Patent Application
20240041401
Embodiments herein relate to ear-wearable systems and devices for detecting hydration levels, dehydration and related methods.
Dehydration represents a significant health issue among elderly people. While anyone can experience dehydration, older individuals are physiologically more susceptible due to changes in bodily fluid reserves, renal function, and thirst response. Older individuals also frequently have medical conditions and/or take medications that further dehydrate the body. As a result, the prevalence of dehydration among elderly individuals is approximately 20-30% and approximately 50% of elderly who are admitted to a hospital are in a state of dehydration. In severe cases of dehydration, mortality may be greater than 50% due to dehydration and/or related complications.
Embodiments herein relate to ear-wearable systems and devices for detecting hydration levels, dehydration and related methods. In a first aspect, an ear-wearable hydration level monitoring system is included having a control circuit, a microphone, wherein the microphone is in electrical communication with the control circuit, a power supply, wherein the power supply is in electrical communication with the control circuit, and a sensor package, wherein the sensor package is in electrical communication with the control circuit and wherein the ear-wearable hydration level monitoring system is configured to process signals of one or more sensors of the sensor package to detect clinical symptoms of hydration levels.
In a second aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include a motion sensor.
In a third aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include at least one selected from the group consisting of a photoplethysmography sensor, a temperature sensor, and a motion sensor.
In a fourth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include at least one selected from the group consisting of a photoplethysmography (PPG) sensor, a electrocardiography (ECG) sensor, a temperature sensor, an electromyography (EMG) sensor, a motion sensor, an electroencephalography (EEG) sensor, and a glucose sensor.
In a fifth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the clinical symptoms of hydration level including one or more of rapid shallow breathing, increased pulse, low blood pressure, dizziness, change in voice quality, increased temperature.
In a sixth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the changes in voice quality include changes in tonal properties.
In a seventh aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to receive data from at least one external sensor.
In an eighth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the external sensor is selected from the group consisting of a humidity sensor, an ambient temperature sensor, a weight sensor, and a sensor disposed on a charging device for the ear-wearable hydration level monitoring system.
In a ninth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to process signals of the microphone to detect signs of hydration level.
In a tenth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include smacking or licking lips.
In an eleventh aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include changes in voice pitch and/or tremor.
In a twelfth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include rapid shallow breathing.
In a thirteenth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include dysphonia.
In a fourteenth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to issue an alert when hydration level clinical symptoms cross a threshold value.
In a fifteenth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the threshold value is dynamically set based on factors including one or more of an ambient temperature, an ambient humidity, and activity levels of the device wearer.
In a sixteenth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to identify drinking events based at least in part on signals from the microphone and record the same.
In a seventeenth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to issue an alert if a number of identified drinking events over a defined time period change by at least a threshold value.
In an eighteenth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to issue an alert when hydration level clinical symptoms cross a threshold value for at least a threshold period of time.
In a nineteenth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, further can include a first unit, wherein the first unit is configured to be wearable about a first ear, and a second unit, wherein the second unit is configured to be wearable about a second ear, wherein signals are exchanged between the first unit and the second unit.
In a twentieth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system can be configured to classify an observed pattern representing signals from the microphone and the sensor package into a scale of hydration levels using a machine learning derived algorithm.
In a twenty-first aspect, an ear-wearable hydration level monitoring system is included having a control circuit, a microphone, wherein the microphone is in electrical communication with the control circuit, a power supply, wherein the power supply is in electrical communication with the control circuit, a sealing member attached to a structure, and a humidity sensor attached to the structure, wherein the humidity sensor is configured to measure humidity within an ear canal of a wearer of the ear-wearable hydration level monitoring system between the sealing member and a tympanic membrane of the wearer.
In a twenty-second aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to receive data from at least one external sensor.
In a twenty-third aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the external sensor is selected from the group consisting of a humidity sensor, an ambient temperature sensor, a weight sensor, and a sensor disposed on a charging device for the ear-wearable hydration level monitoring system.
In a twenty-fourth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sealing member can include a sealing dome.
In a twenty-fifth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sealing member can include a sealing baffle.
In a twenty-sixth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, wherein the sealing baffle is mounted on a receiver.
In a twenty-seventh aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, further can include a sensor package, wherein the sensor package is in electrical communication with the control circuit.
In a twenty-eighth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include a motion sensor.
In a twenty-ninth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include at least one selected from the group consisting of a photoplethysmography sensor, a temperature sensor, and a motion sensor.
In a thirtieth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include at least one selected from the group consisting of a photoplethysmography (PPG) sensor, a electrocardiography (ECG) sensor, a temperature sensor, an electromyography (EMG) sensor, a motion sensor, an electroencephalography (EEG) sensor, and a glucose sensor.
In a thirty-first aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to issue an alert when ear canal humidity crosses a threshold value.
In a thirty-second aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the threshold value is dynamically set based on factors including one or more of an ambient temperature, an ambient humidity, and activity levels of the device wearer.
In a thirty-third aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to identify drinking events based at least in part on signals from the microphone and record the same.
In a thirty-fourth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable hydration level monitoring system is configured to issue an alert if a number of identified drinking events over a defined time period change by at least a threshold value.
In a thirty-fifth aspect, a method of monitoring an individual for hydration level using an ear-wearable monitoring system is included, the method including gathering signals with a microphone, gathering signals with a sensor package, and processing signals of the microphone and the sensor package to detect clinical symptoms of hydration level.
In a thirty-sixth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the clinical symptoms of hydration level including one or more of rapid shallow breathing, increased pulse, low blood pressure, dizziness, change in voice quality, increased temperature.
In a thirty-seventh aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the changes in voice quality include changes in tonal properties.
In a thirty-eighth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include processing signals of the microphone to detect signs of hydration level.
In a thirty-ninth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include dysphonia.
In a fortieth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include rapid shallow breathing.
In a forty-first aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include changes in voice pitch and/or tremor.
In a forty-second aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include smacking or licking lips.
In a forty-third aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include issuing an alert when hydration level clinical symptoms cross a threshold value.
In a forty-fourth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the threshold value is dynamically set based on factors including one or more of an ambient temperature, an ambient humidity, and activity levels of the device wearer.
In a forty-fifth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include identifying drinking events based at least in part on signals from the microphone and record the same.
In a forty-sixth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, wherein identifying drinking events based at least in part on signals from the microphone and record the same further includes issuing an alert if a number of identified drinking events over a defined time period change by at least a threshold value.
In a forty-seventh aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include issuing an alert when hydration level clinical symptoms cross a threshold value for at least a threshold period of time.
In a forty-eighth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include classifying an observed pattern representing signals from the microphone and the sensor package into a scale of hydration level severities using a machine learning derived algorithm.
In a forty-ninth aspect, a method of monitoring ear canal humidity to detect hydration level of an individual is included, the method including sealing off a portion of an individual's ear canal, measuring humidity within the sealed off portion of the ear canal, and evaluating the measured humidity to detect hydration level.
In a fiftieth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include issuing an alert when humidity values cross a threshold value.
In a fifty-first aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include issuing an alert when humidity values cross a threshold value for at least a threshold period of time.
In a fifty-second aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the threshold value is dynamically set based on factors including one or more of an ambient temperature, an ambient humidity, and activity levels of the device wearer.
In a fifty-third aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include identifying drinking events based at least in part on signals from a microphone and record the same.
In a fifty-fourth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include issuing an alert if a number of identified drinking events over a defined time period change by at least a threshold value.
In a fifty-fifth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include gathering signals with a microphone.
In a fifty-sixth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include gathering signals with a sensor package.
In a fifty-seventh aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include processing signals of the microphone and the sensor package to detect clinical symptoms of hydration level.
In a fifty-eighth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the clinical symptoms of hydration level including one or more of rapid shallow breathing, increased pulse, low blood pressure, dizziness, change in voice quality, increased temperature.
In a fifty-ninth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the changes in voice quality include changes in tonal properties.
In a sixtieth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the method can further include processing signals of the microphone to detect signs of hydration level.
In a sixty-first aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include smacking or licking lips.
In a sixty-second aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include changes in voice pitch and/or tremor.
In a sixty-third aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include rapid shallow breathing.
In a sixty-fourth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of hydration level include dysphonia.
In a sixty-fifth aspect, an ear-wearable dehydration monitoring system can be included having a control circuit, a microphone, wherein the microphone can be in electrical communication with the control circuit, a power supply, wherein the power supply can be in electrical communication with the control circuit, and a sensor package, wherein the sensor package can be in electrical communication with the control circuit, wherein the ear-wearable dehydration monitoring system can be configured to process signals of one or more sensors of the sensor package to detect clinical symptoms of dehydration.
In a sixty-sixth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include a motion sensor.
In a sixty-seventh aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include at least one selected from the group consisting of a photoplethysmography sensor, a temperature sensor, and a motion sensor.
In a sixty-eighth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the sensor package can include at least one selected from the group consisting of a photoplethysmography (PPG) sensor, a electrocardiography (ECG) sensor, a temperature sensor, an electromyography (EMG) sensor, a motion sensor, an electroencephalography (EEG) sensor, and a glucose sensor.
In a sixty-ninth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the clinical symptoms of dehydration can include one or more of rapid shallow breathing, increased pulse, low blood pressure, dizziness, change in voice quality, increased temperature.
In a seventieth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the changes in voice quality include changes in tonal properties.
In a seventy-first aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable dehydration monitoring system can be configured to receive data from at least one external sensor.
In a seventy-second aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the external sensor can be selected from the group consisting of a humidity sensor, an ambient temperature sensor, a weight sensor, and a sensor disposed on a charging device for the ear-wearable dehydration monitoring system.
In a seventy-third aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable dehydration monitoring system can be configured to process signals of the microphone to detect signs of dehydration.
In a seventy-fourth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of dehydration include smacking or licking lips.
In a seventy-fifth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of dehydration include pitch tremor.
In a seventy-sixth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of dehydration include rapid shallow breathing.
In a seventy-seventh aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the signs of dehydration include dysphonia.
In a seventy-eighth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable dehydration monitoring system can be configured to issue an alert when dehydration clinical symptoms cross a threshold value.
In a seventy-ninth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the threshold value can be dynamically set based on factors including one or more of an ambient temperature, an ambient humidity, and activity levels of the device wearer.
In an eightieth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable dehydration monitoring system can be configured to identify drinking events based at least in part on signals from the microphone and record the same.
In an eighty-first aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable dehydration monitoring system can be configured to issue an alert if a number of identified drinking events over a defined time period change by at least a threshold value.
In an eighty-second aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable dehydration monitoring system can be configured to issue an alert when dehydration clinical symptoms cross a threshold value for at least a threshold period of time.
In an eighty-third aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable dehydration monitoring system can further include a first unit, wherein the first unit can be configured to be wearable about a first ear, and a second unit, wherein the second unit can be configured to be wearable about a second ear, wherein signals can be exchanged between the first unit and the second unit.
In an eighty-fourth aspect, in addition to one or more of the preceding or following aspects, or in the alternative to some aspects, the ear-wearable dehydration monitoring system can be configured to classify an observed pattern representing signals from the microphone and the sensor package into a scale of dehydration severities using a machine learning derived algorithm.
This summary is an overview of some of the teachings of the present application and is not intended to be an exclusive or exhaustive treatment of the present subject matter. Further details are found in the detailed description and appended claims. Other aspects will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which is not to be taken in a limiting sense. The scope herein is defined by the appended claims and their legal equivalents.
Aspects may be more completely understood in connection with the following figures (FIGS.), in which:
While embodiments are susceptible to various modifications and alternative forms, specifics thereof have been shown by way of example and drawings, and will be described in detail. It should be understood, however, that the scope herein is not limited to the particular aspects described. On the contrary, the intention is to cover modifications, equivalents, and alternatives falling within the spirit and scope herein.
As referenced above, dehydration represents a significant health issue. This is particularly true among elderly people as they are physiologically more susceptible due to changes in bodily fluid reserves, renal function, and thirst response.
In the absence of a gold standard for diagnosing dehydration, observing clinical symptoms of hydration level is a key approach to detection. Systems and devices herein can automatically assess hydration to detect dehydration and notify the individual experiencing dehydration and/or a caregiver. This monitoring technology can provide benefits for users across all age groups. However, this monitoring technology can provide particular benefits for the elderly including prolonging how long elderly individuals are able to live independently and/or improving the quality of care received by individuals residing in assisted living.
Systems herein can include an ear-wearable device including a receiver and sensor package containing various sensors, such as microphone, photoplethysmography (PPG) sensor, electrocardiography (ECG) sensor, temperature sensor, electromyography (EMG) sensor, inertial measurement unit (IMU) sensor, electroencephalography (EEG) sensor, and glucose sensor. These sensors can be used individually or in conjunction to detect symptoms of hydration level or dehydration (including, but not limited to, increased pulse, rapid/shallow breathing, low blood pressure, dizziness, dry mouth, change in voice quality, increased temperature, delirium, and increased glucose concentration). For example, rapid, shallow breathing may be detected acoustically via microphone or by monitoring respiratory sinus arrhythmia via ECG or PPG. A single sensor can be used to detect multiple symptoms. For example, the microphone can detect a change in vocal quality in addition to detecting rapid, shallow breathing.
Aspects of embodiments herein can include the application of voice quality and core body temperature analysis to the detection of hydration level. Audio input to detect change in voice and the temperature increase are two elements that can be tied directly to the hydration level condition leading to improved sensitivity and specificity of hydration level detection. Combining voice quality and temperature data with the data output of sensors such as optical sensors and motion sensors can provide a rich set of biometric data leading to robust hydration level detection sufficient to allow discrimination between dehydration and other conditions that could otherwise appear similar to dehydration when only using a more limited set of biometric data.
Additional aspects of embodiments herein can include long-term tracking and personalized hydration level determination on an ear-wearable platform. Personalized baselines can be established to more accurately differentiate abnormal conditions. For example, a resting heart rate of 70 bpm may be clinically considered as normal, but for a trained athlete with normal resting heart rate of 50 bpm, a 70 bpm HR should be considered as elevated. In the case of blood pressure, many hypertensive patients may go through their day with systolic pressure above 140 mmHg even well hydrated. Therefore, a universal absolute threshold for detecting hydration level symptoms is unlikely to work across population. With an ear-wearable device that is worn every day over long periods of time, individualized baselines can be established and provide more accurate and personalized hydration level alerts.
Referring now to
Referring now to
As before, the device wearer 100 can be monitored using an ear-wearable hydration level monitoring system that can specifically include an ear-wearable device 102. In various scenarios, the ear-wearable hydration level monitoring system can also include a second ear-wearable device 202 that is worn on or about the other ear of the device wearer 100. In some embodiments, the ear-wearable devices 102 and 202 can function substantially independently and monitor for hydration level redundantly. In other embodiments, the ear-wearable device 102 can exchange signals/data and function cooperatively to more accurately detect possible dehydration.
Ear-wearable devices herein can take on many different specific forms. Referring now to
In various embodiments, the ear-wearable monitoring device 102 can include a control circuit, a microphone in electrical communication with the control circuit, a power supply in electrical communication with the control circuit, and a sensor package. The sensor package can include various sensors as described further below. The ear-wearable hydration level monitoring device 102 can be configured to process signals of one or more sensors of the sensor package to detect various clinical symptoms of hydration level or dehydration. In various embodiments, the ear-wearable hydration level monitoring device 102 can also be configured to process signals of the microphone to detect signs of hydration level. In various embodiments, the ear-wearable hydration level monitoring device 102 can be configured to issue an alert when hydration level or dehydration symptoms cross a threshold value and are sustained for at least a threshold period of time.
Ear-wearable devices herein can be worn on or in the ear. For example, referring now to
It will be appreciated that many different sensors can be included with embodiments herein and can detect various clinical symptoms of hydration level or dehydration. Referring now to
In this particular example, the sensor package 502 includes a photoplethysmography sensor 508, a temperature sensor 510, a motion sensor 512, and a microphone 506.
While a normal breathing rate will be different for each individual, breathing rates amongst an elderly set of patients can be from 12 to 18 breaths per minute for those living independently and 16 to 25 breaths per minute for those in long term-care. Further, while a normal pulse rate can depend on various factors, a normal pulse rate amongst the elderly can be from 60 to 100 beats per minute. Similarly, while a normal blood pressure for an individual can depend on various factors, a normal blood pressure is generally less than 120/80.
However, for most of the clinical symptoms of dehydration, it is particularly valuable to understand what normal values are for a given patient. As such, embodiments herein can detect such values and establish a baseline value for an individual over a period of time. For example, systems and/or devices herein can monitor data over time periods of hours, days, weeks, months or years in order to derive a baseline value for any of the measures that can serve as signs of dehydration. In some embodiments, the baseline value can be a moving average value of any of the measures or any combination thereof. In some embodiments, the baseline value can be a statistical measure. In some embodiments, the baseline value can account for diurnal cycles. For example, blood pressure typically rises sharply on waking in the morning and falls during sleep at night.
Thus, in various embodiments herein, symptoms of dehydration can include one or more of in increased rate of breathing over a baseline value, an increased pulse rate over a baseline value, a decreased blood pressure over a baseline value, an increased temperature over a baseline value, and/or an increase in dizziness or unsteadiness over a baseline value.
Changes in voice quality can also be a sign of dehydration. Voice quality can be assessed by capturing an individual's voice using a microphone. The signals from the microphone can then be processed using analog and/or digital signal processing techniques. As such, in various embodiments herein, an ear-wearable device can be configured to detect signs of dehydration including changes in voice pitch (typically a lowered pitch/frequency associated with hoarseness) and tremor (e.g., a quavering of the voice). In various embodiments herein, an ear-wearable device can be configured to detect signs of dehydration including dysphonia (hoarseness).
In various embodiments, the ear-wearable device or system can distinguish between speech or sounds associated with the device wearer and speech or sounds associated with a third party. This can be useful to be sure that detected changes in voice quality actually relate to the device wearer instead of another nearby individual.
Distinguishing between speech or sounds associated with the device wearer and speech or sounds associated with a third party can be performed in various ways. In some embodiments, this can be performed through signal analysis of the signals generated from the microphone(s). For example, in some embodiments, this can be done by filtering out frequencies of sound that are not associated with speech of the device-wearer. In some embodiments, such as where there are two or more microphones (on the same ear-wearable device or on different ear-wearable devices) this can be done through spatial localization of the origin of the speech or other sounds and filtering out, spectrally subtracting, or otherwise discarding sounds that do not have an origin within the device wearer. In some embodiments, such as where there are two or more ear-worn devices, own-voice detection can be performed and/or enhanced through correlation or matching of intensity levels and or timing.
In some cases, the system can include a bone conduction microphone in order to preferentially pickup the voice of the device wearer. In some cases, the system can include a directional microphone that is configured to preferentially pickup the voice of the device wearer. In some cases the system can include an intracanal microphone (a microphone configured to be disposed within the ear-canal of the device wearer) to preferentially pickup the voice of the device wearer. In some cases, the system can include a motion sensor (e.g., an accelerometer configured to be on or about the head of the wearer) to preferentially pick up skull vibrations associated with the vocal productions of the device wearer.
In some cases, an adaptive filtering approach can be used. By way of example, a desired signal for an adaptive filter can be taken from a first microphone and the input signal to the adaptive filter is taken from the second microphone. If the hearing aid wearer is talking, the adaptive filter models the relative transfer function between the microphones. Own-voice detection can be performed by comparing the power of an error signal produced by the adaptive filter to the power of the signal from the standard microphone and/or looking at the peak strength in the impulse response of the filter. The amplitude of the impulse response should be in a certain range in order to be valid for the own voice. If the user's own voice is present, the power of the error signal will be much less than the power of the signal from the standard microphone, and the impulse response has a strong peak with an amplitude above a threshold. In the presence of the user's own voice, the largest coefficient of the adaptive filter is expected to be within a particular range. Sound from other noise sources results in a smaller difference between the power of the error signal and the power of the signal from the standard microphone, and a small impulse response of the filter with no distinctive peak. Further aspects of this approach are described in U.S. Pat. No. 9,219,964, the content of which is herein incorporated by reference.
In another approach, a system herein can use a set of signals from a number of microphones. For example, a first microphone can produce a first output signal A from a filter and a second microphone can produce a second output signal B from a filter. The apparatus includes a first directional filter adapted to receive the first output signal A and produce a first directional output signal. A digital signal processor is adapted to receive signals representative of the sounds from the user's mouth from at least one or more of the first and second microphones and to detect at least an average fundamental frequency of voice (pitch output) F0. A voice detection circuit is adapted to receive the second output signal B and the pitch output F0 and to produce an own voice detection trigger T. The apparatus further includes a mismatch filter adapted to receive and process the second output signal B, the own voice detection trigger T, and an error signal E, where the error signal E is a difference between the first output signal A and an output O of the mismatch filter. A second directional filter is adapted to receive the matched output O and produce a second directional output signal. A first summing circuit is adapted to receive the first directional output signal and the second directional output signal and to provide a summed directional output signal (D). In use, at least the first microphone and the second microphone are in relatively constant spatial position with respect to the user's mouth, according to various embodiments. Further aspects of this approach are described in U.S. Pat. No. 9,210,518, the content of which is herein incorporated by reference.
In various embodiments, the ear-wearable hydration level monitoring system (described further below) can be configured to classify an observed pattern representing signals from the microphone 506 and the sensor package 502 into a scale of hydration level or dehydration severities using a machine learning derived algorithm. By way of example, dehydration can be classified into mild dehydration, moderate dehydration, and severe dehydration. In some embodiments, machine learning analysis (such as the use of a machine learning classification algorithm) can be used to evaluate current clinical measures of dehydration (including any of those mentioned herein) and classify the same as being evidence of mild dehydration, moderate dehydration, and severe dehydration.
In various embodiments herein, one or more sensors can be operatively connected to a controller (such as a control circuit described further below) or another processing resource (such as a processor of another device or a processing resource in the cloud). The controller or other processing resource can be adapted to receive data representative of a characteristic of the subject from one or more of the sensors and/or determine statistics of the subject over a monitoring time period based upon the data received from the sensor. As used herein, the term “data” can include a single datum or a plurality of data values or statistics. The term “statistics” can include any appropriate mathematical calculation or metric relative to data interpretation, e.g., probability, confidence interval, distribution, range, or the like. Further, as used herein, the term “monitoring time period” means a period of time over which characteristics of the subject are measured and statistics are determined. The monitoring time period can be any suitable length of time, e.g., 1 millisecond, 1 second, 10 seconds, 30 seconds, 1 minute, 10 minutes, 30 minutes, 1 hour, 1 day, 1 week, etc., or a range of time between any of the foregoing time periods.
Any suitable technique or techniques can be utilized to determine statistics for the various data from the sensors, e.g., direct statistical analyses of time series data from the sensors, differential statistics, comparisons to baseline or statistical models of similar data, etc. Such techniques can be general or individual-specific and represent long-term or short-term behavior. These techniques could include standard pattern classification methods such as Gaussian mixture models, clustering as well as Bayesian approaches, neural network models and deep learning.
Further, in some embodiments, the controller can be adapted to compare data, data features, and/or statistics against various other patterns, which could be prerecorded patterns (baseline patterns) of the particular individual wearing an ear-wearable device herein, prerecorded patterns (group baseline patterns) of a group of individuals wearing ear-wearable devices herein, one or more predetermined patterns that serve as patterns indicative of particular hydration levels or dehydration (positive example patterns), one or more predetermined patterns that service as patterns indicative of the absence of particular hydration levels or dehydration (negative example patterns), or the like. As merely one scenario, if a pattern is detected in an individual that exhibits similarity crossing a threshold value to a positive example pattern or substantial similarity to that pattern, then that can be taken as an indication of an occurrence of a particular hydration level or dehydration.
Similarity and dissimilarity can be measured directly via standard statistical metrics such normalized Z-score, or similar multidimensional distance measures (e.g. Mahalanobis or Bhattacharyya distance metrics), or through similarities of modeled data and machine learning. These techniques can include standard pattern classification methods such as Gaussian mixture models, clustering as well as Bayesian approaches, neural network models, and deep learning.
As used herein the term “substantially similar” means that, upon comparison, the sensor data are congruent or have statistics fitting the same statistical model, each with an acceptable degree of confidence. The threshold for the acceptability of a confidence statistic may vary depending upon the subject, sensor, sensor arrangement, type of data, context, condition, etc.
The statistics associated with the health status of an individual (and, in particular, their status with respect to hydration level), over the monitoring time period, can be determined by utilizing any suitable technique or techniques, e.g., standard pattern classification methods such as Gaussian mixture models, clustering, hidden Markov models, as well as Bayesian approaches, neural network models, and deep learning.
In some embodiments, the system can include and/or utilize a greater or lesser number of sensors. For example, referring now to
clinical symptoms of dehydration 504 can be detected. In this embodiments, clinical symptoms of dehydration 504 can include rapid shallow breathing 514, an increased pulse rate 516, a low blood pressure 518, a change in voice quality 520, an increased temperature 522, dizziness 524, a dry mouth 610, delirium 612, and an increased glucose concentration 614.
Beyond changes in voice quality, microphones herein can be used to detect other occurrences that can be indicative of hydration levels or dehydration. By way of example, in various embodiments, the signs of dehydration include can include smacking or licking lips. The smacking or licking of lips result in unique aural signatures/patterns that can be detected by evaluating the signals from a microphone herein using analog and/or digital signal processing techniques and techniques such as pattern matching approaches described in greater detail below.
In various embodiments, the ear-wearable dehydration monitoring system can be configured to issue an alert, notification, or warning when dehydration clinical symptoms cross a threshold value. Alerts, notifications, and warnings can take various forms including audio notifications such as warning sounds or warning messages delivered by the ear-wearable device or another device, visual notifications such as notification messages on an accessory device, network delivered notifications, haptic notifications, and the like.
High ambient temperature (such as above 75, 80, 85, or 90 degrees Fahrenheit), high humidity (such as above 80, 85, or 90 percent relative humidity), and high or above average activity of the device wearer can all created conditions where water loss can be particularly rapid. As such, in some embodiments, the risk of dehydration can be particularly acute when high ambient temperature, high humidity, and/or high activity levels are detected. The risk of dehydration can also be high in very low humidity environments. In response to such elevated risk, in various embodiments herein, a threshold value for issuing an alert, notification, or warning can be dynamically set based on factors including one or more of an ambient temperature 114, an ambient humidity, and activity levels of the device wearer 100. In various embodiments, the threshold value can be lowered (e.g., the sensitivity of detection can be increased) under such conditions so that an alert can be sent sufficiently early to mitigate the onset and effects of dehydration.
In some embodiments, a system herein can also include and/or utilize an accessory device. The accessory device can be used for various purposes. In some embodiments, the accessory device can be used to provide data from additional sensors that may be a part of the accessory device.
In various embodiments herein, the ear-wearable dehydration monitoring system can be configured to receive data from at least one external sensor or external source. In various embodiments herein, the external sensor can be including at least one of a humidity sensor, an ambient temperature sensor, a weight sensor, and a sensor disposed on a charging device for the ear-wearable dehydration monitoring system. External sources can include, for example, things such as a source of weather information (such as a weather API), an electronic medical record, or the like.
In some embodiments, an accessory device (such as a smart phone) can be used to provide instructions or recommendations to the device wearer, such as instructions for mitigating a detected state of dehydration. In some embodiments, the accessory device can be used to provide instructions or recommendations to a care provider or health professional, such as instructions for mitigating a detected state of dehydration.
Referring now to
Ear-wearable hydration level monitoring devices and/or systems herein can include many different components. Referring now to
The ear-wearable devices 102, 202 can include various components such as a receiver 306 and a microphone 506. The ear-wearable devices 102, 202 can also include a data store containing a data log 802. The ear-wearable devices 102, 202 also includes a battery 806. The ear-wearable devices 102, 202 also includes a machine learning processing unit 808. The machine learning processing unit can include components such as those described with respect to a control circuit herein. The machine learning processing unit can function to execute machine learning algorithms on data provided by the sensors and/or utilize patterns and/or algorithms derived using machine learning analysis with respect to the sensor data. The ear-wearable devices 102, 202 can also include an antenna 850 and an electroacoustic transducer 854.
The ear-wearable devices 102, 202 include a sensor package 502 that can include a photoplethysmography sensor 508, a temperature sensor 510, a motion sensor 512, an electrocardiography sensor 602, an electromyography sensor 604, an electroencephalography sensor 606, and/or a glucose sensor 608. In this embodiment, the ear-wearable hydration level monitoring system also includes a first accessory device 104 (such as a smartphone or other computing device) and a second accessory device 864.
Once a hydration level or dehydration is detected, the system can notify the device wearer by relaying a notification acoustically via receiver in the ear or electronically to an accessory or monitoring device with wireless capability (e.g., smartphone, smartwatch, tablet, computer, etc.). Electronic notification can be transmitted to the device wearer's accessory device or a monitoring device of a personal (e.g., family member) or professional (e.g., assisted living staff) caretaker.
In various embodiments, the ear-wearable hydration level monitoring system (described further below) can be configured to classify an observed pattern representing signals from the microphone 506 and the sensor package 502 into a scale of hydration levels or dehydration severities using a machine learning derived algorithm.
Referring now to
In some embodiments, hydration levels or dehydration can be detected by sensing the humidity of an air-filled area of the body, such as within the external auditory ear canal. Referring now to
In some embodiments, a shroud or similar cover can be used around the hydration sensor to isolate a small space within the external auditory canal. Referring now to
It will be appreciated that ear-wearable devices herein can take on many different forms. In some embodiments, the ear-wearable device can be in the form of an in-the-ear style custom ear-wearable device. While not intending to be bound by theory, it is believed that certain form factors, such as an in-the-ear style custom ear-wearable device, can have better mechanical coupling to the external auditory canal which can be advantageous for measuring humidity therein.
Referring now to
The ear-wearable device housing 1302 can define a battery compartment 1310 in which a battery can be disposed to provide power to the device. The ear-wearable device 102 can also include a receiver 1312. The receiver 1312 can include a component that converts electrical impulses into sound, such as an electroacoustic transducer, speaker, or loudspeaker. The housing 1302 can also define a component compartment 1314 that can contain electrical and other components including but not limited to a microphone, a processor, memory, various sensors, one or more communication devices, power management circuitry, and a control circuit. A cable 1316 or connecting wire can include one or more electrical conductors and provide electrical communication between components inside of the component compartment 1314 and components inside of the receiver 1312.
The shell 1304 extends from an ear canal end 1322 to an aperture end 1326. At the aperture end 1326, the shell 1304 defines an aperture that is closed by the faceplate 1306. The faceplate 1306 is sealed to the shell 1304. The faceplate 1306 is shown in
In various embodiments, a humidity sensor 1002 can be disposed on or adjacent to the ear canal end 1322. The humidity sensor 1002 can be, for example, a capacitive humidity sensor, a resistive humidity sensor, a thermal conductivity humidity sensor, or the like. When positioned within the ear canal (see, e.g.,
The ear-wearable device 102 shown in
As can be seen in
Referring now to
In various embodiments, ear-wearable devices herein and related systems can be used to detect oropharyngeal events (both normal and abnormal) including, but not limited to, mastication, swallowing, drinking, and the like. These events can be used to identify events including the input of water (such as drinking of fluids). As such, embodiments herein include ear-worn devices and related systems that can be used to track aspects such as eating, drinking, swallowing, and other oropharyngeal events. In some embodiments, an exemplary a first ear-worn device can include a control circuit, a motion sensor, one or more microphones, an electroacoustic transducer, and a power supply or power supply circuit. The ear-worn device system can be configured to monitor signals from at least one of the motion sensor and the microphone and evaluate the signals to identify oropharyngeal events. As such, in various embodiments herein, an ear-wearable dehydration monitoring system can be configured to identify drinking events based at least in part on signals from the microphone and record the same.
Certain oropharyngeal events such as drinking are frequently accompanied by a characteristic head movement immediately prior to the event. For example, an individual commonly tips their head backward before beginning to drink from a glass. In some embodiments, ear-worn device systems herein are configured to evaluate the signals from a motion sensor to identify when the device wearer tips their head backward. In some embodiments, signal evaluation to identify oropharyngeal events includes evaluating signals from the motion sensor followed sequentially by evaluating signals from the microphone to detect sounds consistent with drinking.
In some embodiments, weighting factors for identification of oropharyngeal events, such as drinking events, can vary depending on whether another event is detected. For example, weighting factors can be changed such that signals from one or more microphones, motion sensors, or other sensors occurring immediately after head or jaw movement characteristic of the device wearer bringing a drink to their lips are more likely to be deemed a drinking event than are signals from the sensors in the absence of such head or jaw movements.
In various embodiments, devices and systems herein can be configured to distinguish between sounds originating at or near a sound origin associated with drinking versus sounds originating at other points within or outside of the body of the subject. In an embodiment, signal evaluation or processing to identify drinking events can include evaluating signals from the microphone of the first ear-worn device and signals from a microphone of the second ear-worn device and selecting those signals emanating from a spatial location that is laterally between the first ear-worn device and the second ear-worn device and posterior to the lips of the ear-worn device wearer.
In some embodiments, the number of identified drinking events (with or without an estimation of how much fluid was consumed) can be used to evaluate whether dehydration or circumstances that can lead to dehydration are present. In some embodiments, the system herein can track average numbers of drinking events over given time periods (such as per hour, per day, per week, etc.) and compare such numbers against those previously recorded for the individual (as one example of a baseline for the individual). In various embodiments, the ear-wearable dehydration monitoring system can be configured to issue an alert if a number of identified drinking events over a defined time period change by at least a threshold value. For example, the system can issue an alert if the number of identified drinking events decreases by 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, or 80 percent or more, or a value falling within a range between any of the foregoing.
Referring now to
An audio output device 1616 is electrically connected to the DSP 1612 via the flexible mother circuit 1618. In some embodiments, the audio output device 1616 comprises an electroacoustic transducer or speaker (coupled to an amplifier). In other embodiments, the audio output device 1616 comprises an amplifier coupled to an external receiver 1620 adapted for positioning within an ear of a wearer. The external receiver 1620 can include an electroacoustic transducer, speaker, or loudspeaker.
The ear-wearable device 102 may incorporate a communication device 1608 coupled to the flexible mother circuit 1618 and to an antenna 1602 directly or indirectly via the flexible mother circuit 1618. The communication device 1608 can be a BLUETOOTH® transceiver, such as a BLE (BLUETOOTH® low energy) transceiver or other transceiver(s) (e.g., an IEEE 802.11 compliant device). The communication device 1608 can be configured to communicate with one or more external devices, such as those discussed previously, in accordance with various embodiments. In various embodiments, the communication device 1608 can be configured to communicate with an external visual display device such as a smart phone, a video display screen, a tablet, a computer, or the like.
In some embodiments, ear-wearable devices 102 of the present disclosure can incorporate an antenna arrangement coupled to a high-frequency radio, such as a 2.4 GHz radio. The radio can conform to an IEEE 802.11 (e.g., WIFI®) or BLUETOOTH® (e.g., BLE, BLUETOOTH® 4.2 or 5.0) specification, for example. It is understood that ear-wearable devices of the present disclosure can employ other radios, such as a 900 MHz radio or radios operating at other frequencies or frequency bands. Ear-wearable device of the present disclosure can also include hardware, such as one or more antennas, for NFMI or NFC wireless communications. Ear-wearable devices of the present disclosure can be configured to receive streaming audio (e.g., digital audio data or files) from an electronic or digital source.
Ear-wearable devices 102 of the present disclosure can be configured to receive streaming audio (e.g., digital audio data or files) from an electronic or digital source. Representative electronic/digital sources (also referred to herein as accessory devices) include an assistive listening system, a TV streamer, a radio, a smartphone, a cell phone/entertainment device (CPED) or other electronic device that serves as a source of digital audio data or files. Systems herein can also include these types of accessory devices as well as other types of devices.
In various embodiments, the ear-wearable device 102 can also include a control circuit 1622 and a memory storage device 1624. The control circuit 1622 can be in electrical communication with other components of the device. In some embodiments, a clock circuit 1626 can be in electrical communication with the control circuit. The control circuit 1622 can execute various operations, such as those described herein. The control circuit 1622 can include various components including, but not limited to, a microprocessor, a microcontroller, an FPGA (field-programmable gate array) processing device, an ASIC (application specific integrated circuit), or the like. The memory storage device 1624 can include both volatile and non-volatile memory. The memory storage device 1624 can include ROM, RAM, flash memory, EEPROM, SSD devices, NAND chips, and the like. The memory storage device 1624 can be used to store data from sensors as described herein and/or processed data generated using data from sensors as described herein.
It will be appreciated that various of the components described in
Accessory devices herein can include various different components. In some embodiments, the accessory device can be a personal communications device, such as a smart phone. However, the accessory device can also be other things such as a secondary wearable device, a handheld computing device, a dedicated location determining device (such as a handheld GPS unit), or the like.
Referring now to
It will be appreciated that in various embodiments herein, a device or a system can be used to detect a pattern or patterns (such as patterns of data from sensors) indicative of a state of dehydration. Also, it will be appreciated that in various embodiments herein, a device or a system can be used to detect a pattern or patterns indicative of a specific event, such as drinking which can impact an individual's present state of hydration. Such patterns can be detected in various ways. Some techniques are described elsewhere herein, but some further examples will now be described.
As merely one example, one or more sensors can be operatively connected to a controller (such as the control circuit described in
Any suitable technique or techniques can be utilized to determine statistics for the various data from the sensors, e.g., direct statistical analyses of time series data from the sensors, differential statistics, comparisons to baseline or statistical models of similar data, etc. Such techniques can be general or individual-specific and represent long-term or short-term behavior. These techniques could include standard pattern classification methods such as Gaussian mixture models, clustering as well as Bayesian approaches, neural network models and deep learning.
Further, in some embodiments, the controller can be adapted to compare data, data features, and/or statistics against various other patterns, which could be prerecorded patterns (baseline patterns) of the particular individual wearing an ear-wearable device herein, prerecorded patterns (group baseline patterns) of a group of individuals wearing ear-wearable devices herein, one or more predetermined patterns that serve as positive example patterns (such as patterns indicative of hydration/dehydration states), negative example patterns, or the like. As merely one scenario, if a pattern is detected in an individual that exhibits similarity crossing a threshold value to a positive example pattern or substantial similarity to that pattern, then that can be taken as an indication of the presence of a level of hydration/dehydration associated with the positive example pattern. Positive and/or negative example patterns can be stored or accessed for use covering those items to be detected in accordance with embodiments herein including, but not limited to, states of dehydration/hydration, clinical signs of dehydration, events impacting dehydration such as drinking and activity, relevant events with characteristic sounds such as the licking or smacking of lips, environmental conditions impacting dehydration such as weather, temperature, humidity, and the like and other items discussed elsewhere herein.
Similarity and dissimilarity can be measured directly via standard statistical metrics such normalized Z-score, or similar multidimensional distance measures (e.g. Mahalanobis or Bhattacharyya distance metrics), or through similarities of modeled data and machine learning. These techniques can include standard pattern classification methods such as Gaussian mixture models, clustering as well as Bayesian approaches, neural network models, and deep learning.
As used herein the term “substantially similar” means that, upon comparison, the sensor data are congruent or have statistics fitting the same statistical model, each with an acceptable degree of confidence. The threshold for the acceptability of a confidence statistic may vary depending upon the subject, sensor, sensor arrangement, type of data, context, condition, etc.
The statistics associated with the hydration/dehydration status of an individual over the monitoring time period, can be determined by utilizing any suitable technique or techniques, e.g., standard pattern classification methods such as Gaussian mixture models, clustering, hidden Markov models, as well as Bayesian approaches, neural network models, and deep learning.
Many different methods are contemplated herein, including, but not limited to, methods of making devices and systems herein, methods of using devices and systems herein, methods of monitoring an individual for hydration levels or dehydration, methods of monitoring ear canal humidity, and the like. Aspects of system/device operation described elsewhere herein can be performed as operations of one or more methods in accordance with various embodiments herein.
Specifically, in various embodiments herein, a method of monitoring an individual for hydration levels or dehydration using an ear-wearable monitoring system is included. The method can include gathering signals with a microphone, gathering signals with a sensor package, and processing signals of the microphone and the sensor package to detect clinical symptoms of hydration levels or dehydration.
In an embodiment of the method, the clinical symptoms of hydration levels or dehydration including one or more of rapid shallow breathing, increased pulse, low blood pressure, dizziness, change in voice quality, increased temperature.
In an embodiment, the method can further include processing signals specifically of the microphone to detect signs of hydration levels or dehydration. In an embodiment of the method, the signs of hydration levels or dehydration include dysphonia. In an embodiment of the method, the changes in voice quality include changes in tonal properties. In an embodiment of the method, the signs of hydration levels or dehydration include changes in voice pitch and/or tremor. In an embodiment of the method, the signs of hydration levels or dehydration include smacking or licking lips.
In an embodiment, the method can further include issuing an alert when hydration levels or dehydration clinical symptoms cross a threshold value. In some embodiments, the threshold values are predetermined. However, in some embodiments of the method, the threshold value is dynamically set based on factors including one or more of an ambient temperature, an ambient humidity, and activity levels of the device wearer.
In various embodiments, the method can further include identifying drinking events based at least in part on signals from the microphone and record the same. In an embodiment of the method, identifying drinking events based at least in part on signals from the microphone and record the same further comprises issuing an alert if a number of identified drinking events over a defined time period change by at least a threshold value.
In an embodiment, the method can further include issuing an alert when dehydration clinical symptoms cross a threshold value for at least a threshold period of time.
In an embodiment, the method can further include classifying an observed pattern representing signals from the microphone and the sensor package into a scale of dehydration severities using a machine learning derived algorithm.
In an embodiment, a method of monitoring ear canal humidity to detect dehydration of an individual is included. The method can include sealing off a portion of an individual's ear canal, measuring humidity within the sealed off portion of the ear canal, and evaluating the measured humidity to detect dehydration.
In an embodiment, the method can further include issuing an alert when humidity values cross a threshold value. In an embodiment, the method can further include issuing an alert when humidity values cross a threshold value for at least a threshold period of time. In some embodiments the threshold value can be predetermined. In other embodiments of the method, the threshold value is dynamically set based on factors including one or more of an ambient temperature, an ambient humidity, and activity levels of the device wearer.
Ear-wearable devices herein can include one or more sensor packages (including one or more discrete or integrated sensors) to provide data. The sensor package can comprise one or a multiplicity of sensors. In some embodiments, the sensor packages can include one or more motion sensors (or movement sensors) amongst other types of sensors. Motion sensors herein can include inertial measurement units (IMU), accelerometers, gyroscopes, barometers, altimeters, and the like. The IMU can be of a type disclosed in commonly owned U.S. Pat. No. 9,848,273, which is incorporated herein by reference. In some embodiments, electromagnetic communication radios or electromagnetic field sensors (e.g., telecoil, NFMI, TMR, GMR, etc.) sensors may be used to detect motion or changes in position. In various embodiments, the sensor package can include a magnetometer. In some embodiments, biometric sensors may be used to detect body motions or physical activity. Motions sensors can be used to track movement of a patient in accordance with various embodiments herein.
In some embodiments, the motion sensors can be disposed in a fixed position with respect to the head of a patient, such as worn on or near the head or ears. In some embodiments, the operatively connected motion sensors can be worn on or near another part of the body such as on a wrist, arm, or leg of the patient.
According to various embodiments, the sensor package can include one or more of an IMU, and accelerometer (3, 6, or 9 axis), a gyroscope, a barometer, an altimeter, a magnetometer, a magnetic sensor, an eye movement sensor, a pressure sensor, an acoustic sensor, a telecoil, a heart rate sensor, a global positioning system (GPS), a microphone, an acoustic sensor, a wireless radio antenna, an air quality sensor, an optical sensor, a light sensor, an image sensor, a temperature sensor, a physiological sensor such as a blood pressure sensor, an oxygen saturation sensor, a blood glucose sensor (optical or otherwise), a galvanic skin response sensor, a cortisol level sensor (optical or otherwise), an electrocardiogram (ECG) sensor, electroencephalography (EEG) sensor which can be a neurological sensor, eye movement sensor (e.g., electrooculogram (EOG) sensor), myographic potential electrode sensor (EMG), a heart rate monitor, a pulse oximeter or oxygen saturation sensor (SpO2), blood perfusion sensor, hydrometer, sweat sensor, humidity sensor, cerumen sensor, pupillometry sensor, hematocrit sensor, or the like.
In some embodiments, the sensor package can be part of an ear-wearable device. However, in some embodiments, the sensor packages can include one or more additional sensors that are external to an ear-wearable device. For example, various of the sensors described above can be part of a wrist-worn or ankle-worn sensor package, or a sensor package supported by a chest strap. In some embodiments, sensors herein can be disposable sensors that are adhered to the device wearer (“adhesive sensors”) and that provide data to the ear-wearable device or another component of the system.
Data produced by the sensor(s) of the sensor package can be operated on by a processor of the device or system.
As used herein the term “inertial measurement unit” or “IMU” shall refer to an electronic device that can generate signals related to a body's specific force and/or angular rate. IMUs herein can include one or more accelerometers (3, 6, or 9 axis) to detect linear acceleration and a gyroscope to detect rotational acceleration and/or velocity. In some embodiments, an IMU can also include a magnetometer to detect a magnetic field.
An eye movement sensor herein be, for example, an electrooculographic (EOG) sensor, such as an EOG sensor disclosed in commonly owned U.S. Pat. No. 9,167,356, which is incorporated herein by reference. The pressure sensor can be, for example, a MEMS-based pressure sensor, a piezo-resistive pressure sensor, a flexion sensor, a strain sensor, a diaphragm-type sensor and the like.
A temperature sensor herein can be, for example, a thermistor (thermally sensitive resistor), a resistance temperature detector, a thermocouple, a semiconductor-based sensor, an infrared sensor, or the like.
A blood pressure sensor herein can be, for example, a pressure sensor. The heart rate sensor can be, for example, an electrical signal sensor, an acoustic sensor, a pressure sensor, an infrared sensor, an optical sensor, or the like.
A oxygen saturation sensor (such as a blood oximetry sensor) herein can be, for example, an optical sensor, an infrared sensor, a visible light sensor, or the like.
An electrical signal sensor herein can include two or more electrodes and can include circuitry to sense and record electrical signals including sensed electrical potentials and the magnitude thereof (according to Ohm's law where V=IR) as well as measure impedance from an applied electrical potential.
A humidity sensor herein can be, for example, a capacitive humidity sensor, a resistive humidity sensor, a thermal conductivity humidity sensor, or the like.
It will be appreciated that the sensor package can include one or more sensors that are external to the ear-wearable device. In addition to the external sensors discussed hereinabove, the sensor package can comprise a network of body sensors (such as those listed above) that sense movement of a multiplicity of body parts (e.g., arms, legs, torso).
It should be noted that, as used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the content clearly dictates otherwise. It should also be noted that the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise.
It should also be noted that, as used in this specification and the appended claims, the phrase “configured” describes a system, apparatus, or other structure that is constructed or configured to perform a particular task or adopt a particular configuration. The phrase “configured” can be used interchangeably with other similar phrases such as arranged and configured, constructed and arranged, constructed, manufactured and arranged, and the like.
All publications and patent applications in this specification are indicative of the level of ordinary skill in the art to which this invention pertains. All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated by reference.
As used herein, the recitation of numerical ranges by endpoints shall include all numbers subsumed within that range (e.g., 2 to 8 includes 2.1, 2.8, 5.3, 7, etc.).
The headings used herein are provided for consistency with suggestions under 37 CFR 1.77 or otherwise to provide organizational cues. These headings shall not be viewed to limit or characterize the invention(s) set out in any claims that may issue from this disclosure. As an example, although the headings refer to a “Field,” such claims should not be limited by the language chosen under this heading to describe the so-called technical field. Further, a description of a technology in the “Background” is not an admission that technology is prior art to any invention(s) in this disclosure. Neither is the “Summary” to be considered as a characterization of the invention(s) set forth in issued claims.
The embodiments described herein are not intended to be exhaustive or to limit the invention to the precise forms disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art can appreciate and understand the principles and practices. As such, aspects have been described with reference to various specific and preferred embodiments and techniques. However, it should be understood that many variations and modifications may be made while remaining within the spirit and scope herein.
This application is being filed as a PCT International Patent application on Dec. 22, 2021 in the name of Starkey Laboratories, Inc., a U.S. national corporation, applicant for the designation of all countries, and Paul N. Reinhart, a U.S. Citizen, and Andy S. Lin, a U.S. Citizen, inventors for the designation of all countries, and claims priority to U.S. Provisional Patent Application No. 63/130,194, filed Dec. 23, 2020, the contents of which are herein incorporated by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/064886 | 12/22/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63130194 | Dec 2020 | US |
