Wearable device for sleep application and operating system and method thereof

Abstract

Embodiments described herein relate to a wearable device for monitoring sleep indicators of a user and adjusting sleep conditions or environment of the user. In some embodiments, the wearable device may include earplugs worn within each ear of a user, each earplug including one or more sensors for measuring biometric data related to sleep of the user. The wearable device may interface and / or communicate with a secondary device. In some embodiments, the auxiliary device may be a charging box capable of charging earplugs. Biometric data collected using the wearable device may be used to determine sleep information, including sleep states and / or sleep stages of the user. In some embodiments, the audio output of the wearable device may be adjusted to improve the sleep condition of the user. In some embodiments, in addition to or in lieu of the wearable device, a bedside speaker may be used to improve sleep of a user.

Description

[0001] Citations of cross-related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 533,019, filed August 16, 2023, entitled “Wearable Device for Sleep Applications, and Systems and Methods for Operation thereof,” the disclosure of which is incorporated herein by reference. Technical Field

[0003] The embodiments described herein generally relate to devices, systems, and methods for sleep applications, and more specifically to devices, systems, and methods for monitoring and analyzing sleep-related metrics of a user and adjusting the user's sleep environment in order to, for example, improve the user's sleep quality. Background Technology

[0004] Sleep plays an indispensable role in an individual's overall health and quality of life. Research shows that sleep deprivation negatively impacts cognitive function, metabolism, and immune function, and can contribute to a variety of chronic health conditions, including heart disease, high blood pressure, diabetes, and depression. Despite this, sleep deprivation has become a global problem, with many people around the world experiencing nighttime sleep disorders and / or at least one condition that can affect sleep quality, such as insomnia, tinnitus, or obstructive sleep apnea (OSA). The most accurate sleep monitoring systems, such as polysomnography (PSG), require users to wear multiple wired sensors on their skin, nose, fingers, and around their chest and abdomen, which can be uncomfortable and / or inconvenient. However, sleep monitoring systems that offer more convenient sleep tracking often lack accuracy and / or detail in the sleep metrics obtained. Furthermore, sleep interventions aimed at improving sleep quality typically involve supplement use (e.g., melatonin) or behavioral changes (e.g., turning off the lights earlier, limiting device use, and restricting caffeine and alcohol intake). However, these interventions lack personalized programs to improve a user's sleep. Therefore, it is necessary to focus on accuracy and comfort when monitoring users' sleep, and to adopt personalized solutions for devices, systems, and methods to improve users' sleep quality. Summary of the Invention

[0005] The embodiments described herein relate to devices, systems, and methods for monitoring sleep-related metrics of a user and adjusting the user's sleep environment in order to, for example, improve the user's sleep quality.

[0006] In some aspects, wearable devices include a housing configured to be worn inside a user's ear; sensors disposed on or within the housing, the sensors being configured to measure the user's body metrics; a speaker configured to deliver audio output to the user's ear; a processor configured to: receive data associated with the body metrics from the sensors; and control the speaker to deliver audio output; and a communication interface configured to send the data associated with the body metrics to an external device, such that the external device, in response to receiving the data associated with the body metrics, is configured to determine characteristics of the user's sleep periods.

[0007] In some aspects, the wearable device includes a processor, sensors configured to measure a user's body metrics, a speaker configured to send audio output to the user's ear to help the user fall asleep or maintain sleep, and a communication interface configured to bidirectionally transmit information with one or more assistive devices; the one or more assistive devices being configured to determine the user's sleep stage, and the wearable device being configured to be worn inside the user's ear.

[0008] In some aspects, a system includes a wearable device configured to be worn in a user's ear, the wearable device comprising: a first set of one or more sensors configured to measure the user's body metrics; a speaker configured to deliver audio output to the user's ear; and a processor operatively coupled to the sensors and the speaker, the processor being configured to receive data from the one or more sensors and control the speaker to deliver the audio output; and a charging device configured to charge the wearable device when coupled to the charging device, the charging device including a second set of one or more sensors configured to measure environmental metrics of the user's environment.

[0009] In some aspects, a system includes a wearable device configured to be worn in a user's ear, and an assistive device including one or more sensors configured to measure one or more indicators from the user's environment; the wearable device includes a processor, sensors configured to measure body indicators from within the user's ear, a speaker configured to transmit audio output to the user's ear, and a communication interface configured to communicate bidirectionally with the assistive device.

[0010] In some aspects, one method includes measuring one or more bodily metrics using sensors of a wearable device worn in the user's ear; determining the user's sleep stage based on the one or more bodily metrics; generating an audio output based on the user's sleep stage; and outputting the audio output via a speaker of the wearable device.

[0011] In some aspects, a method may include measuring one or more bodily metrics using sensors included in a wearable device configured to be worn in a user's ear; determining a user's sleep stage based on the measured bodily metrics; and adjusting audio output from a speaker included in the wearable device based on the user's sleep stage. In some embodiments, the method may further include calculating a user's rapid movement index (REM), slow movement index (SLIM), and respiratory rate using the measured bodily metrics. In some embodiments, the method may further include displaying the user's sleep stage on a user device. In some embodiments, the method may further include measuring metrics from the environment in which the user sleeps and adjusting the audio output from the speaker based on the measured metrics from the environment. Attached Figure Description

[0012] Figure 1 This is a schematic block diagram of a system for sleep applications according to an embodiment.

[0013] Figure 2 A device network, including systems and devices configured to implement one or more sleep applications, is schematically depicted according to an embodiment.

[0014] Figures 3A-3C schematically depict embodiments of a system for a sleep application according to an embodiment. Figures 3A-3B schematically depict earplugs and a charging case; Figure 3C schematically depicts a bedside speaker for a sleep application.

[0015] Figure 4 This is a flowchart of a method for determining rapid movement indicators, slow movement indicators, and respiratory rate using measurement results from a system for sleep applications, according to an embodiment.

[0016] Figure 5-9 This is a schematic block diagram of an embodiment regarding the distribution of computational load across various components of a system used for sleep applications, according to an embodiment.

[0017] Figure 10 This is an exploded view of a wearable device for sleep applications according to an embodiment.

[0018] Figure 11 This is an exploded view of the charging case of a wearable device for sleep applications according to an embodiment.

[0019] Figure 12 A wearable device for sleep applications, placed in a charging case according to an embodiment, is described. Detailed Implementation

[0020] The embodiments described herein relate to devices, systems, and methods for sleep applications. In particular, the embodiments described herein relate to wearable devices for monitoring sleep-related metrics of a user and / or adjusting the user's sleep conditions or environment, as well as methods and systems for operating such devices.

[0021] Humans need sleep to survive, and sleep deprivation can adversely affect many aspects of human health, including cognitive function, metabolism, immune function, and more. Monitoring an individual's sleep can be challenging due to the trade-off between the accuracy and comfort of the monitoring system. A sleep monitoring system should be comfortable to avoid further disturbing the user's sleep; however, it should also be accurate to inform the user about their sleep patterns precisely. Each person has a unique circadian rhythm, sleep schedule, and sleep environment, highlighting the need for personalized approaches to improve sleep quality. Current sleep interventions lack personalization and cannot continuously adjust a user's sleep conditions throughout the night. The embodiments described herein address these shortcomings by enabling comfortable and accurate monitoring of a user's sleep and adjusting their sleep conditions to improve sleep quality.

[0022] In some embodiments, the wearable device may include earplugs worn in each of a user's ears. Each earplug may include one or more sensors, a processor, memory, one or more input / output (I / O) devices, and one or more communication interfaces for measuring biometric data related to the user's sleep. In some embodiments, the wearable device may interface with and / or communicate with an assistive device. In some embodiments, the assistive device may be a charging case capable of charging the earplugs. Biometric data collected using the wearable device may be used to determine the user's sleep information, including the user's sleep state (sleep / wake) and / or the sleep stage the user is in at a given time point (i.e., N1 or first stage, N2 or second stage, N3 or third stage, REM). In some embodiments, the sleep information may be displayed on a user device (e.g., a mobile phone, tablet, local computer, laptop, etc.). In some embodiments, the sleep information and other information collected and / or calculated by the wearable device and / or assistive device may be transmitted to a server and stored. In some embodiments, the system may include a speaker (e.g., a bedside speaker) or other audio device configured to output audio to the user's sleep environment. In some embodiments, the speaker or other audio device may include any necessary sensors, processors, memory, and communication interfaces to monitor and / or adjust the user's sleep environment. In some embodiments, the speaker may interface with and / or communicate with wearable devices, assistive devices, or user equipment.

[0023] Data collection and computational workloads can be distributed across various devices within the system. In some embodiments, the earbuds can be used to collect raw data and determine body metrics from it; the charging case can be used to analyze body metrics to determine sleep state (sleep / wake); the user device can be used to analyze body metrics to determine sleep stages and display sleep information; and a server can be used to store sleep stage history and other information. In some embodiments, the charging case can be used to directly determine sleep stages, and the user device can be used to display information to the user. In some embodiments, the earbuds can collect only raw data, the charging case can determine body metrics, and then use these body metrics to determine the user's sleep stage. In some embodiments, the body metrics used to determine the user's sleep stage may include the user's rapid movement index (REM), slow movement index (SLIM), and / or respiratory rate.

[0024] In some embodiments, the earbuds may be configured to output audio directly to the user's ears. In some embodiments, the earbuds may output audio designed to improve relaxation or help the user fall asleep (e.g., relaxation content, meditation content, etc.) and / or maintain sleep (e.g., masking content such as pink noise). In some embodiments, the earbuds may output audio specific to the user's sleep stage (the sleep stage determined based on bodily indicators collected by the earbuds). In some embodiments, the audio output may be processed and / or tuned. The computational load of processing and / or tuning the audio output may be distributed across various devices in the system. For example, in some embodiments, the user device (e.g., a mobile phone, tablet, etc.) may process and / or tune the audio before transmitting the audio output information through the assistive device to the wearable device. The wearable device may also process and / or tune (e.g., bass boost, volume reduction / increase) the audio output. In some embodiments, the wearable device may process and / or tune the audio output according to sensor data collected by the system. In some embodiments, both the assistive device and the wearable device may process and / or tune the audio output (e.g., bass boost, volume reduction / increase) based on sensor data collected by the system.

[0025] In some embodiments, the assistive device may collect data related to the user's sleep environment and determine an environment score that indicates how suitable the environment is for sleep. In some embodiments, the audio output of the wearable device may be adjusted according to the environment score (e.g., increasing the volume of calming noise, outputting specific sound frequencies, etc.) to improve the user's sleep conditions.

[0026] The system can be used for interventions to help treat medical conditions including insomnia, tinnitus, and sleep apnea. In some embodiments, the wearable device can be used to detect bodily indicators related to breathing and / or snoring during sleep to detect the likelihood that a user is experiencing obstructive sleep apnea (OSA). In some embodiments, the wearable device can be used to improve tinnitus symptoms through brain training using the wearable device.

[0027] Figure 1 This is a schematic block diagram of a system 100 for a sleep application according to an embodiment. As shown, system 100 includes one or more wearable devices 110 and one or more auxiliary devices 120. Wearable device 110 may have sensors 112 (including one or more inertial measurement unit (IMU) sensors 112a), and optionally includes one or more photoplethysmography (PPG) sensors 112b and one or more other sensors 112c. Wearable device 110 may include one or more processors 114, memory 116, one or more I / O devices 118, and one or more communication interfaces. In some embodiments, wearable device 110 may be configured to interface with and / or communicate with auxiliary device 120. Auxiliary device 120 may include one or more processors 124, memory 126, one or more I / O devices 128, one or more communication interfaces 129, and may optionally include a power supply 121 and one or more sensors 122.

[0028] In some embodiments, wearable device 110 may include earplugs configured to be worn in a user's ear, and assistive device 120 may include a case configured to charge the earplugs. The earplugs may have noise-masking properties to create quieter sleep conditions for the user. In some embodiments, the design of wearable device 110 may result in passive noise masking by forming a close fit with the user's ear. In some embodiments, audio output from wearable device 110 may result in active noise masking by outputting audio at a higher sound level than the surrounding environment.

[0029] Sensors 112 in the wearable device 110 can be configured to measure physiological or biometric data from a user. For example, in some embodiments, an IMU sensor 112a can be used to measure acceleration in three dimensions (x, y, z) of a part of the user's body (e.g., the ear and / or head). A PPG sensor 112b can be used to measure changes in blood volume in a part of the user's tissue. In some embodiments, the wearable device 110 includes two earplugs, each including an IMU sensor 112a and / or a PPG sensor 112b, allowing more than one time series of each biometric to be acquired to improve measurement accuracy. In some embodiments, other sensors 112c may be included to enhance the measurement of the same biometric data as the IMU sensor 112a and / or the PPG sensor 112b. In some embodiments, other sensors 112c may be included to measure different biometrics. In some embodiments, other sensors 112c may include a microphone, a body temperature sensor, a pulse oximeter, a skin conductance sensor (GSR), one or more electroencephalogram (EEG) sensors, etc. In some embodiments, sensor 112 may be configured to measure biometrics related to whether a user may have abnormal breathing patterns or snore during sleep. In some embodiments, wearable device 110 may transmit information corresponding to the biometrics measured from sensor 112 to assistive device 120 via communication interface 119 for further processing. In some embodiments, wearable device 110 may transmit information corresponding to the measured biometrics to processor 114 via communication interface 119 for onboard biometric processing.

[0030] Processors 114 and 124 can be any suitable processing device configured to run and / or execute a set of instructions or code. For example, processors 114 and 124 can be and / or may include one or more data processors, image processors, graphics processing units (GPUs), physical processing units, digital signal processors (DSPs), analog signal processors, mixed signal processors, machine learning processors, deep learning processors, finite state machines (FSMs), compression processors (e.g., data compression to reduce data rates and / or memory requirements), encryption processors (e.g., for secure wireless data and / or power delivery), and / or the like. Processors 114 and 124 can be, for example, general-purpose processors, central processing units (CPUs), microprocessors, microcontrollers, edge AI processors, edge machine learning processors, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), processor boards, virtual processors, and / or the like. Processors 114 and 124 can be configured to run and / or execute or implement software application processes and / or other modules, processes, and / or functions related to monitoring and improving sleep. The underlying device technology can be provided in a variety of component types, such as metal-oxide-semiconductor field-effect transistor (MOSFET) technology (e.g., complementary metal-oxide-semiconductor (CMOS)), bipolar technology (e.g., generative adversarial networks (GAN)), polymer technology (e.g., silicon-conjugated polymers and metal-conjugated polymer-metal structures), analog and digital hybrid technologies and / or the like.

[0031] In some embodiments, the processor 114 in the wearable device 110 may be configured to perform any suitable operations for acquiring data collected from the sensor 112; processing and / or analyzing the data collected from the sensor 112; and / or sending the raw and / or analyzed data to other devices (i.e., the auxiliary device 120) via the communication interface 119. Alternatively or additionally, the processor 114 may be configured to send data from the sensor 112 and / or memory 116 to one or more external or remote devices (e.g., via a network or cloud) for further processing and / or analysis, which will combine Figure 2A more detailed description follows. In some embodiments, processor 114 may be configured to transmit signals to sensor 112, I / O device 118, communication interface 119, and / or other elements of wearable device 110 to activate and / or control the operation of these elements. For example, in some embodiments, processor 114 may be configured to perform operations for obtaining audio output information from an external device (e.g., auxiliary device 120) and for sending audio output information to I / O device 118. Although not depicted, wearable device 110 may also include an onboard power supply or a power source operatively coupled to any component of wearable device 110 that may be configured to power the wearable device 110. In some embodiments, wearable device 110 may include one or more portable power sources, such as batteries, which may be rechargeable, for example, via interaction with one or more auxiliary devices 120 (e.g., charging cases).

[0032] Processor 114 may be configured to run algorithms or operations to analyze raw sensor data acquired by sensor 112 to determine one or more bodily metrics related to the user's sleep. In some embodiments, processor 114 may use acceleration data collected via IMU sensor 112a to determine the user's body movements (e.g., rapid movement index and slow movement index) and the user's respiratory rate. Processor 114 may transmit information related to one or more bodily metrics (e.g., the user's rapid movement index, slow movement index, and respiratory rate) to assistive device 120. Processor 124 in assistive device 120 may be configured to perform any suitable operations for acquiring data collected from sensor 112; analyzing data collected from sensor 112; and / or transmitting raw and / or analyzed data to other devices (i.e., user devices or servers). In some embodiments, certain aspects of processor 124 may be functionally and / or structurally similar to processor 114; therefore, certain aspects of processor 124 will not be further described herein.

[0033] In some embodiments, processor 124 may obtain raw sensor data from wearable device 110 and run algorithms or operations to analyze the raw sensor data to determine one or more bodily metrics (e.g., the user's rapid movement index, slow movement index, and respiratory rate). In some embodiments, processor 124 may directly receive one or more bodily metrics from wearable device and use said one or more bodily metrics to determine the user's sleep information, including the user's sleep state (sleep / wake) and / or the user's sleep stage at a given time point (i.e., N1, N2, N3, REM). In some embodiments, processor 114 and / or processor 124 may be configured to analyze raw sensor data to determine whether the user exhibits abnormal breathing during sleep. For example, in some embodiments, IMU sensor 112a may send acceleration data to processors 114, 124 and / or a microphone may send auditory data to processors 114, 124. In some embodiments, data from a pulse oximeter sensor in wearable device 110 may be sent to processors 114, 124. Processor 114 and / or processor 124 may run algorithms or operations to use sensor data to detect whether the user's breathing is abnormal and / or whether the user is snoring, thereby estimating the probability that the user is experiencing sleep apnea (or OSA). In some embodiments, processor 114 and / or processor 124 may send information related to the user's breathing and / or snoring to a user device (e.g., a mobile phone) to alert the user to possible sleep apnea symptoms.

[0034] In some embodiments, the wearable device 110 may store raw sensor data and / or processed data (e.g., determination of body metrics) in memory 116. Memory 116 on the wearable device 110 may be any suitable storage device configured to store data, information, computer code or instructions (such as those described herein) and / or the like. Memory 116 may store (1) audio output information downloaded from external devices (e.g., assistive device 120, user equipment, server, etc.); (2) data collected from sensor 112; and (3) any other information related to the operation of system 100. In some embodiments, memory 116 may be and / or may include one or more of the following: random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), memory buffer, erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), read-only memory (ROM), flash memory, volatile memory, non-volatile memory, combinations thereof, and the like. In some embodiments, memory 116 may store instructions to cause processor 114 to execute modules, processes, and / or functions associated with monitoring and improving a user's sleep. In some embodiments, memory 116 may also be configured to at least temporarily store sensor 112 data, for example until the data is sent to an assistive device, a remote server, or other device (e.g., a user device).

[0035] Additionally and / or alternatively, auxiliary device 120 may store raw sensor data (e.g., data collected by sensor 112 and / or sensor 122) and processed data in memory 126. In some embodiments, certain aspects of memory 126 may be functionally and / or structurally similar to memory 116; therefore, certain aspects of processor 126 will not be further described herein. Memory 126 may store (1) audio output information downloaded from external devices (e.g., user equipment, server, etc.); (2) data collected from sensors 112, 122; and (3) any other information related to the operation of system 100.

[0036] I / O devices 118, 128 may include any suitable device configured to receive input from a user or transmit output to a user. In some embodiments, I / O devices 118, 128 may include activation mechanisms or user-actuated elements (e.g., touch buttons, switches, etc.) to turn on or activate wearable device 110 or assistive device 120, respectively; or to allow the user to input information, request information, or initiate or stop audio output. In some embodiments, I / O devices 118, 128 may include speakers configured to output audio to a user when triggered by signals from processors 114, 124 and / or user input. In some embodiments, the speaker may, for example, include a balanced armature (BA) driver, a dynamic driver, or any suitable transducer for outputting audio. In some embodiments, I / O devices 118, 128 may include haptic feedback to allow the user to play audio output, pause audio output, or fast forward / rewind audio output. In some embodiments, the speaker may include pulse density modulation (PDM) driver circuitry or pulse code modulation (PCM) (e.g., internal integrated circuit sound (I-C)). 2 At least one of the S)) driving circuits. In some embodiments, the loudspeaker can use a PDM driving circuit and I) 2 Both S-drive circuits. In some embodiments, PDM and I are used. 2 Both can improve the fidelity of audio output, reduce artifacts, and / or increase volume.

[0037] Communication interface 119 may be any suitable device and / or interface capable of communicating with sensor 112, processor 114, memory 116, I / O device 118, or other components of wearable device 110, as well as networks (e.g., local area network (LAN), wide area network (WAN), or cloud) and / or external devices (e.g., auxiliary device 120 and / or user equipment, such as cellular phones, tablets, laptops, or desktop computers). Furthermore, communication interface 119 may include one or more wired and / or wireless interfaces, such as Ethernet interfaces, optical carrier (OC) interfaces, and / or asynchronous transfer mode (ATM) interfaces. In some embodiments, communication interface 122 may be, for example, a network interface card and / or the like, which may at least include an Ethernet port and / or a radio frequency unit (e.g., a Wi-Fi® radio frequency unit, a Bluetooth® radio frequency unit, a cellular unit such as 3G, 4G, 5G, 802.11X Zigbee, etc.). In some embodiments, communication interface 122 may include one or more satellite, Wi-Fi, Bluetooth, or cellular antennas. In some embodiments, communication interface 122 may be communicatively coupled to an external device (e.g., an external processor), which may include one or more satellite, Wi-Fi, Bluetooth, or cellular antennas, or a power source such as a battery or solar panel. In some embodiments, communication interface 119 may be a near-field communication (NFC) device, enabling the exchange of information with nearby electronic devices, such as assistive device 120. In some embodiments, communication interface 119 may be Bluetooth Low Energy (BLE) audio. In some embodiments, wearable device 110 includes a suitable combination of communication interfaces 119, such as BLE audio and NFC.

[0038] In some embodiments, communication interface 119 may be configured to receive biometric signals from sensor 112, processor 114, or other components of wearable device 110, and transmit these signals to external devices, for example, for further processing and / or analysis or presentation to a user. In some embodiments, communication interface 119 may also be configured to transmit signals to sensor 112, processor 114, or other components of wearable device, for example, to I / O device 118 and / or sensor 112. In some embodiments, communication interface 119 transmits information directly to communication interface 129 in auxiliary device 120. In some embodiments, certain aspects of communication interface 129 may be functionally and / or structurally similar to communication interface 119; therefore, certain aspects of communication interface 129 will not be further described herein.

[0039] In some embodiments, information related to the operation of system 100 (e.g., information related to audio output) may be downloaded from the user device and / or server to assistive device 120 and stored in memory 126 before the user goes to sleep. Information related to the operation of system 100 may be sent to wearable device 110 and stored in memory 116, allowing wearable device 110 to operate while the user is asleep (or lying down to sleep) without connection to any external device. In some embodiments, information stored in memory 126 of assistive device 120 may be sent to wearable device 110 via communication interfaces 119, 129 without using Wi-Fi, 3G, 4G, 5G, or any other form of internet connection. This feature encourages users to disconnect from the device before sleep, which has been shown to improve sleep quality. In some embodiments, information related to audio output may be downloaded directly from the user device and / or server to wearable device 110 and stored in memory 116 before the user goes to sleep (or lies down to sleep), allowing wearable device 110 to output audio without connection to the user device or server. In some embodiments, audio output can be streamed in real time from a user device or server, enabling the wearable device 110 to access a wider range of audio outputs at a given time, rather than just the audio outputs stored in memory 116 and / or 126.

[0040] In some embodiments, a user's sleep information can be used to adjust the output of the wearable device 110. In some embodiments, the processor 124 of the assistive device 120 can send signals associated with the user's sleep state and / or sleep stage to the processor 114 of the wearable device 110 to trigger the wearable device 110 to adjust the audio output from the I / O device 118 (e.g., a speaker, driver, etc.). For example, in some embodiments, the wearable device 110 may be configured to output relaxing content (e.g., natural sounds, meditation, etc.) from a speaker when the user is awake, and then automatically switch to masking content (e.g., pink noise, white noise, brown noise) once the user has transitioned to a sleep state to help the user stay asleep. In some embodiments, the wearable device 110 may be configured to output masking content from a speaker both when the user is awake and after the user has transitioned to a sleep state. In some embodiments, wearable device 110 may be configured to generate a “smart alarm clock”, wherein processor 114 may trigger I / O device 118 (e.g., speaker, driver, etc.) to automatically convert audio output to alarm clock output when the user transitions to a light sleep stage, such as stage one, stage two, or REM (as indicated by analysis of sensor data). The “smart alarm clock” can wake the user during a light sleep stage, causing the user to feel better rested for several minutes after waking compared to being woken from a deep sleep stage. In some embodiments, wearable device 110 and / or assistive device 120 may be equipped with a real-time clock, allowing wearable device 110 and / or assistive device 120 to output alarm clock output at a time selected by the user without connecting to a user device (e.g., the user's mobile phone) or a server. In some embodiments, the user device's mode may be switched according to the user's sleep stage. For example, in response to a signal that the user has entered a sleep state, the user device may switch to a “Do Not Disturb” or “Focus” mode. In some embodiments, a user may input rules or restrictions for interrupting and / or adjusting the mode of a user device (e.g., a user may choose to exit Do Not Disturb mode when receiving a call from one or more specific contacts).

[0041] In some embodiments, the output of wearable device 110 and / or assistive device 120 can be determined by environmental indicators of the user's sleep environment measured by sensors 112, 122. In some embodiments, sensors 112, 122 can sense environmental conditions, including but not limited to ambient light, ambient noise, humidity, air pressure, and ambient temperature. In some embodiments, assistive device 120 can be configured to send signals to control external devices in the environment based on measured environmental and / or physical indicators. For example, in response to a user transitioning to a sleep state (determined by wearable device 110 and / or assistive device 120), assistive device 120 can send signals to switch light sources in the room "on" or "off," or to close blinds. In some embodiments, assistive device 120 can send signals to a thermostat to raise and / or lower the ambient temperature according to the user's sleep / wake state. For example, assistive device 120 can send signals to a thermostat to lower the ambient temperature in response to a user transitioning to a sleep state, and to raise the ambient temperature when the user transitions to a light sleep stage and / or prepares to wake up. In some embodiments, the assistive device 120 may transmit signals via a Wi-Fi network to control external devices connected to the Wi-Fi network. In some embodiments, the assistive device 120 may be configured to transmit signals based on the user's sleep stage (N1, N2, N3, REM) to control external devices in the environment. In some embodiments, the assistive device 120 may be configured to determine an environmental score indicating how suitable the user's environment is for sleep. In some embodiments, the assistive device 120 may transmit signals according to the environmental score to control wearable devices and / or external devices (e.g., lowering the thermostat temperature, increasing the volume of sedative noise from the wearable device, outputting specific sound frequencies from the wearable device, etc.) to improve the user's sleep conditions. In some embodiments, the measured environmental indicators and / or environmental scores may be displayed to the user via a user device. In some embodiments, the correlation between one or more measured environmental indicators and one or more measured sleep indicators may be displayed to the user via a user device.

[0042] In some embodiments, the audio output from wearable device 110 and / or assistive device 120 can be automatically adjusted based on measured environmental conditions. For example, in some embodiments, the audio output from wearable device 110 and / or assistive device 120 can be adjusted as ambient noise changes, such that the audio output from wearable device 110 and / or assistive device 120 substantially masks the ambient noise. For example, if the ambient noise is high, the audio output from wearable device 110 and / or assistive device 120 can be increased, such that wearable device 110 and / or assistive device 120 substantially masks the ambient noise. In some embodiments, the audio output is maintained at a specific sound level above the ambient noise level. In some embodiments, the audio output from wearable device 110 and / or assistive device 120 can be reduced if an environmental auditory stimulus classified as a “critical” or “wake-up” stimulus (e.g., a baby crying, smoke / intrusion alarm, sunrise, or a user-selected “wake word”) is present. In some embodiments, sensor 122 may include a temperature sensor that measures ambient temperature, which may cause auxiliary device 120 to switch external devices in the environment, such as HVAC systems, fans, heating units, etc., to "on" or "off" to improve the user's sleep.

[0043] In some embodiments, system 100, including wearable device 110, can be used to help treat or alleviate tinnitus symptoms. Tinnitus is a condition in which a subject perceives a ringing or buzzing sound in one or both ears and may be associated with hearing loss. Tinnitus symptoms can be treated by brain training that removes one or more specific frequencies (i.e., frequencies perceived by the subject without stimulation) from the auditory stimuli presented to the user. Problem frequencies (or frequencies perceived by the subject without stimulation) can be determined by a healthcare provider by performing a test called “notching.” In some embodiments, wearable device 110 can aid in tinnitus diagnosis. In some embodiments, system 100 can be configured to perform an automated notching check to determine a user’s problem frequencies. The wearable device can be configured to output a frequency scan, wherein the wearable device outputs audio at different frequencies. Wearable device 110, assistive device 120, or user device can be configured to receive input from the user about which audio frequencies they can or cannot hear. Providing notching checks via system 100 would allow users to conveniently perform notching checks at home, thereby improving the accessibility of tinnitus diagnosis.

[0044] In some embodiments, system 100 may be configured to provide treatment to a user based on results from notch examination (e.g., notch examination performed by a healthcare provider or notch examination completed by system 100). In some embodiments, results from notch examination performed by system 100 may be manually or automatically incorporated into the programming of wearable device 110, enabling wearable device 110 to perform tinnitus treatment or treatment planning. In some embodiments, results from notch examination performed by system 100 may be transmitted between multiple devices (e.g., other user devices, third-party devices, such as devices used by healthcare professionals). In some embodiments, system 100 may be configured to receive information about a user's problem frequencies via user input. In some embodiments, the specified frequencies may be input by the user or a third-party individual to program audio output information into firmware on the balanced armature (BA) or driver / audio source of system 100. In some embodiments, system 100 may be configured to create "notches" in the frequency output from wearable device 110. System 100 may be configured to adjust the audio output from wearable device 110 by removing one or more of the user's problem frequencies from the audio output. For example, in some embodiments, if a user's tinnitus results in a persistent perception of a 10kHz tone, the wearable device 110 can be configured to remove the 10kHz tone from any audio output produced by the wearable device 110. This feature will reduce brain activity associated with the perception of a 10kHz tone through lateral inhibition, meaning that neurons responding to a 10kHz tone will be inhibited by the excitation of neighboring neurons (e.g., neurons responding to a 5kHz or 15kHz tone).

[0045] Although the systems, apparatuses, and methods described herein are for measuring, processing, and / or analyzing bodily and / or environmental indicators, it will be appreciated that such systems, apparatuses, and methods may also be used to measure, process, and / or analyze any number of bodily and / or environmental indicators, including multiple bodily and / or multiple environmental indicators.

[0046] Figure 2 A network for a sleep application according to an embodiment is schematically depicted. As shown, network 205 includes one or more wearable devices 210 and one or more assistive devices 220. Wearable devices 210 may be functionally and / or structurally similar to those referenced above. Figure 1 The wearable device 110 is described. For example, although not in Figure 2 While depicted in the above description, wearable device 210 may include sensors, processors, memory, I / O devices, or communication interfaces, which may be structurally and / or functionally identical to those components of wearable device 110. Auxiliary device 220 may be functionally and / or structurally similar to those described in the above reference. Figure 1 The described auxiliary device 120. For example, although not in Figure 2 As depicted herein, auxiliary device 220 may include sensors, processors, memory, I / O devices, communication interfaces, power supplies, and sensors, which may be structurally and / or functionally identical to those components of auxiliary device 120. Therefore, reference is no longer made to this description. Figure 2 More details about these components.

[0047] like Figure 2 As shown, wearable device 210 (e.g., similar to wearable device 110) and / or assistive device 220 (e.g., similar to assistive device 120) may be configured to communicate with computing devices, such as one or more user devices 260, one or more servers 270, and / or optionally one or more other devices 280 (e.g., healthcare provider devices, third-party devices). Wearable device 210 and / or assistive device 220 may be configured to communicate with such computing devices via network 205. Network 205 may include one or more networks, which may be implemented as wired and / or wireless networks and are used to operatively couple to any type of network (e.g., local area network (LAN), wide area network (WAN), virtual network, telecommunications network) of any computing device, including wearable device 210, assistive device 220, user device 260, server 270, database 260, and / or other computing device 280.

[0048] In some embodiments, wearable device 210 may be configured to send data (e.g., sleep-related biometric data collected by sensors in wearable device 210) to one or more assistive devices 220, user devices 260, servers 270, and / or one or more other devices 280. In some embodiments, wearable device 210 may include onboard processing (e.g., processor 114) to process the sensor data (e.g., filtering, conversion, etc.) before sending the sensor data to one or more assistive devices 220, user devices 260, servers 270, and / or one or more other devices 280. Alternatively or additionally, wearable device 210 may be configured to send (raw or processed) biometric sensor data to one or more assistive devices 220, user devices 260, servers 270, and / or one or more other devices 280 for further processing and / or analysis of the data by such devices. In some embodiments, the wearable device 210 may include a communication interface (e.g., communication interface 119) configured to allow one-way or two-way communication with external devices, such as the one or more auxiliary devices 220, user device 260, server 270, and / or one or more other devices 280. In some embodiments, the wearable device 210 may be configured to receive signals from the auxiliary device 220, user device 260, server 270, and / or other devices 280. For example, the wearable device 210 may be configured to receive a signal corresponding to audio output to be delivered by the wearable device 210, or to receive a signal associated with adjusting the volume of the audio output based on environmental factors (e.g., ambient light, ambient noise, etc.) detected by other computing devices in the system.

[0049] User device 260 may be a user-associated computing device that tracks a user's sleep information. Examples of user device 260 may include mobile phones (or other portable devices such as tablets, laptops, personal computers, smart devices, etc.). In some embodiments, user device 260 may be configured to analyze (e.g., via a display) sensor data collected from wearable device 210 (raw and / or processed).

[0050] Server 270 may include computing devices for processing and / or analyzing sleep data, storing sleep information, or storing other information transmitted from wearable device 210, assistive device 220, and / or user device 260. Server 270 may be located in the same or different locations as wearable device 210, assistive device 220, and / or user device 260. For example, server 270 may be located in the same room as wearable device 210 (e.g., in a user's home). Alternatively, server 270 may be located in a remote location (e.g., a cloud-based server). Server 270 may store information accessible to wearable device 210, user device 250, server 270, and / or other devices 280. In some embodiments, server 270 may be a hard disk drive, database, cloud storage, network-attached storage, or other data storage device. In some embodiments, server 270 may store sensor data, historical user data (including data associated with sleep history), etc.

[0051] Other devices 280 may be computing devices associated with other individuals or entities (such as healthcare providers, family members of the user, etc.) who have requested access to the user's data and / or have been granted access to the user's data. Although not in Figure 2 As depicted, but it will be appreciated that computing device 210, user device 260, server 270 and / or other devices 280 may include components (e.g., memory, processor, I / O devices, etc.) that enable the computing device to perform functions associated with monitoring the user's sleep and the operation of wearable device 210.

[0052] Figure 3A schematically depicts an embodiment of a system 300 for a sleep application according to an embodiment. As shown, system 300 includes earbuds 310 and charging case 320. In some embodiments, certain aspects of earbuds 310 and charging case 320 may be structurally and / or functionally similar to wearable device 110 and assistive device 120, and therefore, certain aspects of earbuds 310 and charging case 320 will not be described herein with reference to Figure 3A. As shown, charging case 320 may have an RF unit including classic Bluetooth and / or BLE audio; core sensors including a light sensor, temperature sensor, pressure sensor, accelerometer, microphone, and gyroscope; a processor including a microcontroller; and other components including a power management integrated circuit (PMIC), one or more batteries, a fuel indicator, and a Hall effect sensor. In some embodiments, the fuel indicator may be an indicator calculated from other components in the system and displayed to the user. Earbuds 310 may have an RF unit including BLE audio; an accelerometer; and other components including a PMIC, one or more batteries, a fuel indicator, and a speaker. In some embodiments, the fuel indicator may be an indicator calculated from other components in the system and displayed to the user. In some embodiments, the earbuds 310 and the charging case 320 can transmit information bidirectionally via BLE audio.

[0053] In some embodiments, an accelerometer in the earbud 310 can record the acceleration of a part of the user's body, and this information can be transmitted to the charging case 320 and / or other external devices (e.g., user devices and / or servers) for processing and analysis to determine the user's sleep state and / or sleep stage. In some embodiments, information related to audio output can be transmitted or downloaded from the charging case 320, user devices, and / or servers to the earbud 310, enabling the earbud 310 to output audio to the user via a speaker. In some embodiments, information related to audio output can be transmitted or downloaded to the charging case 320 before using the earbud 310, allowing the user to listen to audio from the earbud 310 without connecting to a user device, server, or any other system that may require an internet connection. In some embodiments, the charging case 320 can receive information related to biometric data from the earbud 310. In some embodiments, the charging case 320 can obtain information about the user and / or the user's environment (e.g., ambient temperature, ambient light intensity, air pressure, ambient noise level, etc.) via core sensors. In some embodiments, the charging case 320 can transmit this information (i.e., biometric data and environmental data) to an external device (e.g., a user device or a server) via Bluetooth Classic and / or BLE audio. In some embodiments, a user can control the operation of the earbuds 310 via input to the charging case 320. For example, a user can turn the earbuds 310 on or off, output audio from the earbuds 310, select the type of audio output from the earbuds 310, increase the volume of the audio output from the earbuds 310, or activate sensor recording in the earbuds 310 via input to the charging case 320.

[0054] Figure 3B schematically depicts different embodiments of a system 300' for a sleep application. System 300' includes earbuds 310' and a charging case 320'. As shown, the charging case 320' may have an RF unit including NFC, Bluetooth Classic, Wi-Fi, and BLE audio; core sensors including a light sensor, temperature sensor, air quality sensor, humidity sensor, pressure sensor, accelerometer, microphone, and gyroscope; a processor including a microcontroller and edge AI and ML processors; and other components including a PMIC, one or more batteries, a fuel indicator, and a Hall effect sensor. In some embodiments, the fuel indicator may be an indicator calculated from other components in the system and displayed to the user. Earbuds 310' may have an RF unit including NFC and BLE audio; core sensors including an accelerometer, microphone, body temperature sensor, PPG sensor, and optionally one or more EEG sensors; and other components including a PMIC, one or more batteries, a fuel indicator, and a speaker. In some embodiments, the EEG sensors may be configured to record brain signals from the user. In some embodiments, the brain signals may be processed and / or analyzed to determine information related to the user's sleep (e.g., sleep stage, brain state, etc.). In some embodiments, the fuel indicator may be an indicator calculated from other components in the system and displayed to the user. In some embodiments, certain aspects of the earbuds 310' and the charging case 320' may be structurally and / or functionally similar to the wearable device 110 and the earbuds 310, as well as the auxiliary device 120 and the charging case 320, and therefore certain aspects of the earbuds 310' and the charging case 320' will not be described herein with reference to FIG3B.

[0055] In some embodiments, core sensors in earbud 310' can collect information about the user. For example, in some embodiments, accelerometers and PPG sensors in earbud 310' can record data from the user to determine the user's sleep stages. Microphones can record noise from the user's vicinity to determine, for example, whether the user snores throughout the night or experiences other sleep disorders. User information can be transmitted to charging case 320' and / or other external devices (e.g., user devices and / or servers) for processing and analysis. In some embodiments, user information can be processed and analyzed locally via an onboard edge AI and ML processor, and the processed information can be transmitted to charging case 320' and / or other external devices. In some embodiments, earbud 310' can stream audio output information directly from user devices and / or servers via one of the onboard radio frequency units.

[0056] Figure 3C depicts a bedside speaker 320'' for a sleep application according to an embodiment. As shown, the bedside speaker 320'' may have an RF unit including NFC, Bluetooth Classic, Wi-Fi, and BLE audio; core sensors including a light sensor, temperature sensor, air quality sensor, humidity sensor, pressure sensor, accelerometer, microphone, gyroscope, radar sensor, and proximity sensor; one or more processors including a microcontroller and edge AI and ML processors; and other components including a PMIC, battery, fuel indicator, LED light, button and / or knob, display, and speaker. In some embodiments, the fuel indicator may be an indicator calculated from other components in the system and displayed to the user. In some embodiments, the bedside speaker 320'' may be used additionally and / or alternatively in addition to earplugs and a charging case. In some embodiments, the bedside speaker 320'' may collect environmental data via the core sensors and output audio via the speaker. The bedside speaker 320'' may be useful for users who cannot or do not want to sleep with earplugs in (e.g., users who do not like to sleep with devices in their ears).

[0057] Figure 4 This is a flowchart of a method for determining a user's sleep-related physical indicators (including rapid movement (FM) index, slow movement (SM) index, and respiratory rate (RR)) using measurements from a wearable device. Accelerometer data (corresponding to accelerations of a portion of the user's ear) in the x, y, and z directions are collected by the wearable device via accelerometers within the wearable device. In other words, the accelerometer data can be triaxial. The acceleration data is sent to a processor (e.g., a processor in the wearable device, assistive device, user device, and / or server). In some embodiments, the accelerometer data can be 12-bit. In some embodiments, the accelerometer data can be sampled at a rate between about 10 Hz and about 100 Hz (inclusive of all ranges and subranges therebetween). In some embodiments, the accelerometer data can be sampled at a rate of about 25 Hz.

[0058] To calculate the Fast Motion (FM) index, a high-pass filter is applied to the acceleration data (x-direction, y-direction, and y-direction), followed by L2 normalization of the filtered data. In some embodiments, the high-pass filter may operate at a cutoff frequency between approximately 0.1 Hz and approximately 1 Hz, including all subranges and values ​​therebetween. The normalized data stream can then be decomposed into sliding time windows (e.g., moving windows). For example, the data stream may be decomposed into multiple overlapping time windows, each with a fixed size of approximately 6 seconds (s) and sliding intervals offset from each other by approximately 1 second. Data points within each time window may be summed. In some embodiments, the window size used when calculating the FM index ranges from approximately 1 second to approximately 1 minute. In some embodiments, window sizes used are approximately 1 second, approximately 2 seconds, approximately 3 seconds, approximately 4 seconds, approximately 5 seconds, approximately 6 seconds, approximately 7 seconds, approximately 8 seconds, approximately 9 seconds, approximately 10 seconds, approximately 20 seconds, approximately 30 seconds, approximately 40 seconds, approximately 50 seconds, and approximately 1 minute, including all values ​​and subranges therebetween. In some embodiments, the sliding period used when calculating the FM index ranges from about 0.1 seconds to about 10 seconds. In some embodiments, the sliding period used when calculating the FM index is about 0.1 seconds, about 0.2 seconds, about 0.3 seconds, about 0.4 seconds, about 0.5 seconds, about 0.6 seconds, about 0.7 seconds, about 0.8 seconds, about 0.9 seconds, about 1 second, about 2 seconds, about 3 seconds, about 4 seconds, about 5 seconds, about 6 seconds, about 7 seconds, about 8 seconds, about 9 seconds, or about 10 seconds. A linear transformation can then be applied to the data to obtain the FM index. In some embodiments, the FM index can be an indicator or value that indicates the degree of rapid movement a user is engaging in.

[0059] To compute the Slow Motion (SM) index, a high-pass filter is applied to the acceleration data (x, y, and z components), followed by L2 normalization of the filtered data. In some embodiments, the high-pass filter may operate at a cutoff frequency between approximately 0.1 Hz and approximately 1 Hz, including all subranges and values ​​therebetween. The normalized data stream can then be divided into sliding time windows (e.g., similar to the algorithm used to compute the FM index). In some embodiments, the time window size used to compute the SM index is approximately 20 seconds, and the sliding period used is approximately 1 second. In some embodiments, the window size used to compute the SM index ranges from approximately 1 second to approximately 1 minute. In some embodiments, the window size used when computing the SM index is approximately 1 second, approximately 2 seconds, approximately 3 seconds, approximately 4 seconds, approximately 5 seconds, approximately 6 seconds, approximately 7 seconds, approximately 8 seconds, approximately 9 seconds, approximately 10 seconds, approximately 20 seconds, approximately 30 seconds, approximately 40 seconds, approximately 50 seconds, or approximately 1 minute, including all values ​​and subranges therebetween. In some embodiments, the sliding period used when computing the SM index ranges from approximately 0.1 seconds to approximately 10 seconds. In some embodiments, the sliding intervals used are approximately 0.1 seconds, approximately 0.2 seconds, approximately 0.3 seconds, approximately 0.4 seconds, approximately 0.5 seconds, approximately 0.6 seconds, approximately 0.7 seconds, approximately 0.8 seconds, approximately 0.9 seconds, approximately 1 second, approximately 2 seconds, approximately 3 seconds, approximately 4 seconds, approximately 5 seconds, approximately 6 seconds, approximately 7 seconds, approximately 8 seconds, approximately 9 seconds, and approximately 10 seconds. A linear transformation can then be applied to the data to obtain the SM metric. In some embodiments, the SM metric can be an indicator or value that indicates the degree of slow movement the user is engaging in. The FM metric and the SM metric may differ from each other based on the size of the window used to process the data. For example, as mentioned above, the FM metric can be evaluated based on a window size of approximately 6 seconds, while the SM metric can be evaluated based on a larger window size than the FM metric (e.g., approximately 20 seconds).

[0060] In some embodiments, the linear transformation used to calculate the SM metric is the same as the linear transformation used to calculate the FM metric. In some embodiments, the linear transformation used to calculate the SM metric is different from the linear transformation used to calculate the FM metric. While FM and SM metrics have been described herein, it will be appreciated that any type of metric or value can be used to assess a user's characteristics during rest or sleep. For example, a single metric or value (e.g., a motion metric) can be used to indicate whether a user is engaged in slow or fast movement. In some embodiments, the type of movement can also be categorized, for example, as slow, fast, or a specific type of slow or fast movement. A user's movement can be used, for example, by a processor as described above to determine the user's sleep state.

[0061] To calculate the respiratory rate, acceleration data (x, y, and z components) are downsampled. In some embodiments, the acceleration data is downsampled by a factor within a range of about 1 to about 20, including all subranges and values ​​therebetween. Next, baseline data is removed from the downsampled data, and a bandpass filter is applied. In some embodiments, the bandpass filter may be used to remove data outside of a typical respiratory rate (e.g., between about 5 and about 35 breaths per minute). Next, a motion filter is applied. The data is then further processed using a sliding window (i.e., a moving window). In some embodiments, a 20-second sliding window with a 1-second interval is used. In some embodiments, the sliding window used when calculating the respiratory rate ranges from about 1 second to about 1 minute. In some embodiments, the sliding window used is about 1 second, about 2 seconds, about 3 seconds, about 4 seconds, about 5 seconds, about 6 seconds, about 7 seconds, about 8 seconds, about 9 seconds, about 10 seconds, about 20 seconds, about 30 seconds, about 40 seconds, about 50 seconds, or about 1 minute, including all values ​​and subranges therebetween. In some embodiments, the intervals used in calculating the respiratory rate range from about 0.1 seconds to about 10 seconds. In some embodiments, the intervals used are about 0.1 seconds, about 0.2 seconds, about 0.3 seconds, about 0.4 seconds, about 0.5 seconds, about 0.6 seconds, about 0.7 seconds, about 0.8 seconds, about 0.9 seconds, about 1 second, about 2 seconds, about 3 seconds, about 4 seconds, about 5 seconds, about 6 seconds, about 7 seconds, about 8 seconds, about 9 seconds, and about 10 seconds. Next, the Fast Fourier Transform (FFT) amplitude is calculated, and the amplitudes of the x, y, and z components of the processed data are summed. After summing, a window index is calculated, and certain time points are discarded. The peak amplitude of the FFT is located to calculate the respiratory rate, and then the calculated respiratory rate data is smoothed to obtain the RR index. In some embodiments, additional filtering (e.g., statistical filtering) may be applied before smoothing the respiratory rate data. For example, end-of-night statistical filtering may be applied. In some embodiments, statistical filtering at the end of the night can be applied to eliminate outliers before the data is smoothed.

[0062] Once calculated, the FM, SM, and RR metrics can be combined to determine a user's sleep state and / or sleep stage. In some embodiments, the body metrics can be calculated continuously, allowing sleep stages to be determined continuously throughout the night. In such embodiments, the processor can acquire portions of acceleration data, and the processor can apply the algorithm to portions of acceleration data acquired at a given time. In some embodiments, motion metric (e.g., FM and SM) processing can operate at a frequency of approximately 25 Hz, while RR processing can operate at a frequency of approximately 2.5 Hz. In some embodiments, the FM, SM, and RR metrics can be calculated and / or reported at a rate of approximately 0.1 Hz to approximately 2 Hz (inclusive of all ranges and subranges). In some embodiments, the FM, SM, and RR metrics can be calculated and / or reported at a rate of approximately 1 Hz (e.g., once per second). In some embodiments, the FM, SM, and RR metrics can be reported along with a timestamp.

[0063] Figures 5-9 This is a schematic block diagram of an embodiment regarding the functionality and computational load distribution across various components of a system used for sleep applications. Figure 5 The distribution of computational load across system 500 according to an embodiment is shown. As shown, the computational load is distributed among wearable device 510, auxiliary device 520, user device 560, and server 570. Certain aspects of wearable device 510, auxiliary device 520, user device 560, and server 570 are structurally and / or functionally similar to wearable devices 110, 210, 310, 310', auxiliary devices 120, 220, 320, 320', 320'', user device 260, and server 270; therefore, further details regarding... Figure 5This describes certain aspects of wearable device 510, assistive device 520, user device 560, and server 570. As shown, wearable device 510 is configured to determine body metrics 502, including FM metrics 502a, SM metrics 502b, and RR 502c. Wearable device 510 may include components for determining body metrics 502, such as a processor, memory, and / or a communication interface. Once body metrics 502 are determined, this information is sent to assistive device 520 and / or user device 560. As shown, assistive device 520 uses the body metric 502 information to determine the user's sleep state (sleep / wake) 503. Information corresponding to sleep state 503 is then sent to user device 560. User device 560 may use information associated with body metrics 502, sleep state 503, or both to determine the user's sleep stage 504 and to display sleep information to the user 506. User equipment 560 may send information, including but not limited to body indicators 502, sleep state 503, and sleep stage 504, to server 570. Server 570 may store sleep stage history 508 or other information 509 collected by the system (e.g., raw sensor data).

[0064] Figure 6 The distribution of computational load across system 600 according to an embodiment is shown. As shown, the computational load is distributed among wearable device 610, auxiliary device 620, user device 660, and server 670. Certain aspects of wearable device 610, auxiliary device 620, user device 660, and server 670 are structurally and / or functionally similar to wearable devices 110, 210, 310, 310', 510, auxiliary devices 120, 220, 320, 320', 320'', 520, user devices 260, 560, and servers 270, 570; therefore, further details regarding these aspects will not be discussed herein. Figure 6Describes certain aspects of wearable device 510, assistive device 620, user device 660, and server 670. As shown, wearable device 610 is configured to determine body metrics 602, including FM metrics 502a, SM metrics 502b, and RR 502c. Once body metrics 602 are determined, this information is sent to assistive device 620. As shown, assistive device 620 uses the body metric 502 information to directly determine the user's sleep stage 604. In some embodiments, assistive device 620 may determine the user's sleep state (sleep / wake) and the user's sleep stage 604. Information corresponding to sleep stage 604 is then sent to user device 660. At 606, user device 660 displays sleep information from wearable device 610 (e.g., raw sensor data, biometrics) and / or sleep information from assistive device 620 (e.g., sleep state, sleep stage, environmental data, etc.). User device 660 may send information to server 670. Server 670 may store sleep stage history 608 or other information 609 collected by the system (e.g., raw sensor data).

[0065] Figure 7 The distribution of computational load across system 700 according to an embodiment is shown. As shown, the computational load is distributed among wearable device 710, auxiliary device 720, user device 760, and server 770. Certain aspects of wearable device 710, auxiliary device 720, user device 760, and server 770 are structurally and / or functionally similar to wearable devices 110, 210, 310, 310', 510, 610, auxiliary devices 120, 220, 320, 320', 320'', 520, 620, user devices 260, 560, 660, and servers 270, 570, 670; therefore, further details regarding... Figure 7Describes certain aspects of wearable device 510, assistive device 720, user device 760, and server 770. As shown, wearable device 710 is configured only to collect raw acceleration data 701 via sensors within wearable device 710. Information corresponding to the raw acceleration data 701 is sent to assistive device 720. Assistive device 720 is configured to determine body metrics 702, including FM metrics 702a, SM metrics 702b, and RR 702c. Once assistive device 720 has determined body metrics 702, it uses this information to determine the user's sleep stage 704. In some embodiments, assistive device 720 may determine the user's sleep state (sleep / wake) and the user's sleep stage 704. The information determined by assistive device is then sent to user device 760. At 706, user device 760 displays sleep information from wearable device 710 (e.g., raw sensor data, biometrics) and / or sleep information from assistive device 720 (e.g., sleep state, sleep stage, environmental data, etc.). User equipment 760 may send information to server 770. Server 770 may store sleep stage history 708 or other information 709 collected by the system (e.g., raw sensor data).

[0066] Figure 8 The distribution of computational load for processing and / or tuning audio output across system 800 according to an embodiment is illustrated. As shown, the computational load is distributed among wearable device 810, user device 860, and optional auxiliary device 820. In some embodiments, an audio application player 862 executing on user device 860 may optionally process and / or tune audio before passing audio output information to wearable device 810 via auxiliary device 820. Wearable device 810 may process and / or tune audio output (e.g., bass boost, volume reduction / increase) before outputting audio output to the user. In some embodiments, wearable device 810 may process and / or tune audio output according to sensor data 801 collected by system 800. For example, wearable device 810 may reduce the volume of audio output in response to a signal that the user has entered a sleep state (e.g., based on biometrics calculated from sensor data 801).

[0067] Figure 9The distribution of computational load for processing and / or tuning audio output across system 900 according to an embodiment is illustrated. As shown, the computational load is distributed among wearable device 910, assistive device 920, and user device 960. In some embodiments, an audio application player 962 executing on user device 960 may optionally process and / or tune audio before sending audio output information to assistive device 920. As shown, assistive device 920 may be configured to process and / or tune audio (e.g., bass boost, volume reduction / increase). In some embodiments, assistive device 920 may process and / or tune audio output according to sensor data 902 collected by system 900. For example, assistive device 920 may increase the volume of audio output in response to a signal that ambient noise in the user's bedroom (collected from a microphone on assistive device 920) has increased. Wearable device 910 may process and / or tune audio output (e.g., bass boost, volume reduction / increase) before outputting audio output to the user. In some embodiments, the wearable device 910 may process and / or tune the audio output according to sensor data 901 collected by the system 900. For example, the wearable device 910 may reduce the volume of the audio output in response to a signal that the user has entered a sleep state (e.g., based on biometrics calculated from the sensor data 901). In some embodiments, sensor data 901 and sensor data 902 may include sensor data from both the wearable device 910 and the assistive device 920.

[0068] Figure 10This is an exploded view of a wearable device 1110 for a sleep application according to an embodiment. As shown, the wearable device 1110 is an earplug configured to be worn inside a user's ear. As shown, the wearable device 1110 includes a main housing 1035 coupled to an earplug sleeve (or support member) 1030. The earplug sleeve 1030 can be formed in any shape suitable for a user's outer ear. As shown, the earplug sleeve 1030 is formed in a curved shape with a tip, such that the earplug sleeve 1030 can conform to the contour of the user's outer ear concha, thereby providing stability of the wearable device 1110 in the user's ear during sleep with minimal discomfort. Additionally, the earplug sleeve 1030 can be formed of a flexible material (e.g., silicone, rubber, or any other suitable polymer), allowing the earplug sleeve 1030 to bend and comfortably conform to the user's outer ear. The main housing 1035 can be detachably coupled to the earplug tip 1032 (e.g., formed of a material such as silicone, rubber, or any other suitable polymer). In some embodiments, the main housing 1035 can be coupled to earplug tips 1032 of different sizes according to the anatomy of the user's ear. In some embodiments, the main housing 1035, earplug tips 1030, and earplug tips can be configured to form a tight seal between the wearable device 1110 and the user's ear. This tight seal between the wearable device 1110 and the user's ear can provide and / or enhance the noise masking capability of the wearable device 1110. In some embodiments, the tight seal between the wearable device 1110 and the user's ear, combined with the audio output from the wearable device 1110, can provide a significant noise masking effect. This tight seal can block or reduce ambient noise that may cause vibrations in the anatomical structures of the inner ear, thereby blocking or reducing the amount of ambient noise perceived by the user. Reducing the amount of ambient noise perceived by the user can improve the user's sleep quality by reducing nighttime sleep disturbances caused by ambient noise.

[0069] The main housing 1035 defines a compartment where the battery assembly 1025 and other components of the wearable device 1110 can be arranged. A printed circuit board (PCB) 1020 can be arranged on the battery assembly 1025, and a magnet 1015 can optionally be arranged on the PCB 1020. A housing 1005 is coupled to the main housing 1035 to enclose the components of the wearable device 1110 (e.g., battery assembly 1025, PCB 1020, magnet 1015, etc.) therein. The wearable device 1110 may include one or more electrical connectors 1040 (e.g., spring-loaded pins, spring pins, etc.) configured to electrically connect the wearable device to an auxiliary device (e.g., a charging case). Although shown with a specific form factor, the components of the wearable device 1110 (e.g., battery assembly 1025, PCB 1020, magnet 1015, connectors 1040, etc.) can be arranged in the main housing 1035 in any suitable manner.

[0070] Figure 11This is an exploded view of a charging case 1120 for a wearable device according to an embodiment. The charging case may include a base 1195 supporting a bottom 1190 of a housing (e.g., a metal can). In some embodiments, the housing may be formed of a robust and rigid material. In some embodiments, the housing may be formed of iron, aluminum, stainless steel, carbon steel, galvanized steel, alloy, plastic, polymer, any other suitable material, or a combination thereof. The bottom 1190 of the housing may define one or more openings through which components inside the housing can be accessed and / or seen from the outside of the bottom 1190 of the housing. For example, the bottom 1190 of the housing may define an opening 1192 through which an LED (not shown) can be seen. In some embodiments, the charging case 1120 may include a conduit (e.g., an LED light guide, optical fiber, etc.) (not shown) to transmit light from the LED to the opening defined in the bottom 1190 of the housing. The bottom 1190 of the housing may define an opening through which one or more sensors included in the charging case 1120 can be exposed. The bottom 1190 of the housing may include a sensor cover (not shown) to close openings defining the one or more sensors, thereby protecting the one or more sensors from external environmental influences. The charging case 1120 may optionally include a USB housing (e.g., a USB sleeve or USB shield) 1186. The charging case 1120 may also include a PCB 1185 disposed in the bottom 1190 of the housing. A battery 1180 may be electrically connected to or coupled to the PCB 1185. An LED housing (e.g., an LED cover) including two lateral assemblies 1170 (a left-side assembly and a right-side assembly) and a central assembly 1175 may be disposed within the housing to secure the LEDs and conduits within the housing. The charging case 1120 has antenna carriers, including a plastic antenna carrier 1165 and a silicone antenna carrier 1160, to secure one or more antennas within the housing 1190.

[0071] The bottom 1190 of the housing is coupled to the top 1155 of the housing, and the bottom 1190 and the top 1155 form a closed housing when coupled. The top 1155 of the housing is configured to accommodate a wearable device. In some embodiments, the top 1155 of the housing includes one or more recesses, notches, cavities, recesses, etc., configured to support the wearable device. In some embodiments, these notches may be formed in a shape corresponding to the wearable device, such that the wearable device is placed substantially flush with the outer surface of the top 1155 of the housing. In some embodiments, the top 1155 of the housing includes electrical connection points operable to transmit information and / or power to the wearable device. For example, the top 1155 of the housing may include electrical connection points that interface with an electrical connector 1040 on the wearable device to charge the wearable device. The charging case 1120 may have a lid that includes an inner cover (not shown) and an outer cover 1150 coupled to the top 1155 of the housing. The cover may include a sliding guide (not shown) that allows the cover to move from a first position where the wearable device is enclosed in the housing to a second position where the wearable device is accessible to the user. Although in Figure 11 The cover is referred to as a sliding cover, but the cover may also be coupled to the top 1155 of the housing in other ways. For example, in some embodiments, the cover may be coupled to the top 1155 of the housing via a connector including but not limited to hinges, universal joints, magnets, cantilever joints, torsion snap joints, etc.

[0072] Figure 12 A wearable device 1210 disposed in a charging case 1220 according to an embodiment is depicted. As shown, the wearable device 1210 is an earbud. In some embodiments, the charging case 1220 includes a housing with a notch configured to receive the wearable device 1210. In some embodiments, the charging case 1220 may be configured to charge the wearable device 1210 wirelessly (e.g., via inductive charging). In some embodiments, the charging case 1220 may be configured to charge the wearable device 1210 via an electrical connection point (e.g., a spring pin, etc.). In some embodiments, the charging case 1220 includes a lid that is slidably movable from a first position where the wearable device 1210 is enclosed in the housing to a second position where the wearable device 1210 is accessible to a user.

[0073] While various embodiments of the invention have been described and illustrated herein, those skilled in the art will readily conceive of various other means and / or structures for performing the functions described herein and / or obtaining the results and / or one or more advantages described herein, and each such variation and / or modification is considered to be within the scope of the embodiments of the invention described herein. More generally, those skilled in the art will readily recognize that all parameters, dimensions, materials, and configurations described herein are intended to be exemplary, and actual parameters, dimensions, materials, and / or configurations will depend on one or more specific applications in which the teachings of the invention are applied. Those skilled in the art will recognize, or be able to determine, many equivalents of the specific embodiments of the invention described herein using means not exceeding conventional experimentation. Thus, it should be understood that the foregoing embodiments are presented by way of example only, and that embodiments of the invention may be practiced in ways other than those specifically described and claimed within the scope of the appended claims and their equivalents. The embodiments of the invention disclosed herein relate to each individual feature, system, article, material, kit, and / or method described herein. Furthermore, any combination of such features, systems, articles, materials, kits, and / or methods is included within the scope of this disclosure if two or more such features, systems, articles, materials, kits, and / or methods do not contradict each other.

[0074] Furthermore, various inventive concepts can be embodied in one or more methods, examples of which have been provided. Actions performed as part of said method can be ordered in any suitable manner. Thus, embodiments in which actions are performed in an order different from that shown in the illustrations can be constructed, which may include performing certain actions simultaneously, even if in the exemplary embodiments these actions are represented as actions occurring sequentially.

[0075] Some of the embodiments and / or methods described herein can be implemented using different software, hardware, or combinations thereof (executing on hardware). Hardware modules may include, for example, general-purpose processors, field-programmable gate arrays (FPGAs), and / or application-specific integrated circuits (ASICs). Software modules (executing on hardware) can be expressed in various software languages ​​(e.g., computer code), including C, C++, Java, etc. TM Ruby, Visual Basic TMand / or other object-oriented, procedural, or other programming languages ​​and development tools. Examples of computer code include, but are not limited to, microcode or microinstructions, low-level instructions (such as assembly code), machine instructions (such as instructions generated by a compiler), code for generating web services, and files containing high-level instructions executed by a computer using an interpreter. For example, embodiments may be implemented using imperative programming languages ​​(e.g., C, Fortran, etc.), functional programming languages ​​(Haskell, Erlang, etc.), logic programming languages ​​(e.g., Prolog), object-oriented programming languages ​​(e.g., Java, C++, etc.), or other suitable programming languages ​​and / or development tools. Other examples of computer code include, but are not limited to, control signals, encryption code, and compression code.

[0076] All combinations of the foregoing ideas and the additional ideas discussed herein (provided that such ideas do not contradict each other) are considered part of the subject matter disclosed herein. Terms expressly used herein that may also appear in any disclosure incorporated by reference shall be given the meaning most consistent with the specific ideas disclosed herein.

[0077] The terminology used herein is for describing particular embodiments only and is not intended to be limiting. The singular forms “a,” “an,” and “the” used herein are intended to also include the plural forms unless the context clearly indicates otherwise.

[0078] The terms “about” and / or “approximately” as used herein, when used in conjunction with numerical values ​​and / or ranges, generally refer to values ​​and / or ranges close to the stated values ​​and / or ranges. In some cases, the terms “about” and “approximately” may mean within ±10% of the stated value. For example, in some cases, “about 100 [units]” may mean within ±10% of 100 (e.g., 90 to 110). The terms “about” and “approximately” are used interchangeably.

Claims

1. A wearable device, comprising: A shell configured to be worn inside the user's ear; Sensors disposed on or within the housing, the sensors being configured to measure the user's physical indicators; A speaker configured to deliver audio output to the user's ears; Processor, the processor being configured to: Receive data associated with body indicators from the sensor; and Control the speaker to deliver audio output; and A communication interface configured to send data associated with bodily indicators to an external device, such that the external device, in response to receiving the data associated with bodily indicators, is configured to determine characteristics of the user's sleep periods.

2. The wearable device according to claim 1, wherein the external device includes a box configured to charge the wearable device.

3. The wearable device according to claim 1, wherein the communication interface uses at least one of low-power audio or near-field communication.

4. The wearable device according to claim 1, wherein the sensor comprises at least one of an accelerometer, a photoplethysmography (PPG) sensor, or an electroencephalogram (EEG) sensor.

5. The wearable device according to claim 1, wherein the processor is further configured to determine at least one of a user's fast motion index, slow motion index, or breathing rate based on data associated with body metrics.

6. The wearable device according to claim 1, wherein the characteristics of the user's sleep period include at least one of the following: the user's rapid movement index, slow movement index, or respiratory rate.

7. The wearable device according to claim 1, wherein the external device is configured to measure environmental indicators of the user's environment, and the processor is configured to adjust the audio output delivered by the speaker based on data associated with the environmental indicators.

8. The wearable device according to claim 1, wherein the processor is configured to adjust the audio output delivered by the speaker based on data associated with body indicators or characteristics of sleep periods.

9. The wearable device according to claim 1, wherein the processor is further configured to determine the user's sleep stage based on data associated with body metrics.

10. A system comprising: A wearable device configured to be worn inside a user's ear, the wearable device comprising: A first set of one or more sensors configured to measure the user's physical indicators; A speaker configured to deliver audio output to the user's ears; and A processor operatively coupled to the sensors and the speaker, the processor being configured to receive data from the one or more sensors and control the speaker to deliver audio output; and A charging device configured to charge the wearable device when the wearable device is coupled to the charging device, the charging device including a second set of one or more sensors configured to measure environmental indicators of the user's environment.

11. The system according to claim 10, wherein the wearable device is an earplug.

12. The system of claim 10, wherein the wearable device further comprises a communication interface configured to send information to and receive information from the charging device via at least one of low-power audio or near-field communication.

13. The system according to claim 10, wherein the first set of sensors comprises at least one of an accelerometer, a photoplethysmography (PPG) sensor, or an electroencephalogram (EEG) sensor.

14. The system of claim 10, wherein the processor is further configured to determine a user’s rapid motion index, slow motion index, or respiratory rate based on data from the first set of sensors.

15. The system of claim 10, wherein the charging device is further configured to determine the user's fast motion index, slow motion index, or breathing rate based on data from the first set of sensors.

16. The system of claim 10, wherein the processor is further configured to determine the user's sleep stage based on data from the first set of sensors.

17. The system of claim 10, wherein the charging case is further configured to determine the user's sleep stage based on data from the first set of sensors.

18. The system of claim 10, wherein the processor is further configured to adjust the audio output based on data from the first set of sensors, data from the second set of sensors, or the user's sleep stage.

19. The system according to claim 10, wherein the second set of sensors comprises at least one of the following: a light sensor, a temperature sensor, an air quality sensor, a pressure sensor, a humidity sensor, an accelerometer, a speaker, or a gyroscope.

20. A method comprising: Sensors from a wearable device worn in the user's ear are used to measure one or more body metrics. The user's sleep state or sleep stage is determined based on one or more of the aforementioned physical indicators; Generate audio output based on at least one of the user's sleep state or sleep stage; as well as The audio output is delivered via the speaker of the wearable device.

21. The method according to claim 20, further comprising: Based on the one or more of the aforementioned physical indicators, determine at least one of the user's fast movement index, slow movement index, or breathing rate.

22. The method of claim 20, wherein the sensor comprises an accelerometer.

23. The method according to claim 20, further comprising: The user's sleep stages are displayed on an external device operatively coupled to the wearable device.

24. The method of claim 20, wherein the audio output includes audio that helps a user fall asleep or stay asleep.

25. The method of claim 24, wherein the audio output includes pink noise.

26. The method according to claim 20, further comprising: Measure environmental metrics of the user's environment while the user is sleeping; as well as Adjust the audio output based on the aforementioned environmental indicators.

27. The method of claim 20, wherein the audio output includes an alarm clock that wakes the user.

28. The method according to claim 20, further comprising: An environmental score is determined based on the environmental indicators, and the environmental score indicates how suitable the environment is for sleep.

29. The method according to claim 28, further comprising: At least one of the user's sleep-related information, environmental metrics, or environmental scores is stored on an external device operatively coupled to the wearable device.

30. The method according to claim 29, further comprising: The external device displays at least one of the user's sleep-related information, environmental indicators, or environmental scores.

31. The method according to claim 20, further comprising: When the user is determined to be awake, the output is configured to help the user fall asleep.

32. The method according to claim 31, wherein the audio output is relaxation content.

33. The method according to claim 20, further comprising: When it is determined that the user is asleep, the output includes audio output that masks content to help the user stay asleep.

34. A method comprising: Data corresponding to the acceleration of a portion of the user is obtained via sensors placed in or on a wearable device coupled to the user; The user's physical indicators are determined based on data corresponding to the acceleration of this part of the user. Determine at least one of the user's sleep state or sleep stage based on the user's physical indicators; as well as Audio output is output from the speaker of the wearable device based on at least one of the user's sleep state or sleep stage.

35. The method according to claim 34, wherein the data corresponding to the acceleration of the user's portion includes an x-component, a y-component, and a z-component.

36. The method according to claim 34, wherein the body index includes at least one of the user's fast movement index, slow movement index, or respiratory rate.

37. The method according to claim 34, wherein determining the fast motion index and the slow motion index comprises: A high-pass filter is applied to the data corresponding to the acceleration of this part of the user to generate filtered data; The filtered data is normalized to generate normalized data; Normalized data is divided into multiple time windows, with a predetermined duration between adjacent time windows; Sum the data points within each time window; and Apply linear transformation.

38. The method according to claim 34, wherein the sensor samples the data at a rate between 10 Hz and 100 Hz.

39. The method according to claim 37, wherein the cutoff frequency of the high-pass filter is between 0.1 Hz and 1 Hz.

40. The method according to claim 37, wherein the duration of the time window is between 0.1 seconds and 10 seconds, and the predetermined duration between adjacent time windows is 1 second.

41. The method according to claim 37, wherein the duration of the time window is between 10 seconds and 1 minute, and the predetermined duration between time windows is 1 second.

42. The method according to claim 36, wherein determining the respiratory rate comprises: Apply one or more filters to the data to generate filtered data; Calculate the Fast Fourier Transform (FFT) of the filtered data to generate FTT data; The amplitudes of the x, y, and z components of the FTT data are summed to generate summed FTT data. Locate the peak amplitude of the summed FFT data; as well as Respiratory rate is calculated based on the location of peak amplitude.

43. The method according to claim 42, wherein determining the respiratory rate further comprises: Before applying one or more filters, the data is downsampled to generate downsampled data; as well as Remove the baseline of the downsampled data.

44. The method according to claim 43, further comprising: After applying one or more filters, the filtered data is divided into multiple time windows, with a predetermined duration between adjacent time windows; as well as Sum the data points within each time window.

45. The method according to claim 44, further comprising: Remove a subset of time points from the summed FFT data.

46. ​​The method according to claim 45, further comprising: After calculating the respiratory rate based on the location of the peak amplitude, a statistical filter is applied; as well as Smooth the data.

Images