel event, and identifying a trigger condition at least in part based on that determination.

[0185] In some examples, the data transmission / reception component 535 may be configured as a means for receiving satellite positioning data related to a travel event from a user device, or may otherwise support it. In some examples, the trigger condition identification component 520 may be configured as a means for determining, based on satellite positioning data associated with a travel event, that the travel event is related to a route unfamiliar to the user, and at least in part on that determination, for identifying a trigger condition, or may otherwise support it.

[0186] In some examples, the data transmission / reception component 535 may be configured as a means for receiving route schedule data associated with a travel event, or may support it otherwise. In some examples, the trigger condition identification component 520 may be configured as a means for identifying a trigger condition, or may support it otherwise, based at least in part on the route schedule data.

[0187] Figure 6 shows a diagram of a system 600 including a device 605 that supports a technique for providing user-associated physiological state-related insights, according to aspects of this disclosure. Device 605 may be an example of a component of device 400 described herein, or may include such a component. Device 605 may include an example of a user device 106, as described herein. Device 605 may include components for bidirectional communication, including components for transmitting and receiving communications with a wearable device 104 and a server 110, such as a wearable application 620, a communication module 610, an antenna 615, a user interface component 625, a database (application data) 630, a memory 635, and a processor 640. These components may communicate electronically via one or more buses (e.g., bus 645) or otherwise coupled (e.g., operably, communicatively, functionally, electronically, electrically).

[0188] The communication module 610 may manage input and output signals for device 605 via antenna 615. The communication module 610 may include the example of communication module 220-b for user device 106 shown and described in Figure 2. In this regard, the communication module 610 may manage communication with ring 104 and server 110, as shown in Figure 2. The communication module 610 may also manage peripherals not integrated into device 605. In some cases, the communication module 610 may represent a physical connection or port to an external peripheral. In some cases, the communication module 610 may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-Windows®, OS / 2®, UNIX®, LINUX®, or another known operating system. In other cases, the communication module 610 may represent or interact with a wearable device (e.g., ring 104), a modem, a keyboard, a mouse, a touchscreen, or similar device. In some cases, the communication module 610 may be implemented as part of the processor 640. In some examples, the user may interact with the device 605 via the communication module 610, the user interface component 625, or via hardware components controlled by the communication module 610.

[0189] In some cases, device 605 may include a single antenna 615. However, in some other cases, device 605 may have multiple antennas 615, thereby enabling simultaneous transmission or reception of multiple radio transmissions. Communication module 610 may communicate bidirectionally via one or more antennas 615, a wired link, or a wireless link, as described herein. For example, communication module 610 may represent a radio transceiver and communicate bidirectionally with another radio transceiver. Communication module 610 may also include a modem for modulating packets, providing one or more antennas 615 for transmitting modulated packets, and demodulating packets received from one or more antennas 615.

[0190] The user interface component 625 may manage data storage and processing within the database 630. In some cases, the user may interact with the user interface component 625. In other cases, the user interface component 625 may operate automatically without user interaction. The database 630 may be a single database, a distributed database, multiple distributed databases, a data store, a data lake, or an emergency backup database.

[0191] Memory 635 may include RAM and ROM. Memory 635 may store computer-readable, computer-executable software that, when executed, contains instructions causing the processor 640 to perform various functions described herein. In some cases, memory 635 may include a BIOS that can control basic hardware or software operations, such as interactions with peripheral components or devices.

[0192] The processor 640 may include intelligent hardware devices (e.g., general-purpose processors, DSPs, CPUs, microcontrollers, ASICs, FPGAs, programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or any combination thereof). In some cases, the processor 640 may be configured to operate a memory array using a memory controller. In other cases, the memory controller may be integrated into the processor 640. The processor 640 may be configured to execute computer-readable instructions stored in memory 635 to perform various functions (e.g., functions or tasks supporting methods and systems for sleep staging algorithms).

[0193] For example, the wearable application 620 may be configured as a means for receiving user-associated baseline physiological data from at least one wearable device, or may otherwise support it. The wearable application 620 may be configured as a means for obtaining user-associated physiological baselines based at least in part on user-associated baseline physiological data, or may otherwise support it, where the physiological baselines provide reference user arousal data. The wearable application 620 may be configured as a means for receiving additional user-associated physiological data from at least one wearable device, or may otherwise support it. The wearable application 620 may be configured as a means for obtaining current user arousal data based at least in part on additional user-associated physiological data, or may otherwise support it. The wearable application 620 may be configured as a means for identifying trigger conditions to provide user-associated physiological state-related insights related to vehicle-related mobility events, based at least in part on a comparison between current user arousal data and reference user arousal data. The wearable application 620 may be configured as a means of causing a user device to provide physiological state-related insights associated with the user, or it may support it in a different way.

[0194] The wearable application 620 may include an application (e.g., "app"), program, software, or other components configured to facilitate communication with the ring 104, server 110, other user devices 106, etc. For example, the wearable application 620 may include an application executable on user device 106 that is configured to receive data (e.g., physiological data) from the ring 104, perform processing operations on the received data, send and receive data with server 110, and present data to user 102.

[0195] Figure 7 shows a flowchart illustrating a method for supporting a technique for providing physiological state-related insights associated with a user, according to aspects of this disclosure. The operation of Method 700 may be implemented by a user device or its components, as described herein. For example, the operation of the Method may be performed by a user device, as described with reference to Figures 1 to 6. In some examples, the user device may execute a set of instructions to control functional elements of the user device to perform the described functions. In addition or alternatively, the user device may perform aspects of the described functions using dedicated hardware.

[0196] In 700, the method may include the step of receiving user-associated baseline physiological data from at least one wearable device. The operation of 700 may be performed according to the examples disclosed herein. In some examples, the operation of 700 may be performed by the data acquisition component 420, as described with reference to Figure 4.

[0197] In 705, the method may include the step of obtaining a user-associated physiological baseline based at least in part on baseline physiological data associated with the user, the physiological baseline providing reference user arousal data. The operation of 705 may be performed according to the examples disclosed herein. In some examples, the operation of 705 may be performed by the physiological baseline acquisition component 425, as described with reference to Figure 4.

[0198] In 710, the method may include the step of receiving additional physiological data associated with the user from at least one wearable device. The operation of 710 may be performed according to the examples disclosed herein. In some examples, the operation of 710 may be performed by the data acquisition component 420, as described with reference to Figure 4.

[0199] In 715, the method may acquire current user arousal data based at least in part on additional physiological data associated with the user. The operation of 715 may be performed according to the examples disclosed herein. In some examples, the operation of 715 may be performed by the current user arousal data acquisition component 430, as described with reference to Figure 4.

[0200] In 720, the method may include, at least in part, identifying trigger conditions for providing physiological state-related insights associated with a user in relation to a mobility event involving at least one vehicle, based on a comparison of current user arousal data with reference user arousal data. The operation of 720 may be performed according to the examples disclosed herein. In some examples, the operation of 720 may be performed by the trigger condition identification component 435, as described with reference to Figure 4.

[0201] In 725, the method may include the step of causing a user device to provide physiological state-related insights associated with the user. The operation of 725 may be performed according to the examples disclosed herein. In some examples, the operation of 725 may be performed by the physiological state-related insights providing component 440, as described with reference to Figure 4.

[0202] The methods described above illustrate possible implementations, and it should be noted that the operations and steps may be rearranged or modified in other ways, and other implementations are possible. Furthermore, two or more aspects of the methods may be combined.

[0203] Figure 8 shows a block diagram illustrating the relationship between measured data attributes and derived data attributes according to an aspect of this disclosure.

[0204] The measured values ​​may include, for example, heart rate interval (IBI), physical activity (intensity, duration, time), skin temperature, current time and time of day, ambient light exposure, mental load, and meals. The derived attributes may include, for example, heart rate, resting heart rate, respiratory rate, sleep phase, sleep duration, bedtime, and activity preference. Further derived attributes may include mid-sleep point, sleep onset latency, voluntary wake-up time, circadian type (morning / evening), sleep-driven curve, synchronization effect index, readiness, circadian rhythm, circadian arousal curve, and sleepiness index. In some embodiments, arousal data may be obtained based at least partially on one or more of these measured values ​​and attributes.

[0205] The method is described below. The method includes the steps of: receiving user-associated baseline physiological data from at least one wearable device; obtaining user-associated physiological baselines based at least in part on the user-associated baseline physiological data, wherein the physiological baselines provide reference user arousal data; receiving user-associated additional physiological data from at least one wearable device; obtaining current user arousal data based at least in part on the user-associated additional physiological data; identifying trigger conditions for providing user-associated physiological state-related insights related to a mobility event involving at least one vehicle, based at least in part on a comparison between the current user arousal data and reference user arousal data; and causing a user device to provide user-associated physiological state-related insights.

[0206] The apparatus is described below. The apparatus may include a processor, memory coupled to the processor, and instructions stored in the memory. Instructions may be executable by the processor and cause the apparatus to perform the following steps: receiving user-associated baseline physiological data from at least one wearable device; obtaining a user-associated physiological baseline, at least in part on the user-associated baseline physiological data, wherein the physiological baseline provides reference user arousal data; receiving additional user-associated physiological data from at least one wearable device; obtaining current user arousal data, at least in part on the additional user-associated physiological data; identifying trigger conditions for providing user-associated physiological state-related insights related to at least vehicle-involved mobility events, at least in part on a comparison of the current user arousal data and reference user arousal data; and causing a user device to provide user-associated physiological state-related insights.

[0207] The present invention describes a non-temporary computer-readable medium for storing code. The code may include instructions executable by a processor to perform the following steps: receiving user-associated baseline physiological data from at least one wearable device; obtaining a user-associated physiological baseline, at least in part on the user-associated baseline physiological data, wherein the physiological baseline provides reference user arousal data; receiving additional user-associated physiological data from at least one wearable device; obtaining current user arousal data, at least in part on the additional user-associated physiological data; identifying trigger conditions for providing user-associated physiological state-related insights related to a travel event involving at least one vehicle, at least in part on a comparison between the current user arousal data and reference user arousal data; and causing a user device to provide user-associated physiological state-related insights.

[0208] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the step of identifying a trigger condition further includes identifying a trigger condition when the current user arousal data differs from reference user arousal data by a predetermined threshold amount.

[0209] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, a travel event is a future travel event.

[0210] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, the move event is the current move event.

[0211] In some examples of the methods, apparatus, and non-temporary computer-readable media described herein, additional physiological data includes acceleration sensor data and further comprises the steps of identifying that a user is driving at least one vehicle, at least in part on a comparison of acceleration sensor data with reference acceleration sensor data, and detecting a movement event, at least in part on a comparison.

[0212] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for performing the steps of establishing an active local communication link between a user device and at least one vehicle, and detecting a movement event, at least in part, based on the presence of the active local communication link between the user device and at least one vehicle.

[0213] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for performing the steps of receiving satellite positioning data from a user device and detecting a movement event based at least in part on the satellite positioning data from the user device.

[0214] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for transmitting instructions to at least one wearable device in response to the detection of a movement event, applying a predetermined rate for transmitting physiological data.

[0215] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include actions, features, means, or instructions for transmitting physiological state-related insights to at least one vehicle via an active local communication link between a user device and at least one vehicle.

[0216] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include actions, features, means, or instructions for displaying physiological state-related insights on a graphical user interface of a user device.

[0217] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include actions, features, means, or instructions for causing a user device to provide at least one of the auditory, tactile, and visual alerts associated with the physiological state-related insights.

[0218] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include actions, features, means, or instructions for performing the steps of: receiving user-associated vehicle data from at least one vehicle, wherein the user-associated vehicle data includes user behavior data collected during a travel event; and identifying trigger conditions based at least in part on the user-associated vehicle data.

[0219] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for performing the steps of: receiving meteorological data associated with a travel event; determining, based on the meteorological data associated with the travel event, that the meteorological data associated with the travel event affects the travel event; and identifying trigger conditions, at least in part, based on the meteorological data.

[0220] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for performing the steps of: receiving calendar data associated with a travel event; determining, based on the calendar data associated with the travel event, that the travel event is a future travel event; and identifying trigger conditions, at least in part, based on this determination.

[0221] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for performing the steps of: receiving satellite positioning data related to a travel event from a user device; determining, based on the satellite positioning data related to the travel event, that the travel event is related to an unfamiliar route of the user; and identifying trigger conditions, at least in part, based on the determination.

[0222] Some examples of methods, apparatus, and non-temporary computer-readable media described herein may further include operations, features, means, or instructions for performing the steps of receiving route schedule data associated with a travel event and identifying trigger conditions based at least in part on the route schedule data.

[0223] The descriptions provided herein in relation to the accompanying drawings describe exemplary configurations and do not represent all possible implementations or examples within the claims. The term “exemplary” as used herein means “serving as an example, case, or illustration,” and does not imply “preferred” or “advantageous over other examples.” Detailed descriptions include specific details to give an understanding of the techniques described. However, these techniques may be implemented without these specific details. In some cases, well-known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0224] In the accompanying drawings, similar components or features may have the same reference label. Furthermore, various components of the same type can be distinguished by adding a dash after the reference label and a second label that distinguishes them from similar components. Where only the first reference label is used herein, its description is applicable to any one of the similar components having the same first reference label, regardless of the second reference label.

[0225] The information and signals described herein may be represented using any of the various different techniques and methods. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0226] The various exemplary blocks and modules described in relation to the disclosure herein may be implemented or executed using general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors working with a DSP core, or any other such configuration).

[0227] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. When implemented in software executed by a processor, the functions may be stored on or transmitted via computer-readable media as one or more instructions or codes. Other examples and implementations are also within the scope of this disclosure and the accompanying claims. For example, depending on the nature of the software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwiring, or any combination thereof. Features implementing the functions may also be physically arranged in various locations, including being distributed so that parts of the functions are implemented in different physical locations. Furthermore, as used herein, including in the claims, "or" in a list of items (e.g., a list of items preceded by phrases such as "at least one of" or "one or more of") indicates a comprehensive list, for example, such that a list of at least one of A, B, or C means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Also, as used herein, the phrase "based on" should not be interpreted as a reference to a closed set of conditions. For example, an exemplary step described as "based on Condition A" may be based on both Condition A and Condition B without departing from the scope of this disclosure. In other words, as used herein, the phrase "based on" should be interpreted in the same way as the phrase "based on at least in part."

[0228] Computer-readable media include both non-temporary computer storage media and communication media, including any media that enables the transfer of computer programs from one location to another. Non-temporary storage media may be any available media that can be accessed by a general-purpose or dedicated computer. Examples, rather than limitations, include RAM, ROM, electrically erasable programmable ROM (EEPROM), compact disk (CD) ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-temporary media that can be used to carry or store desired program code means in the form of instructions or data structures, and can be accessed by a general-purpose or dedicated computer or general-purpose or dedicated processor. Any connection is also appropriately referred to as computer-readable media. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of media. The terms "disk" and "disc" as used herein include CDs, LaserDiscs®, Optical Discs, Digital Multipurpose Discs (DVDs), Floppy Disks, and Blu-ray Discs. A disk typically reproduces data magnetically, while a disc reproduces data optically using a laser. Combinations of these also fall within the scope of computer-readable media.

[0229] The descriptions herein are provided to enable those skilled in the art to create or use this disclosure. Various modifications to this disclosure will be readily apparent to those skilled in the art, and the comprehensive principles defined herein may be applied to other variations without departing from the scope of this disclosure. Accordingly, this disclosure should be given in the broadest possible scope that is consistent with the principles and novel features disclosed herein, and is not limited to the examples and designs described herein.

Claims

1. A method for providing physiological state-related insights associated with a user, A step of receiving user-associated baseline physiological data from at least one wearable device, A step of obtaining a physiological baseline associated with the user, based at least in part on the baseline physiological data associated with the user, wherein the physiological baseline provides reference user arousal data. The steps include receiving additional physiological data associated with the user from at least one wearable device, The steps include obtaining current user arousal data based at least in part on the additional physiological data associated with the user, The steps include identifying trigger conditions for providing physiological state-related insights associated with the user in relation to a travel event involving at least one vehicle, based at least partially on a comparison between the current user arousal data and the baseline user arousal data, The steps include causing the user device to provide the physiological state-related insights associated with the user, Methods that include...

2. The step of identifying the trigger condition is: When the current user arousal data differs from the reference user arousal data by a predetermined threshold amount, the trigger condition is identified. The method according to claim 1, further comprising:

3. The method according to claim 1, wherein the aforementioned movement event is a future movement event.

4. The method according to claim 1, wherein the aforementioned movement event is the current movement event.

5. The additional physiological data includes acceleration sensor data, and the method is A step of identifying that the user is driving the at least one vehicle, based at least in part on a comparison between the acceleration sensor data and the reference acceleration sensor data, The steps include detecting the movement event based at least partially on the comparison, The method according to claim 4, further comprising:

6. The steps include establishing an active local communication link between the user device and the at least one vehicle, The steps include detecting the movement event, at least partially based on the existence of the active local communication link between the user device and the at least one vehicle, The method according to claim 4, further comprising:

7. The steps include receiving satellite positioning data from the user device, The steps include detecting the movement event based at least partially on the satellite positioning data from the user device, The method according to claim 4, further comprising:

8. Step 1: In response to the detection of the movement event, send a command to at least one wearable device to apply a predetermined rate for transmitting physiological data. The method according to claim 5, further comprising:

9. The method according to claim 6, wherein the step of causing the user device to provide the physiological state-related insights associated with the user includes transmitting the physiological state-related insights to the at least one vehicle via the active local communication link between the user device and the at least one vehicle.

10. The method according to claim 1, wherein the step of causing the user device to provide the physiological state-related insights associated with the user includes displaying the physiological state-related insights on the graphical user interface of the user device.

11. The method according to claim 1, wherein the step of causing the user device to provide the physiological state-related insights associated with the user includes causing the user device to provide at least one of the auditory alerts, tactile alerts, and visual alerts associated with the physiological state-related insights.

12. A step of receiving vehicle data associated with the user from at least one vehicle, wherein the vehicle data associated with the user includes user behavior data collected during the travel event. It further includes, The method according to claim 3, wherein the step of identifying the trigger condition includes identifying the trigger condition at least in part based on the vehicle data associated with the user.

13. The steps include receiving weather data associated with the aforementioned travel event, A step of determining, based on the weather data associated with the movement event, that the weather data associated with the movement event has an effect on the movement event. It further includes, The method according to claim 1, wherein the step of identifying the trigger condition includes identifying the trigger condition based at least in part on the weather data.

14. The steps include receiving calendar data associated with the aforementioned travel event, The steps include determining that the travel event is a future travel event based on the calendar data associated with the travel event, It further includes, The method according to claim 1, wherein the step of identifying the trigger condition includes identifying the trigger condition based at least in part on the determination.

15. The steps include receiving satellite positioning data associated with the aforementioned movement event from the user device, The steps include determining, based on the satellite positioning data associated with the movement event, that the movement event is related to a route the user has not yet experienced, It further includes, The method according to claim 1, wherein the step of identifying the trigger condition includes identifying the trigger condition based at least in part on the determination.

16. Steps to receive route schedule data associated with the aforementioned travel event. It further includes, The method according to claim 1, wherein the step of identifying the trigger condition includes identifying the trigger condition based at least in part on the route schedule data.

17. A device for providing physiological state-related insights associated with a user, Processor and The memory coupled to the aforementioned processor, Instructions stored in the memory and executable by the processor, The device is equipped with the command, A step of receiving user-associated baseline physiological data from at least one wearable device, A step of obtaining a physiological baseline associated with the user, based at least in part on the baseline physiological data associated with the user, wherein the physiological baseline provides reference user arousal data. The steps include receiving additional physiological data associated with the user from at least one wearable device, The steps include obtaining current user arousal data based at least in part on the additional physiological data associated with the user, The steps include identifying trigger conditions for providing physiological state-related insights associated with the user in relation to a travel event involving at least one vehicle, based at least partially on a comparison between the current user arousal data and the baseline user arousal data, The steps include causing the user device to provide the physiological state-related insights associated with the user, A device that performs an action.

18. A non-temporary computer-readable medium for storing a code, wherein the code is A step of receiving user-associated baseline physiological data from at least one wearable device, A step of obtaining a physiological baseline associated with the user, based at least in part on the baseline physiological data associated with the user, wherein the physiological baseline provides reference user arousal data. The steps include receiving additional physiological data associated with the user from at least one wearable device, The steps include obtaining current user arousal data based at least in part on the additional physiological data associated with the user, The steps include identifying trigger conditions for providing physiological state-related insights associated with the user in relation to a travel event involving at least one vehicle, based at least partially on a comparison between the current user arousal data and the baseline user arousal data, The steps include causing the user device to provide the physiological state-related insights associated with the user, A non-temporary computer-readable medium containing instructions that can be executed by a processor in order to perform that task.

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