Hearing device with health characterization and / or monitoring and related methods

Abstract

A hearing device is disclosed. The hearing device comprises a memory, an interface, a processor, and one or more sensors, where the processor is configured to: obtain, such as receive and / or retrieve, sensor data from the one or more sensors; determining, based on the sensor data, a health parameter indicative, for example, of a cognitive state of a user of the hearing device; and optionally determining whether the health parameter satisfies, for example, a first criterion indicative of cognitive decline. The processor is configured to output a health representation associated with the cognitive state, e.g., via the interface, in accordance with a health parameter satisfying a first criterion.

Description

Technical Field

[0001] The present disclosure relates to the field of hearing devices, and in particular to hearing devices with health characterization and / or monitoring, and related methods. Background Art

[0002] Hearing loss is associated with cognitive impairment and dementia, and is considered the greatest modifiable risk factor for dementia if addressed in early middle age (e.g., in the 45-65 age group). Furthermore, according to The Lancet, the risk of progression to dementia increases with every additional 10 decibels of hearing loss.

[0003] Furthermore, it has been shown that for people with established mild cognitive impairment, the use of hearing aids can significantly reduce the risk of developing dementia compared to people with hearing loss who do not use hearing aids. The scientific hypothesis is that hearing loss leads to an increased cognitive load, which impacts other parts of the brain while other functions are impaired, ultimately leading to a decrease in cognitive reserve and the development of dementia. The World Health Organization estimates that 55 million people worldwide suffer from dementia, most of whom live in low- and middle-income countries. The WHO projects that this number will rise to 139 million people worldwide by 2050. Alzheimer's disease and related dementias are the seventh leading cause of death and a major issue affecting the quality of life of older adults. Summary of the Invention

[0004] Currently, there is a lack of long-term, accurate and convenient technology to characterize and / or monitor a user's health, such as characterizing and / or monitoring a user's cognitive state and its progression.

[0005] Therefore, there is a need for systems for health characterization and / or monitoring and methods for health characterization and / or monitoring that can mitigate, alleviate or address existing deficiencies and that can improve a user's health characterization and / or monitoring with improved cognitive state characterization and / or monitoring and increased awareness of cognitive health.

[0006] A hearing device is disclosed. The hearing device can be considered a hearing device with health representation and / or monitoring. The hearing device includes a memory, an interface, a processor, and one or more sensors. The processor is configured to obtain (such as receive and / or retrieve) sensor data from the one or more sensors; determine, based on the sensor data, a health parameter, for example, indicating a cognitive state of a user of the hearing device; and optionally determine whether the health parameter satisfies a first criterion (e.g., indicating cognitive decline). Optionally, the processor is configured to output, for example, via the interface, a health representation associated with the cognitive state, for example, based on the health parameter satisfying the first criterion.

[0007] A method of operating a hearing device is disclosed. The method includes obtaining sensor data from one or more sensors of the hearing device; determining a health parameter, e.g., indicative of a cognitive state of a user of the hearing device, based on the sensor data; and determining whether the health parameter satisfies a first criterion, e.g., indicative of cognitive decline. Optionally, the method includes outputting a health representation associated with the cognitive state, e.g., based on the health parameter satisfying the first criterion.

[0008] An electronic device is also disclosed. The electronic device includes a memory, an interface, and a processor. The processor is configured to obtain external sensor data from a hearing device via the interface; and determine, based on the external sensor data, a health parameter, for example, indicating a cognitive state of a user of the hearing device. The processor may be configured to determine whether the health parameter satisfies a first criterion indicating cognitive decline. Optionally, the processor is configured to output, for example, via the interface, a health representation associated with the cognitive state, for example, based on the health parameter satisfying the first criterion.

[0009] Furthermore, a method of operating an electronic device is disclosed. The method includes obtaining sensor data, such as external sensor data, from a hearing device; determining a health parameter, such as one indicating a cognitive state of a user of the hearing device, based on the sensor data, such as the external sensor data; and determining whether the health parameter satisfies a first criterion, such as one indicating cognitive decline. Optionally, the method includes outputting a health representation associated with the cognitive state, such as based on the health parameter satisfying the first criterion.

[0010] A hearing system is also disclosed. The hearing system includes an electronic device comprising a memory, an interface, and a processor. The hearing system includes a hearing device comprising the memory, the interface, the processor, and one or more sensors. The system is configured to obtain sensor data, such as external sensor data, from the one or more sensors. The system is configured to determine a health parameter indicative of a cognitive state of a user of the hearing device based on the sensor data, such as the external sensor data. The system is configured to determine whether the health parameter satisfies a first criterion indicative of cognitive decline. Optionally, the system is configured to output a health representation associated with the cognitive state based on the health parameter satisfying the first criterion.

[0011] A method for operating a hearing system is disclosed. The method includes obtaining sensor data (such as external sensor data) from a hearing device. The method includes determining a health parameter indicative of a cognitive state of a user of the hearing device based on the sensor data (such as the external sensor data). The method includes determining whether the health parameter satisfies a first criterion indicative of cognitive decline. Optionally, the method includes outputting a health representation associated with the cognitive state based on the health parameter satisfying the first criterion.

[0012] The disclosed hearing systems, hearing devices, electronic devices, and related methods provide improved health characterization and / or monitoring. In other words, the present disclosure can provide improved sensor-based (such as audio-based) health characterization and / or monitoring to users of hearing devices with improved accuracy and precision. For example, the distance between the hearing device and the user's mouth can be constant and close, which can provide sensor data with improved accuracy. Furthermore, by using a hearing device to provide sensor data, the sensor can be positioned close to an artery, which can provide better measurements and, in turn, more accurate sensor data. It will be appreciated that, for example, by using a hearing device to provide sensor data, fewer motion artifacts may be present compared to sensors mounted on a user's limbs.

[0013] The present disclosure may improve cognitive decline detection by determining health parameters based on sensor data, such as based on sensor data indicative of biomarkers (eg, acoustic biomarkers, physiological biomarkers, and / or biodynamic biomarkers) of a hearing device user.

[0014] As can be appreciated, the present disclosure can provide improved feedback regarding a user's cognitive status, which is more easily understood by the user. The present disclosure provides improved understanding of a user's health status, particularly regarding the cognitive health of users of hearing devices. In other words, the present disclosure can provide understanding of the importance of cognitive health and diseases associated with cognitive decline. For example, by providing a health representation, the present disclosure can improve visualization and / or comprehensibility for users of hearing devices. Thus, for example, based on health parameters, the health representation can provide information regarding cognitive status. As is known, hearing devices are worn for extended periods throughout the day; therefore, hearing devices with health representation and / or monitoring capabilities can increase the probability of detecting cognitive decline.

[0015] In turn, the present disclosure may provide users of hearing devices with customized health representations, eg based on long-term sensor data. Furthermore, the present disclosure may encourage increased use of hearing devices, eg to reduce the risk of cognitive decline.

[0016] As can be appreciated, the present disclosure provides for health characterization and / or monitoring of hearing device users, for example, to perform early detection and / or identification of cognitive decline in hearing device users. For example, hearing device users can be screened for cognitive decline, such as cognitive impairment, on an ongoing basis. This, in turn, can reduce long-term healthcare costs and ease the burden on caregivers and healthcare systems. For example, by utilizing sensor data from hearing devices, the present disclosure provides a flexible and non-invasive method for characterizing and / or monitoring the health of hearing device users. In other words, the present disclosure can provide a low-cost technique for performing health characterization and / or monitoring of hearing device users, which can also be performed remotely for the user at home.

[0017] An advantage of the present disclosure is that the health status of a user of a hearing device can be characterized and / or monitored based on sensor data obtained over a long period of time, for example, a user of a hearing device typically wears the hearing device for most of their waking hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Those skilled in the art will readily understand the above and other features and advantages of the present invention through the following detailed description of examples of the present disclosure with reference to the accompanying drawings:

[0019] Figure 1 schematically illustrating an example hearing system according to the present disclosure,

[0020] Figure 2 is a flowchart of an example method according to the present disclosure,

[0021] Figure 3 is a flowchart of an example method according to the present disclosure, and

[0022] Figure 4A and Figure 4B is a flowchart of an example method according to the present disclosure. DETAILED DESCRIPTION

[0023] Various examples and details will be described below with reference to the relevant drawings. It should be noted that the drawings may be drawn to scale or not to scale, and that elements of similar structure or function are represented by the same reference numerals throughout the drawings. It should also be noted that the drawings are only used to facilitate the description of the examples. They are not intended to be an exhaustive description of the present disclosure or to limit the scope of the present disclosure. In addition, the examples shown do not necessarily have all the aspects or advantages shown. An aspect or advantage described in conjunction with a particular example is not necessarily limited to that example and can be practiced in any other example, even if not shown or explicitly described as such.

[0024] For the sake of clarity, the drawings are schematic and simplified, and only show details that are helpful for understanding the present disclosure, while other details are omitted. Throughout, the same reference numerals are used for the same or corresponding parts.

[0025] A hearing system is disclosed. The hearing system may be considered as a system for performing health characterization and / or monitoring. In other words, the hearing system may be considered as a system for performing health characterization and / or monitoring of a user of a hearing device (such as a user wearing the hearing device).

[0026] The hearing system includes an electronic device including a memory, an interface, and a processor. The electronic device of the hearing system may be an electronic device as disclosed herein. The hearing system includes a hearing device including a memory, an interface, a processor, and one or more sensors. The hearing device of the hearing system may be a hearing device as disclosed herein.

[0027] A hearing device as disclosed herein may comprise one or more processors, a memory, an interface and one or more sensors, such as a microphone (such as a first microphone) and / or a receiver (such as a hearing device speaker).

[0028] The hearing device may be configured to be worn on an ear of a user. The hearing device may be a hearable device. The hearing device may be a hearing aid, wherein the processor is configured to compensate for a hearing loss of the user.

[0029] Hearing devices may be earplugs, hearing aids, headphones, personal sound amplification products (PSAPs), over-the-counter (OCT) hearing devices, hearing protection devices, custom hearing devices, or other over-the-ear hearing devices. Hearing devices may include both prescription and over-the-counter devices.

[0030] Hearing devices can be embodied in a variety of housing styles or form factors. Some of these form factors include behind-the-ear (BTE) hearing devices, receiver-in-the-canal (RIC) hearing devices, receiver-in-the-ear (RIE) hearing devices, or microphone-in-the-ear (MaRIE) hearing devices. These devices share in common that they can include a BTE component configured to be worn behind a user's ear and an in-the-ear (ITE) component configured to be partially or fully inserted into the user's ear canal. Typically, the BTE component can include at least one input sensor, a power supply, and a processing unit. The term BTE hearing device can refer to hearing devices in which a receiver (i.e., an output transducer) is included in the BTE component, and sound is directed to the ITE component via an acoustic tube connecting the BTE and ITE components, while the terms RIE, RIC, and MaRIE devices refer to hearing devices in which a receiver is included in the ITE component, coupled to the BTE component via a connector cable or wire configured to transmit electrical signals between the BTE and ITE components.

[0031] The hearing aid may be a behind-the-ear (BTE) type, an in-the-ear (ITE) type, an in-the-canal (ITC) type, a receiver-in-the-canal (RIC) type, or a receiver-in-the-ear (RITE) type. The hearing aid may be a binaural hearing aid.

[0032] In one or more example hearing systems, hearing devices, and / or electronic devices, the hearing device can be considered a user hearing device, such as headphones, headsets, hearing aids, over-the-counter (OTC) hearing devices, and / or hearing protection devices. In one or more example hearing systems and / or hearing devices, the hearing device can include one or more transceivers for wireless communication. In one or more example hearing systems and / or hearing devices, the hearing device can facilitate wired communication, such as by using a cable (such as an electrical cable).

[0033] The hearing device can be configured to communicate wirelessly with one or more devices, such as with another hearing device (e.g., as part of a binaural hearing system), and / or with one or more accessory devices / electronic devices (such as a smartphone and / or smartwatch), for example via an interface. The hearing device optionally includes an antenna for converting one or more wireless input signals. The wireless input signals can come from an external signal source, such as a spouse microphone device, a wireless television audio transmitter, and / or a distributed microphone array associated with a wireless transmitter. The wireless input signals can come from, for example, another hearing device that is part of a binaural hearing system, and / or from one or more accessory devices. Sensor data as disclosed herein can be based on, for example, one or more wireless input signals from the outside. For example, this can provide information about the user's environment, which can be used to determine health parameters as disclosed herein.

[0034] The hearing device optionally includes a radio transceiver coupled to the antenna for converting the antenna output signal into a transceiver input signal. Wireless signals from different external sources can be multiplexed in the radio transceiver as the transceiver input signal, or provided as independent transceiver input signals at independent transceiver outputs of the radio transceiver. The hearing device can include multiple antennas and / or the antennas can be configured to operate in one or more antenna modes.

[0035] In one or more hearing systems, hearing devices, and / or electronic devices, one or more sensors include a microphone for providing microphone input data. In other words, one or more sensors may include a group of microphones. In one or more hearing systems and / or electronic devices, the external sensor data includes microphone input data. The microphone may include one or more microphones. The microphone may include a first microphone for providing a first microphone input signal and / or a second microphone for providing a second microphone input signal. The microphone may include N microphones for providing N microphone signals, where N is an integer between 1 and 10. In one or more example hearing devices, the number N of microphones is two, three, four, five, or more. The microphone may include a third microphone for providing a third microphone input signal. In one or more example hearing devices, the microphone input data may come from a single microphone signal, such as the first microphone signal, or a combination of microphone input signals from multiple microphones. For example, the microphone data may be based on a first microphone input signal from the first microphone and / or a second microphone input signal from the second microphone. The microphone may include an ear canal microphone and / or an ambient microphone for picking up self-sound (such as the user's own voice). This may allow filtering out external sounds and / or sounds of the user's surroundings, for example, picking up substantially only the user's voice. In one or more hearing systems, hearing devices and / or electronic devices, the health parameter is based on microphone input data.

[0036] In one or more hearing systems, hearing devices and / or electronic devices, the one or more sensors include one or more of the following: a microphone, an optical sensor, a magnetic sensor, an electrical sensor, a motion sensor and a capacitive sensor. An example of an optical sensor is a photoplethysmography (PPG) sensor. An example of a current sensor is an electrocardiogram (ECG) sensor and / or an electroencephalogram (EEG) sensor. A motion sensor can be considered an inertial measurement unit (IMU). An example of a motion sensor can include an accelerometer and / or a gyroscope. The motion sensor can, for example, include a magnetic sensor, such as a magnetometer. An example of a capacitive sensor can include a capacitive microphone and / or a body temperature sensor.

[0037] In one or more hearing systems, hearing devices, and / or electronic devices, the one or more sensors include one or more of the following: a body sensor, such as a heart rate sensor, a blood pressure sensor, a pulse sensor, a photoplethysmography (PPG) sensor, an electrocardiogram (ECG) sensor, an electroencephalogram (EEG) sensor, a bioimpedance sensor, and / or a body temperature sensor. The one or more sensors of the hearing device may include, for example, a body sensor that is a capacitance and / or conductivity sensor for measuring and / or determining stress levels.

[0038] The sensor data as disclosed herein may be obtained from one or more of the above-mentioned sensors. In other words, the sensor data may be generated and / or provided by any one or more of the above-mentioned sensors.

[0039] The hearing device includes one or more processors for processing input signals (such as transceiver input signals and / or microphone input signals). The processor is optionally configured to compensate for hearing loss of the user of the hearing device. The processor provides an electronic output signal based on the input signal entering the processor. The input terminal of the processor is optionally connected to the output terminal of the corresponding microphone and / or preprocessing unit. One or more microphone input terminals of the processor can be connected to one or more corresponding microphone output terminals of the preprocessing unit.

[0040] The hearing system, hearing device and / or electronic device may be configured to communicate wirelessly via a wireless communication system, such as a short-range wireless communication system like Wi-Fi, Bluetooth, Zigbee, IEEE 802.11, IEEE E802.15, infrared, etc.

[0041] The hearing system, hearing device and / or electronic device (such as an accessory device) can be configured to communicate wirelessly via a wireless communication system, which wireless communication system is a 3GPP system, such as a 3GPP system that supports one or more of the following: New Radio (NR), Narrowband IoT (NB-IoT) and Long Term Evolution - Enhanced Machine Type Communication (LTE-M), millimeter wave communication, such as millimeter wave communication within a licensed band, such as device-to-device millimeter wave communication within a licensed band.

[0042] In one or more hearing systems, hearing devices, and / or electronic devices, the interface of the electronic device and / or the interface of the hearing device may include one or more of the following: a Bluetooth interface, a Bluetooth low energy interface, and a magnetic induction interface. For example, the interface of the electronic device and / or the interface of the hearing device may include a Bluetooth antenna and / or a magnetic induction antenna.

[0043] The system is configured to obtain external sensor data from one or more sensors. In other words, the system is configured to obtain the external sensor data at a hearing device and / or an electronic device. In other words, the external sensor data may be obtained, received, and / or retrieved from the hearing device by the electronic device. The external sensor data may be transmitted and / or sent from the hearing device to the electronic device. The system is configured to determine, based on the external sensor data, a health parameter indicative of the cognitive status of a user of the hearing device, such as using a processor of the hearing device and / or a processor of the electronic device. In other words, the system is configured to determine, based on the external sensor data, a health parameter indicative of the cognitive status of the user of the hearing device, such as using a processor of the hearing device and / or a processor of the electronic device. The system is configured to determine, based on the external sensor data, whether the health parameter meets a first criterion indicative of cognitive decline. In other words, the system is configured to determine, based on the hearing device and / or a processor of the electronic device, whether the health parameter meets a first criterion indicative of cognitive decline. Optionally, the system is configured to output a health representation associated with the cognitive state via an interface of the hearing device and / or an interface of the electronic device, such as using a processor of the hearing device and / or a processor of the electronic device, based on the health parameter meeting the first criterion.

[0044] A hearing device is disclosed. The hearing device can be considered a hearing device with health representation and / or monitoring. The hearing device includes a memory, an interface, a processor, and one or more sensors. The processor is configured to obtain sensor data from the one or more sensors. The processor is configured to determine, based on the sensor data, a health parameter indicative of a cognitive state of a user of the hearing device. The processor is configured to determine whether the health parameter satisfies a first criterion indicative of cognitive decline. Optionally, the processor is configured to output, via the interface, a health representation associated with the cognitive state based on the health parameter satisfying the first criterion.

[0045] An electronic device is disclosed herein. The electronic device includes a memory, an interface, and a processor. The processor is configured to obtain external sensor data from a hearing device via the interface. The processor is configured to determine, based on the external sensor data, a health parameter indicative of a cognitive status of a user of the hearing device. The processor is configured to determine whether the health parameter satisfies a first criterion indicative of cognitive decline. Optionally, the processor is configured to output, via the interface, a health representation associated with the cognitive status based on the health parameter satisfying the first criterion.

[0046] As disclosed herein, external sensor data and sensor data may be used interchangeably. External sensor data may be considered sensor data obtained from a hearing device. In other words, external sensor data may be considered sensor data generated by one or more sensors of a hearing device.

[0047] In one or more hearing systems, hearing devices and / or electronic devices, the electronic device includes one or more sensors, such as one or more internal sensors. In one or more hearing systems, hearing devices and / or electronic devices, the system is configured to obtain internal sensor data from one or more sensors of the electronic device. In one or more hearing systems, hearing devices and / or electronic devices, the health parameters are based on the internal sensor data. The internal sensor data can be considered as sensor data obtained from the electronic device. In other words, the internal sensor data can be considered as sensor data generated by one or more sensors of the electronic device. It will be understood that the health parameters are determined based on sensor data (such as external sensor data) and / or internal sensor data.

[0048] The one or more sensors of an electronic device (such as an accessory device) may include one or more of the following: an optical sensor, a magnetic sensor, an electrical sensor, a motion sensor, and a capacitive sensor. An example of an optical sensor is a photoplethysmography (PPG) sensor. An example of a current sensor is an electrocardiogram (ECG) sensor and / or an electroencephalogram (EEG) sensor. A motion sensor may be considered an inertial measurement unit (IMU). An example of a motion sensor may include an accelerometer and / or a gyroscope. A motion sensor may, for example, include a magnetic sensor, such as a magnetometer. An example of a capacitive sensor may include a capacitive microphone and / or a body temperature sensor.

[0049] In one or more hearing systems, hearing devices, and / or electronic devices, one or more sensors of the electronic device (such as an accessory device) include one or more of the following: a body sensor, such as a heart rate sensor, a blood pressure sensor, a pulse oximeter sensor, a photoplethysmography (PPG) sensor, an electrocardiogram (ECG) sensor, an electroencephalogram (EEG) sensor, a bioimpedance sensor, and a body temperature sensor. The one or more sensors of the hearing device may include, for example, a body sensor that is a capacitance and / or conductivity sensor for measuring and / or determining pressure levels. Internal sensor data as disclosed herein may be obtained from one or more of the above sensors. In other words, the internal sensor data may be generated and / or provided by any one or more of the above sensors.

[0050] In one or more example hearing systems, hearing devices and / or electronic devices, the one or more sensors include one or more motion sensors, such as accelerometers, inertial motion sensors, gyroscopes, altimeters and / or position sensors (such as GPS sensors).

[0051] In one or more example hearing systems, hearing devices, and / or electronic devices, a body sensor is configured to determine body data indicative of a user's body information to provide a health parameter. In one or more example hearing systems, hearing devices, and / or electronic devices, a motion sensor is configured to determine motion data indicative of a user's motion information to provide a health parameter.

[0052] In one or more example hearing systems, hearing devices and / or electronic devices, a user may be considered a user of the hearing device and / or a user of the electronic device (eg, a user of an accessory device).

[0053] In one or more hearing system and / or electronic device examples, the electronic device is a server device.

[0054] In one or more hearing system and / or electronic device examples, the electronic device is an accessory device.

[0055] The electronic device may include an accessory device and / or a server device. The electronic device may be configured to run on the accessory device and / or the server device. In other words, the electronic device may be configured to function as a server device and / or an accessory device. For example, the accessory device may be or include a mobile phone (such as a smartphone), a smart watch, a smart speaker, a tablet computer, a computer (such as a laptop or PC), or a tablet computer. In other words, the electronic device may be, for example, a user device, such as a mobile phone or a computer, configured to perform health characterization and / or monitoring. In one or more example hearing systems and / or electronic devices, the accessory device may be considered a user accessory device, such as a mobile phone, a smart watch, a tablet computer, and / or a wearable gadget. In one or more example hearing systems, the accessory device may include one or more transceivers for wireless communication. In one or more example hearing systems, the accessory device may facilitate wired communication, such as by using a cable (such as an electrical cable).

[0056] The server device may be configured on a cloud, such as a cloud network. Different operations configured to be performed by an electronic device and / or system as disclosed herein may be performed at different devices, such as at an electronic device and / or at a server device.

[0057] As disclosed herein, health parameters indicate the cognitive state of a hearing device user. Health parameters can also be considered and / or represent cognitive parameters. In other words, health parameters can indicate information associated with the cognitive state of a hearing device user. The user's cognitive state can indicate the user's ability to hear, speak, see, and / or move. In other words, health parameters can indicate the user's ability to hear, speak, see, and / or move. For example, cognitive parameters can be considered a set of features that together constitute a biomarker. For example, based on microphone input data, cognitive parameters can be considered a set of audio features that together constitute an acoustic biomarker. For example, based on physiological data, cognitive parameters can be considered one or more physiological features that together constitute a physiological biomarker. For example, based on biodynamic data, cognitive parameters can be considered one or more biodynamic features that together constitute a biodynamic biomarker. Health parameters can indicate the user's cognitive load. Health parameters can be determined based on one or more biomarkers as disclosed herein, such as one or more acoustic biomarkers, physiological biomarkers, and / or biodynamic biomarkers. For example, cognitive load can be determined based on one or more biomarkers as disclosed herein, such as one or more acoustic biomarkers, physiological biomarkers, and / or biodynamic biomarkers.

[0058] It will be appreciated that the health parameter may include a score indicating the cognitive state of the user of the hearing device, such as a health score. In other words, determining the health parameter may include determining a health score indicating the cognitive state of the user. Determining whether the health parameter meets the first criterion indicating cognitive decline may include determining whether the health score meets the first criterion. For example, when the score is higher than or equal to a threshold (such as a first threshold), it may be determined that the score meets the criterion (such as the first criterion). The cognitive state may indicate the degree of cognitive ability. Cognitive ability may include memory ability, stress tolerance (ability to work under load), language ability, thinking ability and / or judgment ability. For example, the cognitive state may indicate mild cognitive impairment. Mental illnesses such as anxiety and depression may affect the user's cognitive ability. Therefore, the cognitive state may indicate a mental illness.

[0059] In one or more example hearing systems, based on the health parameter not meeting a first criterion, the system is configured to refrain from outputting a health representation associated with the cognitive state.

[0060] In one or more example hearing devices, based on the health parameter not meeting a first criterion, a processor of the hearing device is configured to refrain from outputting a health representation associated with the cognitive state.

[0061] In one or more example electronic devices, based on the health parameter not meeting a first criterion, a processor of the electronic device is configured to refrain from outputting a health representation associated with the cognitive state.

[0062] The first criterion may include a threshold value indicating cognitive decline in the user of the hearing device, such as a health threshold and / or a cognitive threshold. The first criterion may be based on one or more user parameters. For example, the first criterion may be based on the user's age. Thus, the first criterion may indicate cognitive decline with respect to one or more user parameters. For example, the first criterion may indicate cognitive decline with respect to the user's age, for example, compared to normal cognitive abilities for a person of the same age. The one or more user parameters may also include historical user data of the user of the hearing device. Thus, based on the historical data, the first criterion may indicate a decline in the user's cognitive abilities over time. The first criterion may be based on historical health parameters of the user of the hearing device. Thus, whether the user has experienced cognitive decline over time can be determined based on the user's own historical health parameters (such as cognitive abilities).

[0063] Determining whether the health parameter satisfies the first criterion may include determining whether the health parameter is above, below, or equal to a health threshold, and / or within a certain range indicative of the first criterion. In other words, the health parameter may satisfy the first criterion when it is above or equal to the first threshold. For example, the health parameter may satisfy the first criterion when it is above or equal to a first threshold indicative of cognitive decline. In other words, when the health parameter satisfies the first criterion, the user of the hearing device may be considered to suffer from cognitive decline. That is, when the health parameter satisfies the first criterion, one or more cognitive abilities of the user of the hearing device are determined to be declined. For example, the user's ability to hear, speak, see, and / or move may be determined to be declined.

[0064] Cognitive decline in a health parameter (such as a user's cognitive state) can be determined based on the user's previous (such as historical) cognitive state and / or based on a person's normal cognitive state. In other words, cognitive decline in a user's cognitive state can be determined by comparing it to the user's historical cognitive state and / or the normal cognitive state of a person of approximately the same age, approximately the same gender, and / or approximately the same physiological state. Cognitive decline can be viewed as a deterioration in cognitive abilities, such as memory, attention, problem solving, speech ability, and / or language skills. In other words, cognitive decline can be viewed as a deterioration in cognitive abilities over time, for example, compared to the user's historical cognitive state and / or the normal cognitive state of a person of approximately the same age, approximately the same gender, and / or approximately the same physiological state.

[0065] In one or more example hearing systems, the system is configured to determine (such as at an electronic device and / or a hearing device) a health indicator associated with a cognitive state based on sensor data (such as based on external sensor data). In other words, the system is configured to determine, such as using a processor of the electronic device and / or a processor of the hearing device, a health indicator associated with a cognitive state based on sensor data (such as based on external sensor data).

[0066] In one or more example electronic devices, a processor is configured to determine a health representation associated with a cognitive state based on sensor data, such as from external sensor data.

[0067] In one or more example hearing devices, a processor is configured to determine a health parameter associated with a cognitive state based on sensor data. In one or more example hearing systems, the system includes a machine learning circuit configured to operate according to a machine learning model. In one or more example hearing systems, determining the health parameter includes using a machine learning model to determine the health parameter based on external sensor data, such as using an electronic device and / or a hearing device. In other words, the electronic device and / or the hearing device may include a machine learning circuit configured to operate according to the machine learning model. In other words, the system may be configured to determine the health parameter based on external sensor data using a machine learning model (such as using an electronic device and / or a hearing device).

[0068] In one or more example hearing devices, the processor includes a machine learning circuit configured to operate according to the machine learning model. In one or more example hearing devices, determining the health parameter includes determining the health parameter using the machine learning model based on the sensor data.

[0069] In one or more example electronic devices, the processor includes a machine learning circuit configured to operate according to a machine learning model. In one or more example electronic devices, determining the health parameter includes determining the health parameter using the machine learning model based on external sensor data.

[0070] In other words, the electronic device and / or hearing device may include one or more processors including a machine learning engine configured to operate according to a machine learning model. The machine learning circuitry may be considered a predictor circuit configured to operate according to a prediction model. The machine learning circuitry may be configured to predict a health parameter using a prediction model based on sensor data (such as external sensor data).

[0071] For example, a machine learning model may include or utilize neural networks, artificial intelligence, deep learning, and / or machine learning.

[0072] In one or more example hearing systems, electronic devices and / or hearing devices, the machine learning model includes a model layer, which includes an input layer, one or more intermediate layers, and an output layer for providing health parameters. In one or more example hearing systems, electronic devices and / or hearing devices, the machine learning model includes a neural network. In one or more example hearing systems, electronic devices and / or hearing devices, the machine learning model includes a neural network layer, which includes an input layer, one or more intermediate layers, and an output layer for providing health parameters. In other words, the input layer, the one or more intermediate layers, and / or the output layer can be considered as layers of the machine learning model, such as layers of a neural network. The one or more intermediate layers can be considered as hidden layers (such as hidden features). The one or more intermediate layers can include a first intermediate layer. The machine learning model can include a lookup table with multiple parameters (such as at least four parameters).

[0073] As referred to herein, models (such as machine learning models) can be viewed as models and / or schemes and / or mechanisms and / or methods that are configured to provide one or more health parameters based on operational data (such as sensor data, e.g., audio input data) and / or previous models. As referred to herein, models (such as machine learning models) can be based on the same model architecture. The model architecture can be based on, for example, a neural network comprising one or more different types of layers and / or number of layers. The model architecture can be viewed as a configuration of the model, which configuration, for example, includes one or more parameters of the model.

[0074] In one or more example hearing systems, electronic devices, and / or hearing devices, a model as described herein can be stored on a non-transitory storage medium (e.g., on a memory of the electronic device and / or hearing device). The model can be stored on a non-transitory storage medium of an electronic device and / or hearing device configured to execute the model. In one or more example hearing systems, electronic devices, and / or hearing devices, the model can include model data and / or computer-readable instructions (e.g., based on sensor data and / or audio signals, such as historical sensor data). The model data and / or computer-readable instructions can be used by the electronic device and / or hearing device. The hearing device and / or electronic device can use the model (such as the model data and / or computer-readable instructions) to determine health parameters and health indicators. In other words, the model (such as the model data and / or computer-readable instructions) can be used by the hearing device and / or electronic device to determine one or more parameters, features, and / or biomarkers as described herein, such as health parameters, acoustic biomarkers, physiological biomarkers, and / or biodynamic biomarkers. In general, biomarkers as disclosed herein (such as acoustic biomarkers, physiological biomarkers, and / or biodynamic biomarkers) can indicate and / or represent a user's energy usage and / or levels. Energy usage and / or levels can provide an indication of the user's cognitive state (such as cognitive load).

[0075] In one or more example electronic devices, machine learning circuitry includes a neural network module configured to operate according to a neural network.

[0076] In one or more example hearing systems, electronic devices, and / or hearing devices, the neural network is a deep neural network, such as a convolutional neural network and / or a recurrent neural network. For example, the neural network can include a one-dimensional convolutional neural network or a two-dimensional convolutional neural network.

[0077] In one or more example hearing systems, electronic devices, and / or hearing devices, the machine learning circuitry includes a regressor module configured to operate according to a regression model.

[0078] The machine learning model can be based on a neural network (such as a convolutional neural network, a deep learning neural network, a recursive neural network, and / or a combination of learning circuits). The machine learning circuit can be configured to determine (and optionally recognize) one or more patterns in existing data (sensor data, audio input data, sound patterns (such as sound patterns), and / or health parameters) to facilitate the determination and / or prediction of subsequent health parameters. For example, the machine learning circuit can be configured to determine (such as recognize) a health parameter based on sensor data and / or audio input data over a period of time.

[0079] The machine learning circuit (such as a neural network module and / or a regressor module) can be configured to operate according to a machine learning scheme that is configured to determine a rule, pattern, or relationship that maps an input to an output, so that when a subsequent new input is provided, the machine learning circuit can accurately predict the correct output based on the rule, pattern, or relationship. In one or more embodiments, the machine learning model can first extract one or more features from the input sensor data, such as by using signal processing methods (such as filters), statistics of the signal (such as mean, maximum, median, and / or magnitude), and / or results from unsupervised learning methods (such as dimensionality reduction methods, clustering, and / or autoencoders). The one or more features can then be input into a regression and / or classification model trained using machine learning techniques.

[0080] In one or more example hearing systems, electronic devices, and / or hearing devices, the system is configured to train a machine learning model to provide an updated machine learning model and transmit the updated machine learning model to the electronic device and / or hearing device. In other words, the server device is configured to train the machine learning model to provide an updated machine learning model and transmit the updated machine learning model to the electronic device and / or hearing device.

[0081] In one or more example hearing systems, electronic devices, and / or hearing devices, a server device is configured to train and / or update a machine learning model based on one or more of the sensor data and the health parameters. In one or more embodiments, the processor can be configured to train and / or update the machine learning model based on the results of the health representation (e.g., by comparing the health parameters to known health parameters). The machine learning circuit operates according to the machine learning model, which can be trained and / or updated (e.g., retrained or fine-tuned). The training of the machine learning model can be a supervised learning setting, where the sensor data in the input data can be labeled. The machine learning model or changes to the machine learning model can be based on new data, such as new sensor data and / or new prediction data.

[0082] As disclosed herein, a health representation can be considered a representation that indicates all or part of a user's health parameters and / or cognitive status. In other words, a health representation can be considered a cognitive status representation. A health representation can be considered and / or include an assessment of a user's cognitive status. In other words, a health representation can be considered and / or include an assessment of a user's cognitive abilities. In one or more example hearing systems, electronic devices, and / or hearing devices, the health representation includes a score, such as a cognitive score, that indicates a representation of the user's cognitive status (e.g., while wearing the hearing device). The health representation can indicate whether the user's cognitive status indicates cognitive decline. Thus, the health representation can provide feedback about the user's cognitive status and indicate whether to take action. For example, when a health parameter meets a criterion, the score can indicate cognitive decline. For example, a processor of the system, electronic device, and / or hearing device can be configured to determine whether the score meets a criterion (such as a first criterion). When the score is greater than or equal to a threshold (such as a first threshold), it can be determined that the score meets the criterion (such as the first criterion). The health representation can include information regarding the degree of cognitive decline. For example, the health representation can indicate whether the user suffers from mild cognitive decline, moderate cognitive decline, severe cognitive decline, or severe cognitive decline. Mild cognitive decline, moderate cognitive decline, severe cognitive decline and / or severe cognitive decline can indicate and / or represent the degree of cognitive decline, for example, the progression from mild cognitive impairment to dementia, where mild cognitive decline can be considered as mild cognitive impairment and severe cognitive decline can be considered as the progression of dementia. Cognitive decline is not necessarily related to a mental disorder or mental condition, but can be caused by one or more factors, such as: fatigue, aging, overstimulation, lifestyle factors, medications and stress. Cognitive decline can be temporary and, for example, can be triggered by one or more of the above factors. For example, a deterioration in a user's cognitive ability can be determined or detected over a period of time, but after a period of time, the user's cognitive ability can return to the state before the detected deterioration. The determination of cognitive decline can be different from the determination of cognitive load or the determination of a mental illness.

[0083] In one or more example hearing systems, electronic devices, and / or hearing instruments, the health representation includes a representation of a health parameter. For example, the health representation may include a representation of a biomarker as described herein. The health representation may include one or more of an acoustic biomarker representation, a physiological biomarker representation, and a biodynamic biomarker representation.

[0084] In one or more example hearing systems, electronic devices, and / or hearing devices, the health representation represents an assessment of a user's cognitive state.

[0085] In one or more example hearing systems, electronic devices, and / or hearing devices, outputting the health representation may include outputting the health representation via an interface of the electronic device and / or an interface of the hearing device.

[0086] In one or more example hearing systems and / or electronic devices, outputting the health representation includes displaying a user interface representing the health representation via an interface of the electronic device. In other words, outputting the health representation includes displaying a user interface representing the health representation via an interface of an accessory device as disclosed herein.

[0087] Outputting the health representation may include displaying a user interface indicating the health representation. In one or more example hearing systems and / or electronic devices, outputting the health representation may include outputting a first health representation, a second health representation, a third health representation, etc. via an interface of the electronic device.

[0088] Outputting the health representation may include displaying a user interface indicative of the health representation.

[0089] A user interface may include one or more (such as multiple) user interface objects. For example, a user interface may include one or more user interface objects, such as a first user interface object and / or a second user interface object. A user interface object, as used herein, may refer to a graphical representation of an object displayed on an interface (such as a display) of an electronic device. A user interface object may be user interactive or selectable via user input. For example, an image (such as an icon), a button, and text (such as a hyperlink) may each optionally constitute a user interface object. A user interface object may form part of a widget. A widget may be considered a mini-application that can be used by a user. Outputting a health representation may include outputting a health representation including one or more of the following: text (such as a text string) and / or a phrase, a score (such as an assessment score and / or a cognitive score), image data (such as one or more images), a sound, audio information, and / or a user interface object including one or more of the foregoing items. For example, outputting a health representation may include outputting a health representation including a report of the user's cognitive status. For example, outputting a health representation may include outputting a health representation including a score (such as an assessment score of the cognitive status of a user using a hearing device).

[0090] In one or more example hearing systems, electronic devices, and / or hearing devices, based on a health parameter that satisfies a first criterion, a processor of the system, electronic device, and / or hearing device is configured to determine a first recommendation. The first recommendation can be considered feedback provided to a user of the hearing device regarding the user's cognitive state. The first recommendation can be considered a first assessment. The first recommendation can be a recommendation to avoid and / or reduce cognitive decline. Based on the health parameter that satisfies the first criterion, the first recommendation can be considered and / or include a proposed action that the user of the hearing device should take and / or something that the user should avoid (such as stopping). For example, the first suggestion may include text (such as a message to the user) and / or phrases such as “Cognitive decline has been identified, please contact a healthcare professional and / or your doctor (such as a general practitioner)”, “Please take a cognitive test”, “Please increase the use of hearing devices to reduce cognitive decline”, “Please take your hearing devices to an audiologist to adjust and / or update the hearing device configuration to reduce cognitive decline”, “Please increase brain activity and / or motor activity to reduce your cognitive decline”, “Please get a diabetes check”, “Hypertension has been identified, please contact a healthcare professional and / or your doctor (such as a general practitioner)”, “If you smoke, it is recommended that you quit smoking or at least reduce smoking”, “Please try to improve the air quality in your environment, such as reducing the air pollution you are exposed to”, “Please try to lose some weight to reduce your obesity”, “Please exercise regularly and / or increase your physical activity”, “If you have depression, seek treatment”, “Please avoid excessive drinking” and / or “Please try to maintain frequent social contact”.

[0091] In one or more example hearing systems, electronic devices, and / or hearing devices, a processor of the system, electronic device, and / or hearing device is configured to output a first suggestion via an interface (such as an interface of the electronic device and / or hearing device). In other words, the processor can be configured to output the first suggestion in the form of text (such as a message to a user) and / or a phrase, an assessment score, image data (such as one or more images), audio information (e.g., output to the user via a receiver of the hearing device), and / or a user interface as described herein.

[0092] In one or more example hearing systems, electronic devices, and / or hearing instruments, the first suggestion is included in a health representation. In other words, the processor of the system, electronic device, and / or hearing instrument is configured to include the first suggestion in the health representation. For example, the processor of the system, electronic device, and / or hearing instrument is configured to output the health representation including the first suggestion.

[0093] In one or more example hearing systems and / or electronic devices, based on a first criterion being met, the system is configured to perform a cognitive testing protocol via an interface of the electronic device. In other words, based on a first criterion being met, the system is configured to perform a cognitive testing protocol via an interface of an accessory device. In one or more example hearing systems and / or electronic devices, based on a first criterion being met, the system is configured to perform a cognitive testing protocol via an interface of a hearing device.

[0094] For example, the system can be configured to output and / or display a user interface representing a cognitive test protocol, for example, via an interface of an electronic device (such as a display) and / or via an interface of a hearing device (such as via a receiver of the hearing device). The cognitive test protocol can be configured to be executed by a user of the hearing device on the electronic device. Executing the cognitive test protocol can include, for example, outputting one or more of audio data, visual data, or textual data (such as a questionnaire) via an interface of the electronic device (such as a display) and / or via an interface of the hearing device (such as via a receiver of the hearing device), and receiving one or more user inputs via the interface of the electronic device and / or via an interface of the hearing device. For example, upon determining that a first criterion is met, the cognitive test protocol can be executed to obtain more information about the cognitive state of the user of the hearing device. The health indicator can be based on health parameters and user input from the cognitive test protocol. For example, the cognitive test protocol can include having the user of the hearing device repeat and / or speak one or more audio samples, for example, to test the user's vocal biomarkers while repeating the audio samples. For example, the cognitive test protocol can test one or more of the following: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, vocal pitch parameters, and speech rhythm parameters.

[0095] A cognitive testing protocol may, for example, involve having the user of the hearing device perform one or more actions and / or activities to measure physiological and / or biodynamic biomarkers of the user while performing the one or more actions and / or activities. For example, the cognitive testing protocol may measure one or more of the following: blood pressure parameters, blood flow parameters, heart rate parameters, respiratory parameters, body temperature parameters, oxygen parameters, and brain activity parameters. The cognitive testing protocol may also measure one or more of the following: movement parameters, tremor parameters, jitter parameters, and twitch parameters.

[0096] In one or more example hearing systems and / or electronic devices, a cognitive testing protocol may be included in a health representation and / or the health representation may be based on a cognitive testing protocol.

[0097] In one or more example hearing systems, electronic devices, and / or hearing devices, determining a health parameter includes determining a first sound biomarker based on microphone input data. For example, determining the health parameter may include determining a second sound biomarker, a third sound biomarker, a fourth sound biomarker, a fifth sound biomarker, and / or a sixth sound biomarker. Determining a sound biomarker (such as the first sound biomarker) may include extracting one or more features from the sensor data. Determining a sound biomarker (such as the first sound biomarker) may include extracting one or more audio features from the microphone input data. A sound biomarker may be considered a biomarker that indicates one or more parameters of a sound (such as the sound of a user of the hearing device). A sound biomarker may include and / or indicate a feature in the microphone input data, such as a sound feature.

[0098] In one or more example hearing systems, electronic devices, and / or hearing instruments, the first criterion is based on a first sound biomarker. In other words, the first criterion includes a first sound biomarker criterion. In other words, determining whether the health parameter satisfies the first criterion may include determining whether the health parameter satisfies the first sound biomarker criterion.

[0099] In one or more example hearing systems, electronic devices, and / or hearing devices, determining a first acoustic biomarker includes determining one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter. In one or more example hearing systems, the system is configured to determine, such as using the electronic device and / or hearing device, one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter.

[0100] In one or more example electronic devices, a processor of the electronic device is configured to determine one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter.

[0101] In one or more example hearing devices, a processor of the hearing device is configured to determine one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a sound pitch parameter, and a speech rhythm parameter.

[0102] In one or more example hearing systems, electronic devices, and / or hearing devices, the first sound biomarker is based on one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a sound pitch parameter, and a speech rhythm parameter.

[0103] Language parameters can be considered as parameters that indicate language biomarkers. In other words, language parameters can include specific semantically related words and / or sentences, such as words and / or sentences that are incompletely pronounced by the user, which can indicate cognitive decline. For example, language parameters can indicate that the user is undergoing vocabulary and / or grammatical simplification, and / or loss of semantic ability. Language parameters can be determined based on a text transcript of microphone input data. The text transcript can be obtained by an automatic speech recognition (speech to text) algorithm or service. Language parameters can include embedded features performed by a deep neural network (e.g., a BERT transformer network or other sequence to sequence autoencoder).

[0104] Acoustic parameters can be considered as parameters that indicate acoustic biomarkers. In other words, acoustic parameters can include specific sounds and / or acoustic features, such as acoustic sounds emitted by the user and / or acoustic features in the user's voice, which acoustic sounds and / or acoustic features can indicate cognitive decline. Acoustic parameters can include one or more of the following: pitch, energy level, spectral parameters (Mel-frequency cepstrum, MFCC; e.g., logMelSpec), spectral statistics (slope, roll-off point), speech spectral envelope features (e.g., formants, harmonics, ratios of harmonics and formants), and / or sound quality metrics such as harmonics to noise ratio, HNR, jitter, and / or amplitude perturbations.

[0105] The speech fluency parameter can be considered a parameter that indicates a speech fluency biomarker. In other words, the speech fluency parameter can include information about the speech fluency of the user when speaking. For example, when the user's speech fluency decreases and / or indicates low speech fluency, the speech fluency parameter can indicate cognitive decline. For example, the user of the hearing device may increasingly search for words when speaking, which can indicate a decrease in speech fluency. For example, the speech fluency parameter can indicate that the user stutters when speaking and / or that the stuttering increases when speaking. For example, the speech fluency parameter can indicate that the user has tongue twisters and / or frequently uses filler sounds such as "uh," "hmm," semantic errors, uncertain terms, revisions, repetitions, and / or neologisms.

[0106] Determining the first acoustic biomarker may include determining a coherence parameter. The coherence parameter may include information regarding the user's coherence when speaking. For example, when the user's speech becomes less coherent and / or indicates low coherence, the coherence parameter may indicate cognitive decline. For example, a user of a hearing device may increasingly speak untrustworthy and / or irrelevant details.

[0107] It will be appreciated that the use of acoustic biomarkers is a simple, non-invasive method of characterizing and / or monitoring health parameters.

[0108] The first criterion may include a first speech fluency criterion. In other words, determining whether the health parameter meets the first criterion may include determining whether the health parameter meets the first speech fluency criterion. For example, the first threshold may include a first speech fluency threshold. When the speech fluency parameter is below the first speech fluency threshold, the first criterion is met and the user may be experiencing cognitive decline.

[0109] A mumbling parameter can be considered a parameter indicative of a mumbling biomarker. In other words, the mumbling parameter can include information regarding the user's mumbling while speaking. For example, when the user's mumbling increases and / or indicates a high level of mumbling, the mumbling parameter can indicate cognitive decline. Determining the mumbling parameter can include determining that the user speaks softly and incoherently. In other words, determining the mumbling parameter can include determining that the user is not formulating their words clearly and / or that the user's speech is slurred, such as unintelligible speech and / or words.

[0110] The first criterion may include a first mumbling criterion. In other words, determining whether the health parameter meets the first criterion may include determining whether the health parameter meets the first mumbling criterion. For example, the first threshold may include a first mumbling threshold. When the mumbling parameter is above the first mumbling threshold, the first criterion is met and the user may be experiencing cognitive decline.

[0111] The voice pitch parameter can be considered a parameter indicating a voice pitch biomarker. In other words, the voice pitch parameter can include information about the pitch of the user's voice when the user speaks (such as pitch variation). For example, when the pitch of the user's voice increases and / or decreases, the voice pitch parameter can indicate cognitive decline.

[0112] The first criterion may include a first sound pitch criterion. In other words, determining whether the health parameter meets the first criterion may include determining whether the health parameter meets the first sound pitch criterion. For example, the first threshold may include a first sound pitch threshold. When the sound pitch parameter is above the first sound pitch threshold, the first criterion is met, and the user may experience cognitive decline.

[0113] The speech rhythm parameter can be considered a parameter that indicates a speech rhythm biomarker. In other words, the speech rhythm parameter can include information about the user's speech rhythm when the user speaks. For example, if the user's speech rhythm decreases, the speech rhythm parameter can indicate cognitive decline. Another example could be that the speech rhythm indicates that the user is hesitant when speaking.

[0114] The first criterion may include a first speech cadence criterion. In other words, determining whether the health parameter meets the first criterion may include determining whether the health parameter meets the first speech cadence criterion. For example, the first threshold may include a first speech cadence threshold. When the speech cadence parameter is above the first speech cadence threshold, the first criterion is met, and the user may be experiencing cognitive decline.

[0115] Pitch features and speech rhythm features can be regarded as prosodic features.

[0116] In one or more example hearing systems, electronic devices, and / or hearing devices, the sensor data (such as external sensor data) includes physiological data. In one or more example hearing systems, electronic devices, and / or hearing devices, the health parameter is based on the physiological data.

[0117] In one or more example hearing systems and / or electronic devices, the one or more sensors include one or more physiological sensors for providing physiological data. For example, the physiological sensors may include one or more of the following: body sensors, such as a heart rate sensor, a blood pressure sensor, a pulse sensor, a photoplethysmography (PPG) sensor, an electrocardiogram (ECG) sensor, an electroencephalogram (EEG) sensor, a bioimpedance sensor, and a body temperature sensor. The one or more sensors of the hearing device may include, for example, a body sensor that is a capacitance and / or conductivity sensor for measuring and / or determining stress levels.

[0118] In one or more example hearing systems, electronic devices, and / or hearing devices, determining a health parameter includes determining a first physiological biomarker based on physiological data. For example, determining the health parameter may include determining a second, third, fourth, fifth, and / or sixth physiological biomarker. Determining a physiological biomarker, such as the first physiological biomarker, from sensor data may include extracting one or more features, such as physiological features, from the sensor data. Determining a physiological biomarker, such as the first physiological biomarker, may include extracting one or more physiological features from the physiological data. A physiological biomarker may be considered a biomarker that indicates one or more parameters of a physiological condition of a user of the hearing device. A physiological biomarker may include and / or indicate features, such as physiological features, in the physiological data. The physiological data may indicate and / or represent one or more physiological processes. For example, the physiological data may indicate and / or represent one or more physiological processes, such as changes in the user's physiological processes. For example, changes in the user's physiological processes may include changes in the throat and / or chest, as well as changes in nerves that control the physiological processes. Physiological processes may be affected by cognitive decline, and changes in physiological processes may be identified and / or detected as cognitive decline in the user.

[0119] In one or more example hearing systems, electronic devices, and / or hearing instruments, the first criterion is based on a first physiological biomarker. In other words, the first criterion includes a first physiological biomarker criterion. In other words, determining whether the health parameter satisfies the first criterion may include determining whether the health parameter satisfies the first physiological biomarker criterion.

[0120] In one or more example hearing systems, electronic devices, and / or hearing devices, determining the first physiological biomarker includes determining one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter. In one or more example hearing systems, the system is configured to determine, such as using the electronic device and / or hearing device, one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter.

[0121] In one or more example electronic devices, a processor of the electronic device is configured to determine one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter.

[0122] In one or more example hearing devices, the processor of the hearing device is configured to determine one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter.

[0123] In one or more example hearing systems, electronic devices, and / or hearing devices, the first physiological biomarker is based on one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter.

[0124] Blood pressure parameters can be considered indicators of blood pressure biomarkers. In other words, blood pressure parameters can include information about a user's blood pressure while wearing a hearing device. For example, if a user's blood pressure is experiencing abnormal changes, such as increases and / or decreases, the blood pressure parameters can indicate cognitive decline and / or an increased risk of cognitive decline.

[0125] The first criterion may include a first blood pressure criterion. In other words, determining whether the health parameter satisfies the first criterion may include determining whether the health parameter satisfies the first blood pressure criterion. For example, the first threshold may include a first blood pressure threshold and / or a second blood pressure threshold. When the blood pressure parameter is above the first blood pressure threshold and / or below the second blood pressure threshold, the first criterion is satisfied, and the user may be experiencing cognitive decline and / or be at increased risk of cognitive decline.

[0126] Blood flow parameters can be considered parameters that indicate blood flow biomarkers. In other words, blood flow parameters can include information about a user's blood flow while wearing a hearing device. For example, when a user's blood flow is experiencing abnormal changes, such as increases and / or decreases, the blood flow parameters can indicate cognitive decline and / or an increased risk of cognitive decline.

[0127] The first criterion may include a first blood flow criterion. In other words, determining whether the health parameter satisfies the first criterion may include determining whether the health parameter satisfies the first blood flow criterion. For example, the first threshold may include a first blood flow threshold and / or a second blood flow threshold. When the blood flow parameter is above the first blood flow threshold and / or below the second blood flow threshold, the first criterion is satisfied and the user may be experiencing cognitive decline and / or at increased risk of cognitive decline.

[0128] Heart rate parameters can be considered parameters that indicate a heart rate biomarker. In other words, heart rate parameters can include information about a user's heart rate while wearing a hearing device. For example, if a user's heart rate is experiencing unusual changes, such as increases and / or decreases, the heart rate parameter can indicate cognitive decline. Heart rate parameters can also indicate heart rate variability.

[0129] The first criterion may include a first heart rate criterion. In other words, determining whether the health parameter meets the first criterion may include determining whether the health parameter meets the first heart rate criterion. For example, the first threshold may include a first heart rate threshold and / or a second heart rate threshold. When the heart rate parameter is above the first heart rate threshold and / or below the second heart rate threshold, the first criterion is met and the user may be experiencing cognitive decline and / or at increased risk of cognitive decline.

[0130] Respiratory parameters can be considered parameters that indicate respiratory biomarkers. In other words, respiratory parameters can include information about a user's breathing conditions while wearing a hearing device. For example, if a user's breathing rate is experiencing abnormal changes, such as increases and / or decreases, respiratory parameters can indicate cognitive decline and / or an increased risk of cognitive decline.

[0131] The first criterion may include a first breathing criterion. In other words, determining whether the health parameter meets the first criterion may include determining whether the health parameter meets the first breathing criterion. For example, the first threshold may include a first respiratory rate threshold and / or a second respiratory rate threshold. When the respiratory parameter is above the first respiratory rate threshold and / or below the second respiratory rate threshold, the first criterion is met and the user may be experiencing cognitive decline and / or at increased risk of cognitive decline.

[0132] Body temperature parameters can be considered parameters that indicate a body temperature biomarker. In other words, the body temperature parameters may include information about the user's body temperature while the user is wearing the hearing device. For example, when the user's body temperature is changing abnormally, such as increasing and / or decreasing, the body temperature parameters may indicate cognitive decline and / or that the user may be at increased risk of cognitive decline.

[0133] The first criterion may include a first body temperature criterion. In other words, determining whether the health parameter meets the first criterion may include determining whether the health parameter meets the first body temperature criterion. For example, the first threshold may include a first body temperature threshold and / or a second body temperature threshold. When the body temperature parameter is above the first body temperature threshold and / or below the second body temperature threshold, the first criterion is met and the user may be experiencing cognitive decline and / or have an increased risk of cognitive decline.

[0134] Oxygen parameters can be considered parameters that indicate oxygen biomarkers. In other words, oxygen parameters can include information about a user's oxygen consumption while wearing a hearing device. For example, if a user's oxygen consumption rate is experiencing abnormal changes, such as increases and / or decreases, oxygen parameters can indicate cognitive decline and / or that the user may be at increased risk for cognitive decline.

[0135] The first criterion may include a first oxygen criterion. In other words, determining whether the health parameter satisfies the first criterion may include determining whether the health parameter satisfies the first oxygen criterion. For example, the first threshold may include a first oxygen threshold. When the oxygen parameter is below the first oxygen threshold, the first criterion is satisfied and the user may be experiencing cognitive decline and / or the user may be at increased risk of cognitive decline.

[0136] Brain activity parameters can be considered parameters that indicate brain activity biomarkers. In other words, brain activity parameters may include information about a user's brain activity while wearing a hearing device. For example, when a user's brain activity is experiencing abnormal changes, such as increases and / or decreases, the brain activity parameters may indicate cognitive decline and / or an increased risk of cognitive decline.

[0137] The first criterion may include a first brain activity criterion. In other words, determining whether the health parameter satisfies the first criterion may include determining whether the health parameter satisfies the first brain activity criterion. For example, the first threshold may include a first brain activity threshold. When the brain activity parameter is below the first brain activity threshold, the first criterion is satisfied and the user may be experiencing cognitive decline and / or is at increased risk of cognitive decline.

[0138] In one or more example hearing systems, electronic devices, and / or hearing devices, the sensor data (such as external sensor data) includes biokinetic data. In one or more example hearing systems, electronic devices, and / or hearing devices, the health parameter is based on the biokinetic data.

[0139] In one or more example hearing systems and / or electronic devices, the one or more sensors include one or more biokinetic sensors for providing biokinetic data. For example, the biokinetic sensors may include one or more of: a motion sensor, an IMU sensor, an accelerometer, and / or a gyroscope. The biokinetic sensors may be configured to measure one or more movements of a user of the hearing device. For example, the biokinetic sensors may be configured to measure one or more head movements of a user of the hearing device.

[0140] In one or more example hearing systems, electronic devices, and / or hearing devices, determining a health parameter includes determining a first biokinetic biomarker based on biokinetic data. Determining the health parameter may, for example, include determining a second biokinetic biomarker, a third biokinetic biomarker, a fourth biokinetic biomarker, a fifth biokinetic biomarker, and / or a sixth biokinetic biomarker. Determining a biokinetic biomarker, such as the first biokinetic biomarker, may include extracting one or more features, such as a biokinetic feature, from the sensor data. Determining a biokinetic biomarker, such as the first biokinetic biomarker, may include extracting one or more biokinetic features from the biokinetic data. A biokinetic biomarker may be considered a biomarker that indicates one or more movements of a user of the hearing device. The biokinetic biomarker may include and / or indicate a feature, such as a biokinetic feature, in the biokinetic data.

[0141] In one or more example hearing systems, electronic devices, and / or hearing instruments, the first criterion is based on a first biodynamic biomarker. In other words, the first criterion includes a first biodynamic biomarker criterion. In other words, determining whether the health parameter satisfies the first criterion may include determining whether the health parameter satisfies the first biodynamic biomarker criterion.

[0142] In one or more example hearing systems, electronic devices, and / or hearing devices, determining the first biodynamic biomarker includes determining one or more of: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter.

[0143] In one or more example hearing systems, the system is configured to determine one or more of: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter, such as using electronics and / or a hearing device.

[0144] In one or more example electronic devices, a processor of the electronic device is configured to determine one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter.

[0145] In one or more example hearing devices, the processor of the hearing device is configured to determine one or more of: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter.

[0146] In one or more example hearing systems, electronic devices, and / or hearing devices, the first biodynamic biomarker is based on one or more of: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter.

[0147] Motion parameters can be considered parameters that indicate motion biomarkers. In other words, motion parameters can include information about the user's motion while wearing the hearing device. For example, if the user's motion is changing abnormally, such as increasing and / or decreasing, the motion parameters can indicate cognitive decline.

[0148] The tremor parameter can be considered a parameter that indicates a tremor biomarker. In other words, the tremor parameter can include information about the user's tremor while wearing the hearing device. For example, if the user's tremor is changing abnormally, such as increasing and / or decreasing, the tremor parameter can indicate cognitive decline.

[0149] The jitter parameter can be considered a parameter that indicates a jitter biomarker. In other words, the jitter parameter can include information about the user's jitter when the user wears the hearing device. For example, when the user's jitter is changing abnormally, such as increasing, the jitter parameter can indicate cognitive decline.

[0150] Tic parameters can be considered parameters that indicate a tic biomarker. In other words, a tic parameter can include information about one or more tics experienced by a user while wearing a hearing device. For example, if a user's tics are changing abnormally, such as increasing, the tic parameter can indicate cognitive decline.

[0151] It should be understood that the description of features relating to a hearing device and / or an electronic device also applies to corresponding features in the system and / or method of operating a hearing system, method for operating a hearing device and / or method for operating an electronic device as disclosed herein.

[0152] Figure 1 An example hearing system according to the present disclosure is schematically shown, such as the hearing system 2. The hearing system 2 comprises an electronic device 10 comprising a memory 10A, an interface 10B and a processor 10C. The hearing system 2 comprises a hearing device 30 comprising a memory 30A, an interface 30A, a processor 30C and one or more sensors 30D.

[0153] The electronic device 10 includes a memory 10A, an interface 10B (such as one or more interfaces), and a processor 10C.

[0154] Optionally, the electronic device is a server device 20. In other words, the hearing system 2 may comprise a server device 20. The server device 20 comprises a memory 20A, an interface 20B (such as one or more interfaces) and a processor 20C (such as one or more processors).

[0155] Optionally, system 2 includes machine learning circuitry configured to operate according to a machine learning model. In one or more example hearing systems, determining the health parameter includes determining the health parameter using a machine learning model based on external sensor data, such as using the electronic device 10, 20, and / or the hearing device 30. In other words, the electronic device 10, 20, and / or the hearing device 30 may include machine learning circuitry configured to operate according to the machine learning model. In other words, system 2 may be configured to determine the health parameter using a machine learning model based on external sensor data, such as using the electronic device 10, 20, and / or the hearing device 30.

[0156] Optionally, processor 10C may include machine learning circuitry 12 configured to operate according to a machine learning model. Optionally, processor 20C may include machine learning circuitry 21 configured to operate according to a machine learning model. Optionally, processor 30C may include machine learning circuitry 31 configured to operate according to a machine learning model. In one or more example electronic devices and / or systems, the models referred to herein may be stored on a non-transitory storage medium (e.g., stored on memory 10A of electronic device 10, memory 20A of server device 20, and / or memory 30A of hearing device 30).

[0157] Optionally, the one or more sensors 10D and / or the one or more sensors 30D comprise a microphone for providing microphone input data. Optionally, the external sensor data comprises microphone input data.

[0158] Optionally, the health parameter is based on microphone input data.

[0159] Optionally, the one or more sensors 30D and / or the one or more sensors 30D include one or more of: a microphone, an optical sensor, a magnetic sensor, an electrical sensor, a motion sensor, and a capacitive sensor.

[0160] Optionally, the interface 10B of the electronic device 10 and / or the interface 30B of the hearing device 30 includes one or more of the following: a Bluetooth interface, a Bluetooth low energy interface, and a magnetic induction interface. For example, the interface 10B of the electronic device 10 and / or the interface 30B of the hearing device 30 may include a Bluetooth antenna and / or a magnetic induction antenna.

[0161] System 2 is configured to obtain external sensor data from one or more sensors, such as one or more sensors 10D and / or one or more sensors 30D. In other words, system 2 is configured to obtain external sensor data at the hearing device 30 and / or at the electronic device serving as the accessory device 10 and / or at the electronic device serving as the server device 20. In other words, the external sensor data can be obtained, received, and / or retrieved from the hearing device 30 by the electronic device serving as the accessory device 10 and / or the electronic device serving as the server device 20. The external sensor data can be transmitted and / or sent by the hearing device 30 to the electronic device serving as the accessory device 10 and / or the electronic device serving as the server device 20. System 2 is configured to determine a health parameter indicating the cognitive status of the user 1 or 1B of the hearing device 30 based on the external sensor data, such as using processor 30C of the hearing device 30 and / or processor 10C of the electronic device serving as the accessory device 10 and / or the electronic device serving as the server device 20. In other words, the system 2 is configured to determine, based on external sensor data, a health parameter indicative of the cognitive state of the user 1 , 1B of the hearing device 30, such as at the processor 30C of the hearing device 30 and / or at the processor 10C of the electronic device serving as the accessory device 10 and / or at the processor 20C of the electronic device serving as the server device 20. The system 2 is configured to determine, such as using the processor 30C of the hearing device 30 and / or the processor 10C of the electronic device serving as the accessory device 10 and / or the electronic device serving as the server device 20, whether the health parameter satisfies a first criterion indicative of cognitive decline. In other words, the system 2 is configured to determine, such as at the processor 30C of the hearing device 30 and / or at the processor 10C of the electronic device serving as the accessory device 10 and / or the electronic device serving as the server device 20, whether the health parameter satisfies a first criterion indicative of cognitive decline. Optionally, the system 2 is configured to output a health representation associated with the cognitive state based on the health parameter satisfying the first criterion, such as using the processor 30C of the hearing device 30 and / or the processor 10C of the electronic device as the accessory device 10 and / or the electronic device as the server device 20, via the interface 30B of the hearing device 30 and / or the interface 10B of the electronic device as the accessory device 10 and / or the interface 20B of the electronic device as the server device 20.

[0162] A hearing device 30 is disclosed. The hearing device 30 can be considered a hearing device with health representation and / or monitoring. The hearing device 30 includes a memory 30A, an interface 30B, a processor 30C, and one or more sensors 30D. The processor 30C is configured to obtain sensor data from the one or more sensors 30D. Optionally, the processor 30C is configured to obtain 24, 34 internal sensor data from one or more sensors 10D of the electronic device 10, for example, via a network 40 (such as a global network, such as the Internet and / or a local network). The processor 30C is configured to determine, based on the sensor data, a health parameter indicative of the cognitive status of a user 1, 1B of the hearing device 30. The processor 30C is configured to determine whether the health parameter meets a first criterion indicating cognitive decline. Optionally, the processor 30C is configured to output a health representation 22, 36 associated with the cognitive status via the interface 30B based on the health parameter meeting the first criterion. Optionally, the user 1, 1B may provide input 38 (such as user input) to the hearing device 30, such as via the interface 30B. The determination of one or more of the sensor data, health parameters and health representation may be based on input 38 from the user 1, IB. The user 1, IB may, for example, provide one or more inputs in response to a cognitive testing protocol.

[0163] An electronic device as an accessory device 10 is disclosed. The electronic device 10 includes a memory 10A, an interface 10B, and a processor 10C. The processor 10C is configured to obtain external sensor data 14, 32 from a hearing device (such as a hearing device 30) via the interface 10B (such as via a network 40, e.g., a global network such as the Internet and / or a local network). The processor 10C is configured to determine a health parameter indicative of a cognitive state of a user 1, 1B of the hearing device 30 based on the external sensor data. The processor 10C is configured to determine whether the health parameter satisfies a first criterion indicating cognitive decline. Optionally, the processor 10C is configured to output 6, 34 a health representation associated with the cognitive state via the interface 10B based on the health parameter satisfying the first criterion.

[0164] An electronic device serving as a server device 20 is disclosed. The electronic device 20 includes a memory 20A, an interface 20B, and a processor 20C. The processor 20C is configured to obtain 16 external sensor data from a hearing device (such as a hearing device 30) via the interface 20B (such as via a network 40, such as a global network, e.g., the Internet and / or a local network). The processor 20C is configured to determine a health parameter indicative of a cognitive state of a user 1, 1B of the hearing device 30 based on the external sensor data. The processor 20C is configured to determine whether the health parameter satisfies a first criterion indicating cognitive decline. Optionally, the processor 20C is configured to output 18 a health representation associated with the cognitive state via the interface 20B (such as via the network 40, to the electronic device 10) based on the health parameter satisfying the first criterion.

[0165] Optionally, the electronic device 10 includes one or more sensors 10D, such as one or more internal sensors. In one or more hearing systems, hearing devices, and / or electronic devices, the system 2 is configured to obtain internal sensor data from the one or more sensors 10D of the electronic device 10. In one or more hearing systems, hearing devices, and / or electronic devices, the health parameters are based on the internal sensor data. The internal sensor data can be considered as sensor data obtained from the electronic device 10. In other words, the internal sensor data can be considered as sensor data generated by the one or more sensors 10D of the electronic device 10. It will be understood that the health parameters are determined based on the sensor data (such as external sensor data) and / or the internal sensor data.

[0166] Optionally, the system 2 (such as the server device 20) is configured to train the machine learning model to provide an updated machine learning model, and transmit the updated machine learning model 14, 18, 24 to the electronic device 10 and / or the hearing device 30. In other words, the server device 20 is configured to train the machine learning model to provide an updated machine learning model, and transmit 18 the updated machine learning model to the electronic device 10 and / or the hearing device 30.

[0167] Optionally, outputting the health representation comprises displaying a user interface 6 representing the health representation via interface 10B of electronic device 10. In other words, outputting the health representation comprises displaying 6 a user interface representing the health representation, eg to user 1, 1A, via interface 10B of accessory device 10 as disclosed herein.

[0168] Optionally, based on the first criterion being met, the system 2 is configured to execute the cognitive testing protocol via the interface 10B of the electronic device 10. In other words, based on the first criterion being met, the system 2 is configured to execute the cognitive testing protocol via the interface 10B of the accessory device 10. In one or more example hearing systems and / or electronic devices, based on the first criterion being met, the system 2 is configured to execute the cognitive testing protocol via the interface 30B of the hearing device 30.

[0169] Optionally, a cognitive testing regimen may be included in the health representation and / or the health representation may be based on a cognitive testing regimen.

[0170] Optionally, the system 2 is configured to determine a health parameter, such as using the electronic device 10 , 20 and / or the hearing device 30 , including determining a first sound biomarker based on the microphone input data.

[0171] Optionally, the system 2 is configured to determine one or more of: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, sound pitch parameters, and speech rhythm parameters, such as using the electronic devices 10 , 20 and / or the hearing device 30 .

[0172] Optionally, the one or more sensors 10D, 30D include one or more physiological sensors for providing physiological data.

[0173] Optionally, the sensor data, such as external sensor data, includes physiological data, such as physiological data associated with the user 1 , 1 B. In one or more example hearing systems, electronic devices, and / or hearing devices, the health parameter is based on the physiological data.

[0174] Optionally, the system 2 is configured to determine a first physiological biomarker based on the physiological data, such as using the electronic device 10 , 20 and / or the hearing device 30 .

[0175] Optionally, the system 2 is configured to determine one or more of the following: blood pressure parameters, blood flow parameters, heart rate parameters, breathing parameters, body temperature parameters, oxygen parameters, and brain activity parameters, such as using the electronic devices 10 , 20 and / or the hearing device 30 .

[0176] Optionally, the system 2 is configured to determine one or more of the following: blood pressure parameters, blood flow parameters, heart rate parameters, respiratory parameters, body temperature parameters, oxygen parameters, and brain activity parameters, such as using the electronic devices 10 , 20 and / or the hearing device 30 .

[0177] Optionally, the one or more sensors 10D, 30D comprise one or more biokinetic sensors for providing biokinetic data.

[0178] Optionally, the sensor data, such as external sensor data, includes biokinetic data, such as biokinetic data associated with the user 1 , 1B.

[0179] Optionally, the system 2 is configured to determine a first biokinetic biomarker based on the biokinetic data, such as using the electronic device 10 , 20 and / or the hearing device 30 .

[0180] Optionally, the system 2 is configured to determine one or more of the following: a movement parameter, a tremor parameter, a jitter parameter, and a twitch parameter, such as using the electronic device 10 , 20 and / or the hearing device 30 .

[0181] The hearing device 30 may be configured to perform Figure 2 Any of the methods disclosed in .

[0182] Processor 30C is optionally configured to execute Figure 2 The hearing device 30 may be implemented as any one of the operations disclosed in the foregoing (such as any one or more of S104A, S104A_1, S104B, S104B_1, S104C, S104C_1, S104D, and S107). The operation of the hearing device 30 may be embodied in the form of an executable logic routine (such as a line of code, a software program, etc.) stored on a non-transitory computer-readable medium (e.g., the memory 30A) and executed by the processor 30C.

[0183] Furthermore, the operation of the hearing device 30 may be viewed as a method that the hearing device 10 is configured to perform. Additionally, while the functions and operations described may be implemented by software, such functions may also be implemented by dedicated hardware or firmware, or some combination of hardware, firmware, and / or software.

[0184] The memory 30A may be one or more of a buffer memory, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, a random access memory (RAM), or other suitable devices. In a typical arrangement, the memory 30A may include a non-volatile memory for long-term data storage and a volatile memory used as a system memory for the processor 30C. The memory 30A may exchange data with the processor 30C via a data bus. A control line and an address bus ( Figure 1 Memory 30A is considered a non-transitory computer-readable medium.

[0185] Memory 30A may be configured to store information in a portion of the memory, such as sensor data, voice data, audio data, image data, health parameters, health indications, biomarkers, recommendations, and / or machine learning models disclosed herein.

[0186] The electronic device 10 may be configured to perform Figure 3 Any of the methods disclosed in .

[0187] Processor 10C is optionally configured to execute Figure 2 Any of the operations disclosed in A (such as any one or more of S204, S206A, S206A_1, S206B, S206B_1, S206C, S206C_1, S206D, S209, S210A, S212). The operations of the electronic device 10 may be embodied in the form of an executable logic routine (such as a line of code, a software program, etc.) that is stored on a non-transitory computer-readable medium (e.g., memory 10A) and executed by the processor 10C.

[0188] Furthermore, the operation of the electronic device 10 can be viewed as a method that the electronic device 10 is configured to perform. In addition, although the functions and operations described can be implemented by software, such functions can also be implemented by dedicated hardware or firmware, or some combination of hardware, firmware and / or software.

[0189] The memory 10A may be one or more of a buffer memory, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, a random access memory (RAM), or other suitable devices. In a typical arrangement, the memory 10A may include a non-volatile memory for long-term data storage and a volatile memory used as a system memory for the processor 10C. The memory 10A may exchange data with the processor 10C via a data bus. A control line and an address bus ( Figure 1 Memory 10A is considered a non-transitory computer-readable medium.

[0190] Memory 10A may be configured to store information in a portion of the memory, such as sensor data, voice data, audio data, image data, health parameters, health indications, biomarkers, recommendations, and / or machine learning models disclosed herein.

[0191] The server device 20, such as the processor 20C, can be configured to perform any of the operations described herein that are performed by the electronic device 10 (such as the accessory device 10, such as the processor 10C). In other words, the description related to the processor 10C can be applied to the description of the processor 20C. For example, when the electronic device acts as a server device, the electronic device 10 and the server device 20 can be considered as one device and / or two independent devices.

[0192] System 2 can be configured to perform Figures 4A-4B Any of the methods disclosed in .

[0193] System 2 is optionally configured to perform Figures 4A-4B Any of the operations disclosed in (such as any one or more of S304, S306A, S306A_1, S306B, S306B_1, S306C, S306C_1, S306D, S309, S310A, S312, S314, S316, S318). The operations of system 2 may be embodied in the form of an executable logic routine (such as a line of code, a software program, etc.) stored on a non-transitory computer-readable medium (such as memory 10A, 30A) and executed by processor 10C, 30C.

[0194] Furthermore, the operations of system 2 can be viewed as methods that system 2 is configured to perform. Additionally, while the functions and operations described may be implemented by software, such functions may also be implemented by dedicated hardware or firmware, or some combination of hardware, firmware, and / or software.

[0195] Figure 2 A flow chart illustrating an example method, such as the method 100, is shown. A method 100 of operating a hearing device is disclosed. The method 100 may be performed by a hearing device as disclosed herein, such as the hearing device 30.

[0196] The method 100 comprises obtaining sensor data from one or more sensors of a hearing device S102 .

[0197] The method 100 includes determining a health parameter indicating a cognitive state of a user of a hearing device based on sensor data S104. The method 100 includes determining S106 whether the health parameter satisfies a first criterion indicating cognitive decline. The method 100 includes outputting a health representation associated with the cognitive state based on the health parameter satisfying the first criterion S108.

[0198] Optionally, in dependence on the health parameter not meeting the first criterion, the method 100 comprises limiting S107 outputting the health representation associated with the cognitive state S108.

[0199] In one or more example methods, the one or more sensors include a microphone for providing microphone input data. In one or more example methods, the health parameter is based on the microphone input data.

[0200] In one or more example methods, determining S104 a health parameter includes determining S104A a first sound biomarker based on the microphone input data. In one or more example methods, the first criterion is based on the first sound biomarker.

[0201] In one or more example methods, determining a first sound biomarker S104A includes determining S104A_1 one or more of the following: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a voice rhythm parameter. In one or more example methods, the first sound biomarker is based on one or more of the following: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a voice rhythm parameter.

[0202] In one or more example methods, the one or more sensors include one or more physiological sensors for providing physiological data. In one or more example methods, the health parameter is based on the physiological data.

[0203] In one or more example methods, determining S104 a health parameter includes determining S104B a first physiological biomarker based on the physiological data. In one or more example methods, the first criterion is based on the first physiological biomarker.

[0204] In one or more example methods, determining S104B a first physiological biomarker includes determining S104B_1 one or more of the following: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter. In one or more example methods, the first physiological biomarker is based on one or more of the following parameters: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter.

[0205] In one or more example methods, the one or more sensors include one or more biokinetic sensors for providing biokinetic data. In one or more example methods, the health parameter is based on the biokinetic data.

[0206] In one or more example methods, determining S104 a health parameter includes determining S104C a first biokinetic biomarker based on the biokinetic data. In one or more example methods, the first criterion is based on the first biokinetic biomarker.

[0207] In one or more example methods, determining S104C a first biokinetic biomarker includes determining S104C_1 one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter. In one or more example methods, the first biokinetic biomarker is based on one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter.

[0208] In one or more example methods, the one or more sensors include one or more of: an optical sensor, a magnetic sensor, an electrical sensor, a motion sensor, and a capacitive sensor.

[0209] In one or more example methods, the processor includes a machine learning circuit configured to operate according to the machine learning model. In one or more example methods, determining S104 the health parameter includes determining S104D the health parameter using the machine learning model based on the sensor data.

[0210] Figure 3 A flow chart of an example method, such as method 200, is shown. A method 200 of operating an electronic device is disclosed. The method 200 may be performed by an electronic device, such as electronic device 10, disclosed herein.

[0211] A method 200 for operating an electronic device is disclosed. The method 200 includes obtaining (202) external sensor data from a hearing device. The method 200 includes determining (206) a health parameter indicating a cognitive state of a user of the hearing device based on the external sensor data. The method 200 includes determining (208) whether the health parameter satisfies a first criterion indicating cognitive decline. The method 200 includes outputting (210) a health representation associated with the cognitive state based on the health parameter satisfying the first criterion.

[0212] Optionally, based on the health parameter not meeting the first criterion, the method 200 includes refraining S209 from outputting S210 a health representation associated with the cognitive state.

[0213] In one or more example methods, outputting the health representation S210 includes displaying S210A a user interface representing the health representation.

[0214] In one or more example methods, upon the first criterion being met, the method includes executing S212 a cognitive testing protocol.

[0215] In one or more example methods, the external sensor data includes microphone input data. In one or more example methods, the health parameter is based on the microphone input data.

[0216] In one or more example methods, determining S206 the health parameter includes determining S206A a first sound biomarker based on the microphone input data. In one or more example methods, the first criterion is based on the first sound biomarker.

[0217] In one or more example methods, determining S206A a first sound biomarker includes determining S206A_1 one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a voice rhythm parameter. In one or more example methods, the first sound biomarker is based on one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a voice rhythm parameter.

[0218] In one or more example methods, the external sensor data includes physiological data. In one or more example methods, the health parameter is based on the physiological data.

[0219] In one or more example methods, determining a health parameter S206 includes determining 206B a first physiological biomarker based on the physiological data. In one or more example methods, the first criterion is based on the first physiological biomarker.

[0220] In one or more example methods, determining S206B a first physiological biomarker includes determining S206B_1 one or more of the following: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter. In one or more example methods, the first physiological biomarker is based on one or more of the following parameters: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter.

[0221] In one or more example methods, the external sensor data includes biokinetic data. In one or more example methods, the health parameter is based on the biokinetic data.

[0222] In one or more example methods, determining S206 a health parameter includes determining S206C a first biokinetic biomarker based on the biokinetic data. In one or more example methods, the first criterion is based on the first biokinetic biomarker.

[0223] In one or more example methods, determining S206C a first biokinetic biomarker includes determining S206C_1 one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter. In one or more example methods, the first biokinetic biomarker is based on one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter.

[0224] In one or more example methods, the electronic device includes one or more sensors, and the method includes obtaining S204 internal sensor data from the one or more sensors. In one or more example methods, the health parameter is based on the internal sensor data.

[0225] In one or more example methods, the processor includes a machine learning circuit configured to operate according to the machine learning model. In one or more example methods, determining S206 the health parameter includes determining S206D the health parameter based on the sensor data using the machine learning model.

[0226] In one or more example methods, the interface includes one or more of: a Bluetooth interface, a Bluetooth low energy interface, and a magnetic induction interface.

[0227] Figures 4A-4BA flow chart of an example method, such as method 300 , is shown.

[0228] A method 300 of operating a hearing system is disclosed. The method 300 may be performed by a system such as the system 2 disclosed herein.

[0229] Method 300 includes obtaining (302) external sensor data from a hearing device. Method 300 includes determining (306) a health parameter indicative of a cognitive state of a user of the hearing device based on the sensor data. Method 300 includes determining (308) whether the health parameter satisfies a first criterion indicating cognitive decline. Method 300 includes outputting (310) a health representation associated with the cognitive state based on the health parameter satisfying the first criterion.

[0230] Optionally, based on the health parameter not meeting the first criterion, the method 300 includes refraining S309 from outputting S310 a health representation associated with the cognitive state.

[0231] In one or more example methods, outputting the health representation S310 includes displaying S310A a user interface representing the health representation.

[0232] In one or more example methods, based on the first criterion being met, the method includes executing S312 a cognitive testing protocol.

[0233] In one or more example methods, the external sensor data includes microphone input data. In one or more example methods, the health parameter is based on the microphone input data.

[0234] In one or more example methods, determining S306 the health parameter includes determining S306A a first sound biomarker based on the microphone input data. In one or more example methods, the first criterion is based on the first sound biomarker.

[0235] In one or more example methods, determining S306A a first sound biomarker includes determining S306A_1 one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a voice rhythm parameter. In one or more example methods, the first sound biomarker is based on one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a voice rhythm parameter.

[0236] In one or more example methods, the external sensor data includes physiological data. In one or more example methods, the health parameter is based on the physiological data.

[0237] In one or more example methods, determining a health parameter S306 includes determining a first physiological biomarker 306B based on the physiological data. In one or more example methods, the first criterion is based on the first physiological biomarker.

[0238] In one or more example methods, determining S306B a first physiological biomarker includes determining S306B_1 one or more of the following: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter. In one or more example methods, the first physiological biomarker is based on one or more of the following: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter.

[0239] In one or more example methods, the external sensor data includes biokinetic data. In one or more example methods, the health parameter is based on the biokinetic data.

[0240] In one or more example methods, determining S306 a health parameter includes determining S306C a first biokinetic biomarker based on the biokinetic data. In one or more example methods, the first criterion is based on the first biokinetic biomarker.

[0241] In one or more example methods, determining S306C a first biokinetic biomarker includes determining S306C_1 one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter. In one or more example methods, the first biokinetic biomarker is based on one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter.

[0242] In one or more example methods, the electronic device includes one or more sensors, and the method includes obtaining S304 internal sensor data from the one or more sensors. In one or more example methods, the health parameter is based on the internal sensor data.

[0243] In one or more example methods, the electronic device includes a machine learning circuit configured to operate according to the machine learning model. In one or more example methods, determining S306 the health parameter includes determining S306D the health parameter based on the external sensor data using the machine learning model.

[0244] In one or more example methods, the interface of the electronic device and / or the interface of the hearing device includes one or more of: a Bluetooth interface, a Bluetooth Low Energy interface, and a magnetic induction interface.

[0245] In one or more example methods, the electronic device is a server device.

[0246] In one or more example methods, the method includes training S314 a machine learning model, providing S316 an updated machine learning model, and transmitting S318 the updated machine learning model to an electronic device.

[0247] In one or more example methods, the electronic device is an accessory device.

[0248] Examples of electronic devices, systems, and methods according to the present disclosure are listed in the following clauses:

[0249] A1. A hearing device comprising

[0250] Memory;

[0251] interface;

[0252] processor; and

[0253] one or more sensors;

[0254] Wherein, the processor is configured to:

[0255] obtaining sensor data from the one or more sensors;

[0256] determining, based on the sensor data, a health parameter indicative of a cognitive state of a user of the hearing device;

[0257] determining whether the health parameter meets a first criterion indicative of cognitive decline; and

[0258] Based on the health parameter satisfying the first criterion, a health representation associated with the cognitive state is output via the interface.

[0259] A2. The hearing device of clause A1, wherein the one or more sensors comprises a microphone for providing microphone input data, and wherein the health parameter is based on the microphone input data.

[0260] A3. The hearing device of clause A2, wherein determining the health parameter comprises determining a first acoustic biomarker based on the microphone input data, and wherein the first criterion is based on the first acoustic biomarker.

[0261] A4. A hearing device according to clause A3, wherein determining the first sound biomarker includes determining one or more of the following: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, sound pitch parameters and speech rhythm parameters; and wherein the first sound biomarker is based on one or more of the following: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, sound pitch parameters and speech rhythm parameters.

[0262] A5. The hearing device of any of the preceding clauses, wherein the one or more sensors comprise one or more physiological sensors for providing physiological data, and wherein the health parameter is based on the physiological data.

[0263] A6. The hearing device of clause A5, wherein determining the health parameter comprises determining a first physiological biomarker based on the physiological data, and wherein the first criterion is based on the first physiological biomarker.

[0264] A7. A hearing device according to clause A6, wherein determining the first physiological biomarker includes determining one or more of the following: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiratory parameter, a body temperature parameter, an oxygen parameter and a brain activity parameter; and wherein the first physiological biomarker is based on one or more of the following: the blood pressure parameter, the blood flow parameter, the heart rate parameter, the respiratory parameter, the body temperature parameter, the oxygen parameter and the brain activity parameter.

[0265] A8. The hearing device of any of the preceding clauses, wherein the one or more sensors comprise one or more biokinetic sensors for providing biokinetic data, and wherein the health parameter is based on the biokinetic data.

[0266] A9. The hearing device of clause A8, wherein determining the health parameter comprises determining a first biodynamic biomarker based on the biodynamic data, and wherein the first criterion is based on the first biodynamic biomarker.

[0267] A10. A hearing device according to clause A9, wherein determining the first biodynamic biomarker includes determining one or more of the following: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter; and wherein the first biodynamic biomarker is based on one or more of the following: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter.

[0268] A11. The hearing device according to any of the preceding clauses, wherein the one or more sensors comprise one or more of the following: an optical sensor, a magnetic sensor, an electrical sensor, a motion sensor, and a capacitive sensor.

[0269] A12. A hearing device according to any of the preceding clauses, wherein the processor includes a machine learning circuit configured to operate according to a machine learning model, wherein determining the health parameter includes determining the health parameter using the machine learning model based on the sensor data.

[0270] A13. A method of operating a hearing device, the method comprising:

[0271] obtaining sensor data from one or more sensors of the hearing device;

[0272] determining, based on the sensor data, a health parameter indicative of a cognitive state of a user of the hearing device;

[0273] determining whether the health parameter meets a first criterion indicative of cognitive decline; and

[0274] Based on the health parameter satisfying the first criterion, a health representation associated with the cognitive state is output.

[0275] A14. A method of operating a hearing device according to clause A13, wherein the one or more sensors comprises a microphone for providing microphone input data, and wherein the health parameter is based on the microphone input data.

[0276] A15. The method of operating a hearing device according to clause A14, wherein determining (S104) the health parameter comprises:

[0277] A first sound biomarker is determined (S104A) based on the microphone input data, and wherein the first criterion is based on the first sound biomarker.

[0278] A16. The method of operating a hearing device according to clause A15, wherein determining (S104A) the first acoustic biomarker comprises:

[0279] Determining (S104A_1) one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter; and wherein the first sound biomarker is based on one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter.

[0280] A17. Method of operating a hearing device according to any of clauses A13-A16, wherein the one or more sensors comprise one or more physiological sensors for providing physiological data, and wherein the health parameter is based on the physiological data.

[0281] A18. The method of operating a hearing device according to clause A17, wherein determining (S104) the health parameter comprises:

[0282] A first physiological biomarker is determined (S104B) based on the physiological data, and wherein the first criterion is based on the first physiological biomarker.

[0283] A19. The method of operating a hearing device according to clause A18, wherein determining (S104B) the first physiological biomarker comprises:

[0284] determining (S104B_1) one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter; and wherein the first physiological biomarker is based on one or more of: the blood pressure parameter, the blood flow parameter, the heart rate parameter, the respiration parameter, the body temperature parameter, the oxygen parameter, and the brain activity parameter.

[0285] A20. A method of operating a hearing device according to any of clauses A13-A19, wherein the one or more sensors comprise one or more biokinetic sensors for providing biokinetic data, and wherein the health parameter is based on the biokinetic data.

[0286] A21. A method of operating a hearing device according to clause A20, wherein determining (S104) a health parameter comprises:

[0287] A first biokinetic biomarker is determined (S104C) based on the biokinetic data, and wherein the first criterion is based on the first biokinetic biomarker.

[0288] A22. A method of operating a hearing device according to clause A21, wherein determining (S104C) a first biodynamic biomarker includes determining (S104C_1) one or more of the following: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter; and wherein the first biodynamic biomarker is based on one or more of the following: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter.

[0289] A23. A method of operating a hearing device according to any of clauses A13-A22, wherein the one or more sensors comprise one or more of: an optical sensor, a magnetic sensor, an electrical sensor, a motion sensor, and a capacitive sensor.

[0290] A24. A method of operating a hearing device according to any of clauses A13 to A23, wherein the processor comprises machine learning circuitry configured to operate according to a machine learning model, wherein determining (S104) the health parameter comprises:

[0291] Based on the sensor data, the health parameter is determined (S104D) using the machine learning model.

[0292] B1. An electronic device comprising

[0293] Memory;

[0294] Interfaces; and

[0295] processor;

[0296] Wherein, the processor is configured to:

[0297] obtaining external sensor data from the hearing device via the interface;

[0298] determining a health parameter indicative of a cognitive state of a user of the hearing device based on the external sensor data;

[0299] determining whether the health parameter meets a first criterion indicative of cognitive decline;

[0300] Based on the health parameter satisfying the first criterion, a health representation associated with the cognitive state is output via the interface.

[0301] B2. The electronic device of clause B1, wherein outputting the health representation comprises displaying a user interface representing the health representation via the interface.

[0302] B3. The electronic device of any of clauses B1-B2, wherein, based on the first criterion being met, the electronic device is configured to perform a cognitive testing regimen.

[0303] B4. An electronic device according to any of clauses B1-B3, wherein the external sensor data includes microphone input data, and wherein the health parameter is based on the microphone input data.

[0304] B5. An electronic device as described in clause B4, wherein determining the health parameter includes determining a first sound biomarker based on the microphone input data, and wherein the first criterion is based on the first sound biomarker.

[0305] B6. An electronic device according to clause B5, wherein determining the first sound biomarker includes determining one or more of the following: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, sound pitch parameters and speech rhythm parameters; and wherein the first sound biomarker is based on one or more of the following: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, sound pitch parameters and speech rhythm parameters.

[0306] B7. An electronic device according to any of clauses B1-B6, wherein the external sensor data includes physiological data, and wherein the health parameter is based on the physiological data.

[0307] B8. An electronic device as described in clause B7, wherein determining the health parameter includes determining a first physiological biomarker based on the physiological data, and wherein the first criterion is based on the first physiological biomarker.

[0308] B9. An electronic device according to clause B8, wherein determining the first physiological biomarker includes determining one or more of the following: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiratory parameter, a body temperature parameter, an oxygen parameter and a brain activity parameter; and wherein the first physiological biomarker is based on one or more of the following: the blood pressure parameter, the blood flow parameter, the heart rate parameter, the respiratory parameter, the body temperature parameter, the oxygen parameter and the brain activity parameter.

[0309] B10. An electronic device according to any of clauses B1-B9, wherein the external sensor data includes biokinetic data, and wherein the health parameter is based on the biokinetic data.

[0310] B11. The electronic device of clause B10, wherein determining the health parameter comprises determining a first biokinetic biomarker based on the biokinetic data, and wherein the first criterion is based on the first biokinetic biomarker.

[0311] B12. An electronic device according to clause B11, wherein determining the first biodynamic biomarker includes determining one or more of the following: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter; and wherein the first biodynamic biomarker is based on one or more of the following: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter.

[0312] B13. An electronic device according to any one of clauses B1-B12, wherein the electronic device includes one or more sensors, and wherein the processor is configured to obtain internal sensor data from the one or more sensors, and wherein the health parameter is based on the internal sensor data.

[0313] B14. An electronic device according to any one of clauses B1-B13, wherein the processor includes a machine learning circuit configured to operate according to a machine learning model, wherein determining the health parameter includes using the machine learning model to determine the health parameter based on the external sensor data.

[0314] B15. The electronic device of any one of clauses B1-B14, wherein the interface comprises one or more of: a Bluetooth interface, a Bluetooth Low Energy interface, and a magnetic induction interface.

[0315] B16. A method of operating an electronic device, the method comprising:

[0316] obtaining external sensor data from the hearing device;

[0317] determining a health parameter indicative of a cognitive state of a user of the hearing device based on the external sensor data;

[0318] determining whether the health parameter meets a first criterion indicative of cognitive decline; and

[0319] Based on the health parameter satisfying the first criterion, a health representation associated with the cognitive state is output.

[0320] B17. The method of operating an electronic device according to clause B16, wherein outputting (S110) the health indication comprises:

[0321] A user interface showing the health representation is displayed (S110A).

[0322] B18. A method of operating an electronic device according to any of clauses B16-B17, wherein, depending on the first criterion being satisfied, the method comprises:

[0323] A cognitive test protocol is executed (S112).

[0324] B19. A method of operating an electronic device according to any of clauses B16-B18, wherein the external sensor data includes microphone input data, and wherein the health parameter is based on the microphone input data.

[0325] B20. The method of operating an electronic device according to clause B19, wherein determining (S106) the health parameter comprises:

[0326] A first sound biomarker is determined (S106A) based on the microphone input data, and wherein the first criterion is based on the first sound biomarker.

[0327] B21. The method of operating an electronic device according to clause B20, wherein determining (S106A) the first sound biomarker comprises:

[0328] Determining (S106A_1) one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter; and wherein the first sound biomarker is based on one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter.

[0329] B22. A method of operating an electronic device according to any of clauses B16-B21, wherein the external sensor data includes physiological data, and wherein the health parameter is based on the physiological data.

[0330] B23. The method of operating an electronic device according to clause B22, wherein determining (S106) the health parameter comprises:

[0331] A first physiological biomarker is determined (106B) based on the physiological data, and wherein the first criterion is based on the first physiological biomarker.

[0332] B24. The method of operating an electronic device according to clause B23, wherein determining (S106B) the first physiological biomarker comprises:

[0333] determining (S106B_1) one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter; and wherein the first physiological biomarker is based on one or more of: the blood pressure parameter, the blood flow parameter, the heart rate parameter, the respiration parameter, the body temperature parameter, the oxygen parameter, and the brain activity parameter.

[0334] B25. A method of operating an electronic device according to any of clauses B16-B24, wherein the external sensor data includes biokinetic data, and wherein the health parameter is based on the biokinetic data.

[0335] B26. The method of operating an electronic device according to clause B25, wherein determining (S106) the health parameter comprises:

[0336] A first biokinetic biomarker is determined (S106C) based on the biokinetic data, and wherein the first criterion is based on the first biokinetic biomarker.

[0337] B27. The method of operating an electronic device according to clause B26, wherein determining (S106C) the first biokinetic biomarker comprises:

[0338] Determining (S106C_1) one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter; and wherein the first biodynamic biomarker is based on one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter.

[0339] B28. A method of operating an electronic device according to any of clauses B16-B27, wherein the electronic device comprises one or more sensors, and wherein the method comprises:

[0340] Internal sensor data is obtained (S104) from the one or more sensors, and wherein the health parameter is based on the internal sensor data.

[0341] B29. The method of operating an electronic device according to any one of clauses B16 to B28, wherein the processor comprises machine learning circuitry configured to operate according to a machine learning model, wherein determining (S106) the health parameter comprises:

[0342] Based on the sensor data, the health parameter is determined (S106D) using the machine learning model.

[0343] B30. A method of operating an electronic device according to any one of clauses B16-B29, wherein the interface comprises one or more of: a Bluetooth interface, a Bluetooth low energy interface, and a magnetic induction interface.

[0344] C1. A hearing system, comprising:

[0345] An electronic device comprising a memory, an interface, and a processor; and

[0346] a hearing device comprising a memory, an interface, a processor, and one or more sensors;

[0347] Wherein, the system is configured to:

[0348] obtaining external sensor data from the one or more sensors;

[0349] determining a health parameter indicative of a cognitive state of a user of the hearing device based on the external sensor data;

[0350] determining whether the health parameter meets a first criterion indicative of cognitive decline; and

[0351] A health representation associated with the cognitive state is output based on the health parameter meeting the first criterion.

[0352] C2. The hearing system of clause C1, wherein outputting the health representation comprises displaying a user interface representing the health representation via the interface of the electronic device.

[0353] C3. The hearing system of any of clauses C1-C2, wherein, depending on the first criterion being met, the system is configured to perform a cognitive testing protocol via the interface of the electronic device.

[0354] C4. A hearing system according to any of clauses C1-C3, wherein the external sensor data comprises microphone input data, and wherein the health parameter is based on the microphone input data.

[0355] C5. A hearing system according to clause C4, wherein determining the health parameter comprises determining a first sound biomarker based on the microphone input data, and wherein the first criterion is based on the first sound biomarker.

[0356] C6. A hearing system according to clause C5, wherein determining the first sound biomarker includes determining one or more of the following: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, sound pitch parameters and speech rhythm parameters; and wherein the first sound biomarker is based on one or more of the following: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, sound pitch parameters and speech rhythm parameters.

[0357] C7. A hearing system according to any of clauses C1-C6, wherein the external sensor data comprises physiological data, and wherein the health parameter is based on the physiological data.

[0358] C8. A hearing system according to clause C7, wherein determining the health parameter comprises determining a first physiological biomarker based on the physiological data, and wherein the first criterion is based on the first physiological biomarker.

[0359] C9. A hearing system according to clause C8, wherein determining the first physiological biomarker includes determining one or more of the following: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiratory parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter; and wherein the first physiological biomarker is based on one or more of the following: the blood pressure parameter, the blood flow parameter, the heart rate parameter, the respiratory parameter, the body temperature parameter, the oxygen parameter, and the brain activity parameter.

[0360] C10. A hearing system according to any of clauses C1 to C9, wherein the external sensor data comprises biokinetic data, and wherein the health parameter is based on the biokinetic data.

[0361] C11. A hearing system according to clause C10, wherein determining the health parameter comprises determining a first biodynamic biomarker based on the biodynamic data, and wherein the first criterion is based on the first biodynamic biomarker.

[0362] C12. A hearing system according to clause C11, wherein determining the first biodynamic biomarker includes determining one or more of the following: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter; and wherein the first biodynamic biomarker is based on one or more of the following: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter.

[0363] C13. A hearing system according to any of clauses C1-C12, wherein the electronic device includes one or more sensors, and wherein the system is configured to obtain internal sensor data from the one or more sensors of the electronic device, and wherein the health parameter is based on the internal sensor data.

[0364] C14. A hearing system according to any of clauses C1-C13, wherein the hearing system includes a machine learning circuit configured to operate according to a machine learning model, wherein determining the health parameter includes using the machine learning model to determine the health parameter based on the external sensor data.

[0365] C15. The hearing system according to any of clauses C1 to C14, wherein the interface of the electronic device and / or the interface of the hearing device comprises one or more of: a Bluetooth interface, a Bluetooth Low Energy interface, and a magnetic induction interface.

[0366] C16. A hearing system according to any of clauses C1-C15, wherein the electronic device is a server device.

[0367] C17. A hearing system according to clause C16, wherein the server device is configured to train the machine learning model to provide an updated machine learning model, and transmit the updated machine learning model to the electronic device and / or the hearing device.

[0368] C18. A hearing system according to any of clauses C1-C17, wherein the electronic device is an accessory device.

[0369] C19. A method of operating an electronic device, the method comprising:

[0370] obtaining external sensor data from the hearing device;

[0371] determining a health parameter indicative of a cognitive state of a user of the hearing device based on the external sensor data;

[0372] determining whether the health parameter meets a first criterion indicative of cognitive decline; and

[0373] Based on the health parameter satisfying the first criterion, a health representation associated with the cognitive state is output.

[0374] C20. The method of operating a hearing system according to clause C19, wherein outputting (S110) the health indication comprises:

[0375] A user interface showing the health representation is displayed (S110A).

[0376] C21. A method of operating a hearing system according to any of clauses C19-C20, wherein, depending on the first criterion being met, the method comprises:

[0377] A cognitive test protocol is executed (S112).

[0378] C22. A method of operating a hearing system according to any of clauses C19-C22, wherein the external sensor data comprises microphone input data, and wherein the health parameter is based on the microphone input data.

[0379] C23. A method of operating a hearing system according to clause C22, wherein determining (S106) a health parameter comprises:

[0380] A first sound biomarker is determined (S106A) based on the microphone input data, and wherein the first criterion is based on the first sound biomarker.

[0381] C24. The method of operating a hearing system according to clause C23, wherein determining (S106A) the first acoustic biomarker comprises:

[0382] Determining (S106A_1) one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter; and wherein the first sound biomarker is based on one or more of: a language parameter, an acoustic parameter, a speech fluency parameter, a mumbling parameter, a voice pitch parameter, and a speech rhythm parameter.

[0383] C25. A method of operating a hearing system according to any of clauses C19-C24, wherein the external sensor data comprises physiological data, and wherein the health parameter is based on the physiological data.

[0384] C26. The method of operating a hearing system according to clause C25, wherein determining (S106) the health parameter comprises:

[0385] A first physiological biomarker is determined (106B) based on the physiological data, and wherein the first criterion is based on the first physiological biomarker.

[0386] C27. A method of operating a hearing system according to clause C26, wherein determining (S106B) a first physiological biomarker includes: determining (S106B_1) one or more of the following parameters: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiratory parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter; and wherein the first physiological biomarker is based on one or more of the following: the blood pressure parameter, the blood flow parameter, the heart rate parameter, the respiratory parameter, the body temperature parameter, the oxygen parameter, and the brain activity parameter.

[0387] C28. A method of operating a hearing system according to any of clauses C19-C27, wherein the external sensor data comprises biokinetic data, and wherein the health parameter is based on the biokinetic data.

[0388] C29. The method of operating a hearing system according to clause C28, wherein determining (S106) a health parameter comprises:

[0389] A first biokinetic biomarker is determined (S106C) based on the biokinetic data, and wherein the first criterion is based on the first biokinetic biomarker.

[0390] C30. The method of operating a hearing system according to clause C29, wherein determining (S106C) the first biokinetic biomarker comprises:

[0391] Determining (S106C_1) one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter; and wherein the first biodynamic biomarker is based on one or more of: a motion parameter, a tremor parameter, a shake parameter, and a twitch parameter.

[0392] C31. A method of operating a hearing system according to any of clauses C19-C30, wherein the electronic device comprises one or more sensors, and wherein the method comprises:

[0393] Internal sensor data is obtained (S104) from the one or more sensors, and wherein the health parameter is based on the internal sensor data.

[0394] C32. A method of operating a hearing system according to any of clauses C19 to C31, wherein the electronic device comprises machine learning circuitry configured to operate according to a machine learning model, wherein determining (S106) the health parameter comprises:

[0395] Based on the external sensor data, the health parameter is determined (S106D) using the machine learning model.

[0396] C33. A method of operating a hearing system according to any of clauses C19-C32, wherein the interface of the electronic device and / or the interface of the hearing device comprises one or more of: a Bluetooth interface, a Bluetooth low energy interface and a magnetic induction interface.

[0397] C34. A method of operating a hearing system according to any of clauses C19-C33, wherein the electronic device is a server device.

[0398] C35. A method of operating a hearing system according to clause C34, wherein the method comprises:

[0399] Training (S114) the machine learning model;

[0400] providing (S116) an updated machine learning model; and

[0401] The updated machine learning model is transmitted (S118) to the electronic device.

[0402] C36. A method of operating a hearing system according to any of clauses C19-C35, wherein the electronic device is an accessory device.

[0403] The use of the terms "first," "second," "third," and "fourth," "primary," "secondary," and "tertiary," etc., does not imply any particular order, but rather is used to identify individual elements. Furthermore, the use of the terms "first," "second," "third," and "fourth," "primary," "secondary," and "tertiary," etc., does not imply any order or importance, but rather the terms "first," "second," "third," and "fourth," "primary," "secondary," and "tertiary," etc., are used to distinguish one element from another. It should be noted that the words "first," "second," "third," and "fourth," "primary," "secondary," and "tertiary," etc., are used here and elsewhere for labeling purposes only and are not intended to represent any particular spatial or temporal order. Furthermore, labeling an element first does not imply the existence of a second element, and vice versa.

[0404] It will be appreciated that the accompanying drawings include some circuits or operations shown with solid lines, and some circuits, components, features or operations shown with dotted lines. The circuits or operations included in the solid lines are the circuits, components, features or operations included in the most extensive examples. The circuits, components, features or operations included in the dotted lines are examples, which may be included in the circuits, components, features or operations in the solid line examples, or are part of them, or are other circuits, components, features or operations that may be included in addition to the circuits, components, features or operations in the solid line examples. It will be appreciated that these operations do not need to be performed in the order presented. In addition, it will be appreciated that not all operations need to be performed. The example operations may be performed in any order and combination. It will be appreciated that it is not necessary to perform these operations in order. The circuits, components, features or operations included in the dotted lines may be considered optional.

[0405] Other operations not described herein may also be combined in the example operations. For example, one or more additional operations may be performed before, after, simultaneously with, or between any of the operations.

[0406] Certain features discussed above as separate implementations may also be implemented in combination as a single implementation. Conversely, features described as a single implementation may also be implemented in multiple implementations separately or in any suitable subcombination. Furthermore, although features may be described above as functioning in a particular combination, in some cases one or more features of a claimed combination may be deleted from that combination, and the combination may be claimed as any subcombination or variation of any subcombination.

[0407] It should be noted that the word "comprising" does not necessarily exclude the presence of other elements or steps than those listed.

[0408] It should be noted that the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.

[0409] It should also be noted that any reference signs do not limit the scope of the claims, that examples may be implemented at least partially by hardware and software, and that several "parts," "units," or "means" may be represented by the same item of hardware.

[0410] As used herein, degree language (such as the terms "approximately," "about," "typically," and "substantially") indicates a value, amount, or characteristic that is close to the stated value, amount, or characteristic, but still performs the desired function or achieves the desired result. For example, the terms "approximately," "about," "typically," and "substantially" may refer to an amount that is less than or equal to 10% of the stated amount, less than or equal to 5% of the stated amount, less than or equal to 1% of the stated amount, less than or equal to 0.1% of the stated amount, and less than or equal to 0.01% of the stated amount. If the stated amount is 0 (such as, none, none), the above range may be a specific range rather than a specific percentage of the value. For example, less than or equal to 10 wt. / vol.%, less than or equal to 5 wt. / vol.%, less than or equal to 1 wt. / vol.%, less than or equal to 0.1 wt. / vol.%, and less than or equal to 0.01 wt. / vol.%.

[0411] While features have been shown and described, it should be understood that these features are not intended to limit the claimed disclosure, and that those skilled in the art will appreciate that various changes and modifications may be made without departing from the scope of the claimed disclosure. Accordingly, the specification and drawings should be regarded as illustrative rather than restrictive. The claimed disclosure is intended to encompass all alternatives, modifications, and equivalents.

[0412] List of reference numerals

[0413] 1. 1A and 1B users

[0414] 2 systems

[0415] 4 Input, user input

[0416] 6Health display and output

[0417] 10Electronic devices and accessories

[0418] 10A One or more interfaces

[0419] 10B memory

[0420] 10C processor

[0421] 10D One or more sensors

[0422] 12 Machine Learning Circuits

[0423] 13 Control and transmission

[0424] 14 Get

[0425] 16 Get

[0426] 18 Control and transmission

[0427] 20 Electronic devices, server devices

[0428] 20A memory

[0429] 20B One or more interfaces

[0430] 20C processor

[0431] 21 Machine Learning Circuit

[0432] 22 Control and transmission

[0433] 24 Get

[0434] 30 Hearing devices

[0435] 30A one or more interfaces

[0436] 30B memory

[0437] 30C processor

[0438] 30D One or more sensors

[0439] 31 Machine Learning Circuit

[0440] 32 Control, transmission

[0441] 34 Get

[0442] 36 Health display and output

[0443] 38 Input, user input

[0444] 40 Network

Claims

1. A hearing device, comprising: Memory; interface; processor; as well as one or more sensors; Wherein, the processor is configured to: obtaining sensor data from the one or more sensors; determining, based on the sensor data, a health parameter indicative of a cognitive state of a user of the hearing device; determining whether the health parameter meets a first criterion indicative of cognitive decline; and Based on the health parameter satisfying the first criterion, a health representation associated with the cognitive state is output via the interface.

2. The hearing device according to claim 1, wherein The one or more sensors include: A microphone for providing microphone input data, and wherein the health parameter is based on the microphone input data.

3. The hearing device according to claim 2, wherein: Determining health parameters includes: A first acoustic biomarker is determined based on the microphone input data, and wherein the first criterion is based on the first acoustic biomarker.

4. The hearing device according to claim 3, wherein: Determining the first acoustic biomarker includes determining one or more of: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, voice pitch parameters, and speech rhythm parameters; and wherein the first sound biomarker is based on one or more of the following: language parameters, acoustic parameters, speech fluency parameters, mumbling parameters, voice pitch parameters, and speech rhythm parameters.

5. A hearing device according to any one of the preceding claims, wherein: The one or more sensors include one or more physiological sensors for providing physiological data, and wherein the health parameter is based on the physiological data.

6. The hearing device according to claim 5, wherein: Determining a health parameter includes determining a first physiological biomarker based on the physiological data, and wherein the first criterion is based on the first physiological biomarker.

7. The hearing device according to claim 6, wherein: Determining a first physiological biomarker comprises determining one or more of: a blood pressure parameter, a blood flow parameter, a heart rate parameter, a respiration parameter, a body temperature parameter, an oxygen parameter, and a brain activity parameter; and wherein the first physiological biomarker is based on one or more of: the blood pressure parameter, the blood flow parameter, the heart rate parameter, the respiration parameter, the body temperature parameter, the oxygen parameter, and the brain activity parameter.

8. A hearing device according to any one of the preceding claims, wherein: The one or more sensors include one or more biokinetic sensors for providing biokinetic data, and wherein the health parameter is based on the biokinetic data.

9. The hearing device according to claim 8, wherein: Determining a health parameter comprises determining a first biokinetic biomarker based on the biokinetic data, and wherein the first criterion is based on the first biokinetic biomarker.

10. The hearing device according to claim 9, wherein: Determining a first biokinetic biomarker comprises determining one or more of: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter; and wherein the first biokinetic biomarker is based on one or more of: a motion parameter, a tremor parameter, a jitter parameter, and a twitch parameter.

11. A hearing device according to any one of the preceding claims, wherein: The one or more sensors include one or more of: an optical sensor, a magnetic sensor, an electrical sensor, a motion sensor, and a capacitive sensor.

12. A hearing device according to any one of the preceding claims, wherein: The processor includes a machine learning circuit configured to operate according to a machine learning model, wherein determining the health parameter includes determining the health parameter using the machine learning model based on the sensor data.

13. A method of operating a hearing device, the method comprising: obtaining sensor data from one or more sensors of the hearing device; determining, based on the sensor data, a health parameter indicative of a cognitive state of a user of the hearing device; determining whether the health parameter meets a first criterion indicative of cognitive decline; as well as Based on the health parameter satisfying the first criterion, a health representation associated with the cognitive state is output.

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