Patent Application
20230211117
The disclosed concept pertains to biosignal monitoring devices used to detect various physiological states of the wearer, and, in particular, to methods and systems for improving the fit of such devices in an automated manner.
Wearable biosignal monitoring devices are often used to provide valuable insight into a variety of conditions of the device user: daily activity patterns, mood, sleep stage, etc. These devices use physiological sensors to detect biosignals such as EEG, heart rate, SpO2, etc. in order to provide such insight. Many wearable biosignal monitoring devices are available for at-home consumer use, including, for example and without limitation, wrist-worn smart watch devices, respiration belts, smart adhesive patches, finger sensors, and therapeutic sleep headbands. One such at-home biosignal monitoring device is the SmartSleep Deep Sleep Headband (DSH) manufactured and distributed by Philips. The DSH is a non-invasive portable head-worn device which uses biosignal monitoring to determine the sleep stage of the wearer and selectively deliver soft non-arousing tones through a speaker at appropriate stages of sleep in order to stimulate deep sleep, thereby reducing daytime sleepiness associated with insufficient sleep or poor perceived sleep quality. Research shows that peripheral stimulation (e.g. electric, magnetic, or sensory) during deep sleep can improve slow-wave activity and increase the restorative value of sleep. The DSH delivers stimulation to a user in a closed loop manner by: monitoring the user’s electroencephalogram (EEG) during sleep, identifying appropriate moments for stimulation, and delivering auditory stimulation to improve sleep slow waves without causing arousals. The DSH monitors EEG though electrodes integrated in the headband and delivers therapy via a bone conduction speaker or over a set of ear speakers also integrated in the headband.
The efficacy of wearable biosignal monitoring devices depends on the device being properly fit and and the sensors being properly positioned on the user so that the physiological activity of the user can be measured accurately. A consumer who uses a wearable biosignal monitoring device at home may not realize when a sensor is unable to accurately sense a physiological signal or how to properly adjust the fit of the monitoring device in order to fix the positioning of the sensor for optimal signal detection. If the device is too loose, the signal quality of the bio sensor will likely be poor due to bad detection of the desired physiological signal. Conversely, if the device is too tight, user comfort will be affected and may lead to non-compliance. For example, with respect to the DSH, if the device is too loose, the signal quality will be affected leading to poor detection of sleep stages and reduced therapy or no therapy delivery whatsoever. Conversely, if the device is too tight, user comfort will be affected leading to disturbed sleep, reports of pressure points, and headache.
Accordingly, there is room for improvement in methods and systems for adjusting the positioning of sensors included in wearable biosignal monitoring devices and for adjusting the fit of such devices.
Accordingly, it is an object of the present invention to provide, in an exemplary embodiment, a fit and positioning optimization system for a biosignal monitoring device that is configured to monitor the physiological signals of a user. The optimization system includes: a number of bio sensors configured to sense physiological activity of the user; a device fit event detector, the fit event detector comprising at least one of: a number of sensory event actuators configured to deliver sensory stimuli to the user, or a biosignal event detection unit comprising instructions for executing a number of event detection algorithms; a controller comprising a signal analysis unit and configured to be in electrical communication with the number of bio sensors and the fit event detector; and a user interface configured to be in electrical communication with the controller. The controller is configured to use the fit event detector to define a user response based on physiological activity signals sensed by the bio sensors. The signal analysis unit is configured to store physiological response reference data, and to determine if the user response is within a predefined normative range based on the reference data. The controller is configured to form a recommendation, based on whether the user response is within the normative range, indicating whether adjustments to the fit or positioning of the device are required in order to optimize monitoring of biosignals. The controller is also configured to then alert the user of the recommendation through the user interface.
In another embodiment, a method for optimizing fit and positioning for a biosignal monitoring device comprises first positioning the device on the user, said device being configured to monitor physiological activity of a user. The device includes a number of bio sensors, a fit event detector, a controller comprising a signal analysis unit, and a user interface configured to be in electrical communication with the controller. The fit event detector comprises at least one of: a number of sensory event actuators configured to deliver sensory stimuli to the user, or a biosignal event detection unit comprising instructions for executing a number of event detection algorithms. The signal analysis unit stores physiological response reference data and is configured to be in electrical communication with the number of bio sensors and the fit detector. The method further comprises: sensing physiological activity with the number of the bio sensors; defining, using the fit event detector, a user response based on the sensed physiological activity; determining, with the signal analysis unit, if the defined user response is within a predefined normative range based on the reference data; forming a recommendation, with the signal analysis unit, indicating whether the fit and/or positioning of the device require adjustments in order to optimize monitoring of physiological activity, based on whether the defined user response is within the normative range; and alerting the user of the recommendation through the user interface.
In a further embodiment, a method for optimizing fit and positioning for a wearable sleep therapy device comprises positioning the device on the user and performing a number of fit analyses after the device has been positioned on the user, the sleep therapy device being configured to monitor sleep stages of a user and deliver non-arousing auditory tones to the user to enhance sleep quality in response to the monitoring of the sleep stages. The device comprises: a number of EEG sensors; a number of speakers configured to selectively deliver sleep-improving auditory tones to the user during sleep; a fit event detector comprising at least one of a number of sensory event actuators comprising the number of speakers or a biosignal event detection unit comprising instructions for executing a number of event detection algorithms; a controller comprising a signal analysis unit configured to be in electrical communication with the number of EEG sensors, the signal analysis unit being configured to store physiological reference data; and a user interface configured to be in electrical communication with the controller. Performing the number of fit analyses comprises: sensing physiological activity of the user with the number of EEG sensors; defining, using the fit event detector, a user response based on physiological activity sensed by the number of EEG sensors; determining, with the signal analysis unit, if the physiological activity is within a predefined normative range based on the reference data; forming a recommendation, with the signal analysis unit, indicating whether the fit of the device requires adjustments in order to properly position either the EEG sensors or the speakers for monitoring of sleep stages or delivery of the non-arousing auditory tones, based on whether the fit-detecting physiological activity is within the normative range; and alerting the user of the recommendation through the user interface. The number of fit analyses can be performed either during wakefulness prior to sleep onset or after sleep onset, and the monitoring of the sleep stages is based on physiological signals sensed by the number of EEG sensors.
These and other objects, features, and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention.
As used herein, the singular form of “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
The terms “bio signal” and “physiological signal” are used interchangeably herein.
As used herein, the statement that two or more parts or components are “coupled” shall mean that the parts are joined or operate together either directly or indirectly, i.e., through one or more intermediate parts or components, so long as a link occurs. The statement that two or more parts or components are “removably coupled” shall mean that the individual components are structured to be coupled to one another and can be decoupled from one another without comprising the structural integrity of either individual component.
As used herein, the term “number” shall mean one or an integer greater than one (i.e., a plurality).
As used herein, the term “controller” shall mean a number of programmable analog and/or digital devices (including an associated memory part or portion) that can store, retrieve, execute and process data (e.g., software routines and/or information used by such routines), including, without limitation, a field programmable gate array (FPGA), a complex programmable logic device (CPLD), a programmable system on a chip (PSOC), an application specific integrated circuit (ASIC), a microprocessor, a microcontroller, a programmable logic controller, or any other suitable processing device or apparatus. The memory portion can be any one or more of a variety of types of internal and/or external storage media such as, without limitation, RAM, ROM, EPROM(s), EEPROM(s), FLASH, and the like that provide a storage register, i.e., a non-transitory machine readable medium, for data and program code storage such as in the fashion of an internal storage area of a computer, and can be volatile memory or nonvolatile memory.
As used herein, the term “machine learning model” shall mean a software system that develops and builds a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so, including, without limitation, a computer software system that develops that has been trained to recognize patterns from a set of training data, and subsequently develops algorithms to recognize patterns from the training data set in other data sets.
Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein.
The disclosed concept, as described in greater detail herein in connection with various particular exemplary embodiments, provides automated methods and systems for assisting a user of a wearable biosignal monitoring device (also referred to herein as a “biomonitoring device”) in adjusting the device to ensure that the device is properly fit and that the bio sensors of the device are properly positioned after the user has put the device on. Biosignal monitoring devices generally utilize sensors such as electrodes to detect physiological signal activity, and it will be appreciated that good skin contact and low electrode impedance are essential for optimizing the signal quality of physiological activity detected by electrodes. Research and medical grade systems for measuring physiological activity usually require a preparation step consisting of skin cleaning, electrode set up, and conductive gel application in order to ensure good skin contact and low electrode impedance. With respect to consumer grade systems intended for at-home use though, such elaborate and time-consuming set up procedures are usually undesirable from a user perspective. However, proper device placement and good fit are just as important for ensuring the quality of the measured signal, therapy delivery (where applicable), and user satisfaction in consumer grade applications are they are in research and medical grade applications. Thus, the methods and systems of the present invention facilitate effective measurement of physiological signals in consumer grade wearable biosignal monitoring devices by determining during device setup if the device is properly fit and if the sensors are properly positioned, and, upon detection of improper fit and/or positioning, guiding the user in adjusting the positioning and/or fit of the device as appropriate.
Referring now to
For the sake of brevity, because wearable biosignal monitoring devices are available in several different forms (wrist-worn devices, respiration belts, smart adhesive patches, finger sensors, etc.), the methods of the present invention are discussed herein primarily using the sleep headband 2 pictured in
Continuing to refer to
In an exemplary embodiment of sleep headband 2, bio sensors 5 comprise EEG sensors integrated into either or both of the forehead band 21 and scalp band 22. Sensory actuators 6 can comprise, for example and without limitation, auditory, visual, and/or tactile stimulators. In exemplary embodiments of sleep headband 2, sensory actuators 6 comprise at least auditory stimulators such as speakers, in order to perform the primary device function of delivering therapeutic tones during deep sleep for the purpose of enhancing slow-wave brain activity. However, it should be noted that such auditory sensory actuators 6 can provide stimulation during wakefulness as part of the disclosed guided device setup, detailed further herein with respect to Method 100 depicted in
Said auditory sensory actuators 6 of sleep headband 2 may comprise, for example and without limitation, a bone conduction speaker integrated into the forehead band 21 or ear speakers integrated into the ear region 7. Sensory actuators 6 of sleep headband 2 may also additionally comprise visual and/or tactile stimulators, the uses of which are also detailed further later herein with respect to
As schematically depicted in
In an exemplary embodiment, controller 8 is integrated within biomonitoring device 2 and comprises both signal analysis unit 3 and communication module 9. Accordingly, in
As indicated in
Referring now to
The distinction between bio event detector 11 and sensory event actuator 11′ arises from the type of event that each detects. Bio event detector 11 detects spontaneous physiological signals in user U. In contrast, sensory event actuator 11′ generates and delivers stimuli to user U via sensory actuators 6 for the purpose of eliciting a physiological response in user U, and subsequently transmits information about the timing of the stimuli delivery to signal analysis unit 3. Both bio event detector 11 and sensory event actuator 11′ are detailed further herein below. It should be further noted that monitoring system 1 comprises bio event detector 11 in some exemplary embodiments, and alternatively comprises sensory event actuator 11′ in other exemplary embodiments. Monitoring system 1 can, however, comprise both a bio event detector 11 and sensory event actuator 11′ without departing from the scope of the disclosed concept.
Bio event detector 11 receives input from bio sensors 5 and is configured to execute a number of event detection algorithms in order to detect spontaneous physiological events expected to be present in biosignals typically obtained from the location in which a given bio sensor 5 is positioned. Said spontaneous physiological events can include commonly detected signal artefacts. Event detection algorithms can be used to detect many different types of spontaneous physiological signals, including eye blinks, heart rate, muscle movement, etc. In one non-limiting illustrative example, eye blinks are artefacts commonly detected in EEG signals, particularly scalp EEG signals, and bio event detector 11 can be configured to detect eye blink signal artefact data from a scalp EEG signal. It is expected that scalp EEG sensors integrated into a biomonitoring device 2, such as scalp EEG electrode bio sensors 5 that can be integrated into scalp band 22 of sleep headband 2, would produce EEG signals with well-defined eye blink artefact data.
Bio event detector 11 transmits each detected spontaneous event signal as input to signal analysis unit 3, and signal analysis unit 3 then compares the spontaneous event signal to normative signal data considered typical for the particular event type detected in order to determine if the signal sensed by bio sensor 5 is of satisfactory quality, as detailed further herein with respect to method 100 depicted in
Still referring to bio event detector 11, in
Referring again to
Continuing with the same example, the mobile phone user platform app can be used to trigger a stimulus to be provided by the sensory actuator(s) 6 integrated within the headband 2. For example, the app can actuate an LED integrated within headband 2 to produce flashing light. Another non-limiting example in a different biomonitoring device 2 is using the app to produce a vibration in a wrist-worn device. Alternatively, the mobile phone itself can produce the stimulation, for example and without limitation by producing a light flash on the phone screen for visual stimulation, or by producing a vibration through the phone for haptic stimulation. It will be appreciated that in this case, the mobile phone comprises not only user interface 4, but sensory event actuator 11′ and sensory actuator 6 as well. Even though visual and haptic stimulation are not part of the therapy delivered by the sleep headband 2 when user U is sleeping, the responses of user U to the visual or haptic stimulation sensed by the bio sensors 5 can still be compared to normative data stored in signal analysis unit 3 to determine if bio sensors 5 have sufficient contact and are well-positioned. While the delivered stimulation does not have to be related to the primary function of biomonitoring device 2 in order to produce a useful fit analysis, sensory event actuator 11′ can be especially useful if one of the primary functions of device 2 is to deliver a particular type of stimulus. In one non-limiting example, in the case of sleep headband 2, sensory event actuator 11′ can cause an auditory sensory actuator 6 to play a number of auditory tones in order to determine during wakefulness whether user U will respond meaningfully during sleep to delivery of therapeutic tones based on the current device fit and positioning.
In an exemplary embodiment, one specific type of user response that signal analysis unit 3 defines is an event related potential (ERP). Both external and internal events are known to elicit stereotypical brain responses, with each response comprising a time and phase locked to event onset. Such time- and phase-locked event responses are ERPs. Specifically, ERPs are voltage fluctuations arising from summed postsynaptic potentials of large neural populations firing synchronously during processing of information. ERPs are calculated as the average waveform over a number of events, and the amplitude and latency of the successive peaks in the ERP waveform can be used to determine the time course of information processing in the brain. Stimulation of increasing intensity (volume, in the case of auditory stimulation) produces reliable increase in the ERP amplitude. Biomonitoring devices 2 whose bio sensors 5 include scalp EEG sensors (including, for example and without limitation, sleep headband 2) are particularly good candidates for using a sensory event actuator 11′ to determine ERP, as the high time resolution of scalp recorded EEG generally provides a good basis for studying the brain responses to external or internal events.
Both the spontaneous physiological events detected by bio event detector 11 and the user response to external stimulus events actuated by sensory event actuator 11′ constitute “fit events”. It should be noted that evaluation of fit events detected by fit event detectors 11,11′ is an advantageous feature of the present invention, as the fit event evaluation performed by signal analysis unit 3 ensures that meaningful physiological responses that are expected to occur in the body when biomonitoring device 2 is being used for its primary purpose are detectable from the same types of physiological signals detected during device setup, prior to using the device for its primary use. For example and without limitation, the primary purpose of sleep headband 2 is to detect sleep stages of user U by monitoring the EEG bio sensor 5 signals with controller 8, and to deliver soft non-arousing tones to user U during deep sleep in order to enhance slow-wave brain activity. When performing device setup during wakefulness, the same EEG bio sensors 5 that controller 8 uses to detect sleep stages are also used to detect fit events with fit event detector 11,11′ (e.g. to detect eye blink artefact in the EEG signals in the case of event detector 11, or determine ERPs in the case of sensory event actuator 11′ actuating sensory actuators 6 to deliver external stimulation). Determining that eye blink signal artefact or ERPs are within a normative range during wakefulness increases the likelihood that EEG bio sensors 5 will properly determine the sleep stage of user U during sleep and that the delivered therapeutic auditory tones will be effective in enhancing slow-wave activity during deep sleep.
In additional exemplary embodiments, controller 8 may optionally comprise a signal quality parameter module 12, configured to determine various additional attributes of the signals detected by bio event detector 11 or elicited by sensory event actuator 11′ and provide those attributes as additional inputs to signal analysis unit 3 in order to further refine the analysis of whether the positioning of bio sensors 5 is satisfactory. Such signal attributes can include, for example and without limitation, impedance, signal power, noise, and signal-to-noise ratio (SNR).
Referring now to
At step 101 of method 100, after user U has put on biomonitoring device 2, controller 8 detects a fit event using fit event detector 11, 11′ as previously detailed with respect to
Still referring to step 101 in the context of sleep headband 2, detecting fit events during both wakefulness and sleep provides useful information in a scenario where user U sets up sleep headband 2 properly but the device 2 shifts in the night for some reason and consequently shifts the bone conduction speaker sensory actuator 6. In some exemplary embodiments, detection of fit events during wakefulness is omitted altogether and fit feedback is only provided after the sleep session based on the sensed responses of user U to the therapeutic auditory tones delivered during sleep. In a scenario where fit feedback is provided to user U based on responses to therapeutic tones delivered during sleep, regardless of whether or not fit feedback was also provided to user U during wakefulness, after user U wakes up, the monitoring system 1 can provide recommendations via user interface 4 for correcting the fit and/or positioning of sleep headband 2 in order to avoid the recurrence of adverse fit or positioning issues in the future. For example and without limitation, the user interface 4 can be configured to explain to user U that the bone conduction speaker was not properly positioned or connected, and provide tailored advice for avoiding recurrence of the issue in the future.
At step 102, a user response based on either the spontaneous physiological signal detected at step 101 (when monitoring system 1 includes bio event detector 11) or the response of user U to the stimuli delivered at step 101 (when monitoring system 1 includes signal actuator 11′) is defined. Specifically, signal analysis unit 3 quantifies various features of the measurements taken by bio sensors 5 during the fit event detection at step 101. As previously stated, in the exemplary embodiment wherein monitoring system 1 includes bio event detector 11, signal analysis unit 3 uses a response event detection algorithm, (e.g. blink detector, heart rate detector, muscle movement detector, etc.), to detect the presence of a corresponding specific physiological signal. In the exemplary embodiment wherein monitoring system 1 includes sensory event actuator 11′, signal analysis unit 3 does not need to include an event detection algorithm, as information regarding the timing of the stimuli is provided directly to signal analysis unit 3 by the sensory event actuator 11′. In either embodiment, i.e. whether monitoring system includes bio event detector 11 or sensory event actuator 11′, the physiological response locked to the fit event timing (the fit event being the spontaneously occurring physiological signal in user U or the specific external stimulation event determined to have elicited a specific physiological signal in user U) is analyzed either as a response to a single event or as an average response over multiple events. It should be noted that if no event is detected where one was expected (e.g. eye blinks before sleep onset while wearing a sleep headband 2, an ERP produced in response to a sensory stimuli), the lack of response is considered an indication of device fit/signal quality issues.
Still referring to step 102, it should be noted that, generally, measuring responses over a period of time, and measuring multiple types of spontaneous physiological signals (when bio event detector 11 is used) or measuring responses to multiple types of stimuli (when sensory event actuator 11′ is used), will produce a better estimation of device fit/positioning, in comparison to measuring a response for a single event or measuring a response to a single stimulus. Accordingly, in an exemplary embodiment, the user response can be defined as the average response over all stimulation events, which can be calculated as the average waveform in a window of predefined duration around event onset. For example and without limitation, in the context of an auditory stimulus, the time of tone onset is identified, and a cutoff time of 1 second before and 1 second after tone onset is implemented in order to define a 2-second window of time centered on event onset (i.e. tone onset) in order to identify the physiological response of user U during that 2-second window of time. Continuing with the same example, if 1,500 tones are played, then 1,500 2-second long physiological response waveforms are averaged to produce one 2-second long average response waveform, and the average response waveform constitutes the defined user response. After the user response is defined, relevant features are extracted. The features can include, for example and without limitation: the amplitude of first/second/etc. positive or negative peaks in the waveform; peak to peak distance; average amplitude or area under the curve in a predefined intervals; and others. To enhance signal to noise ratio, various filtering or fitting procedures can be applied before extracting the features. Additionally, single trial waveforms containing extreme values can be identified as containing artifacts and omitted prior to calculating the averaged response using absolute or relative thresholds (e.g. extreme values such as waveforms with an amplitude above 200 µV, or an amplitude exceeding the mean EEG amplitude by, e.g. 2 standard deviations, or an amplitude exceeding the 98th percentile of all values).
Continuing to refer to step 102, the processing and quantification of either the spontaneous biosignal detected in user U or the response of user U to the stimuli delivered at step 101 is implemented in a feature extraction process 120 depicted in
Returning to
Still referring to step 103, it will be appreciated that there are several statistical tests designed to determine the difference between the mean values of two or more groups (in the present context, the values being compared are the mean values for the data of user U and the mean values for the reference data), and that each statistical test has particular requirements that need to be met in order to be applied to the data. In one non-limiting example, for a t-test or ANOVA (analysis of variance): the tested variables must be continuous (interval/ratio) and approximately normally distributed, the samples must be independent of one another, and the samples must not contain outliers. In another non-limiting example, non-parametric tests, such as a Wilcoxon signed rank test, make no assumptions about the probability distributions of the tested variables but require the data to be paired and to come from the same population and each pair must be chosen randomly and independently. Assuming that necessary requirements are met, the two probability distributions (of the tested variable and the baseline variable, the tested variable being the response of user U quantified at step 102 and the baseline variable being the reference data compiled from a group of subjects) are compared and a probability of rejecting a hypothesis that there is no difference between the two means when that is the case (the null hypothesis) is calculated. This probability, called p-value, is compared to a pre-chosen probability, called significance level (usually α = 0.05). If the calculated p-value is smaller than the chosen significance level, then the null hypothesis is rejected and we say that there is a difference between the tested value and the baseline. To counteract the increased chance of rejecting the null hypothesis when multiple tests are carried out, the value of α can be reduced proportionally to the number of tests carried out via a procedure known in statistics as Bonferroni correction. Such correction is, however, simply equivalent to selecting another (stricter) significance level.
After the differences between the mean values of the responses of user U and the mean values for the reference data have been determined, the goodness of device fit is then estimated as good (either on a binary or a continuous scale), if the extracted features from the current session show either:
Whether monitoring system 1 includes bio event detector 11 or signal actuator 11′, if method 100 is executed prior to the upcoming session of biomonitoring device 2 use and if signal analysis unit 3 determines that the user response is within the normal range after performing step 103, process 100 proceeds to step 105 where process 100 is deemed complete and user interface 4 indicates to user P that the device is deemed to be properly fit and positioned for the upcoming monitoring session. If, however, signal analysis unit 3 determines after performing step 103 that the user U data is outside of the normal range, process 100 proceeds to step 104 wherein a notification is delivered to user U via user interface 4 to inform user U that the fit and/or positioning of biomonitoring device 2 needs to be adjusted. Notifications provided to user U at steps 104 and 105 can include, for example and without limitation, a written notification displayed on a screen of the user interface 4, a light included on the biomonitoring device 2 illuminating green at step 105 to indicate good fit while instead illuminating red at step 104 to indicate poor fit, or a “happy” tone being played at step 105 to indicate good device fit.
Continuing to refer to step 104, in some exemplary embodiments, user interface 4 provides specific recommendations or instructions to user U for adjusting the fit and/or positioning of the biomonitoring device 2. For example and without limitation, user interface 4 may comprise a mobile app with clear instructions that are narrated and/or pictured for user U. In the case of sleep headband 2, such instructions provided to user U may pertain to tightening the fit adjuster 10, or sliding sleep headband 2 in order to adjust the placement of forehead band 21 and scalp band 22 relative to the head of user U. Specifically, user U may be directly to slide sleep headband 2 slightly up or down in order to avoid trapping hair between the bio sensors 5 and the skin, or to slide sleep headband 2 slightly left or right in order to avoid contact between the bio sensors 5 and the temples, In another non-limiting example where biomonitoring device 2 comprises an adhesive patch such as the ones used for EKG monitoring, the recommendations or instructions provided to user U at step 104 can include directing user U to move the patch to a different location on the body in order to improve the quality of the signal sensed by the patch.
Continuing to refer to step 104, in additional exemplary embodiments, biomonitoring device 2 can comprise predefined fit adjustment markers, and user U can be guided through a calibration procedure. In an illustrative example using sleep headband 2, such a calibration procedure can comprise instructing user U to tightly fasten the fit adjuster 10 and subsequently loosen fit adjuster 10 incrementally in steps according to the predefined markers. In other exemplary embodiments, user interface 4 can be implemented at least partially directly on biomonitoring device 2 as a number of sound, vibration, or visual indicators. For example and without limitation, a light can be included on biomonitoring device 2 that illuminates red if the bio signals sensed during device setup are unsatisfactory and illuminates green if the bio signals sensed during device are satisfactory, or a speaker can emit a “happy” sound or vibration if the device setup is deemed to be satisfactory.
Furthermore, other exemplary embodiments of method 100 can include a step 104A, in which user U can provide additional feedback about the user’s perceived comfort to controller 8 through user interface 4, or controller 8 itself may provide additional non-biosignal related feedback, and wherein such additional feedback is also used (in addition to the biosignal data) by signal analysis unit 3 to form fit/positioning adjustment recommendations for user U at step 104. Non-limiting examples of the additional non-bio signal feedback that can be provided by controller 8 include the attributes previously enumerated with respect to signal quality parameter module 12 shown in
Referring now to
Continuing to refer to
Still referring to
The methods and systems disclosed herein offer an important advantage over simple signal quality measures such as impedance, noise power, signal-to-noise ratio (SNR), and others by evaluating the quality of the meaningful physiological responses of the body sensed by a biosignal monitoring device, wherein said physiological responses are used during execution of the primary function of the device, prior to the device being used for its primary purpose. Stereotypical brain responses to an external or internal stimuli, such as ERP, or muscle activity, such as eye blinks or eye movement, are very convenient markers, and the systems and methods disclosed herein advantageously utilize the fact that the shape and amplitude of these EEG phenomena are well studied and can be easily modeled for a particular biosignal monitoring system.
Another related advantage offered by the methods and systems disclosed herein is providing, for those biomonitoring systems 1 that include actuators 6, a combined estimate of both sensor 5 and actuator 6 placement, as having an estimate of only sensor 5 placement or only actuator 6 placement may not provide an accurate depiction of whether the overall monitoring system will function properly. For example, as previously stated, a dry electrode that is just touching the skin can have a very low noise level, but the lack of pressure will cause the dynamic range of the signal to be low. In another example, a SNR defined over two ranges that fluctuate in the same direction in case of a bad signal can incorrectly suggest that the quality of device fit is high when it is in fact low. Furthermore, an actuator 6 (e.g. for sleep headband 2, a bone conduction speaker used to deliver deep sleep enhancing auditory tones) that does not make proper contact will not affect the sensors’ impedance at all but will not deliver the anticipated effect. The current invention looks at the meaningful system-operation-relevant physiological phenomena and judges the placement of both sensors 5 and actuators 6, and ensures the proper functioning of a complete biosignal monitoring system by guiding a user to make any necessary adjustments to the fit and placement of said sensors and actuators prior to using the monitoring system.
As previously stated with respect to
In another non-limiting example, in a smart adhesive patch with biosensors, the methods disclosed herein can be used to ensure placement of the patch on the correct body area, or to ensure that the sensor has been correctly adhered to the skin (for instance, when proper functioning of the patch necessitates blocking all ambient light with the adhesive area), and the user can be instructed to press the adhesive to the skin more firmly if necessary. In a further non-limiting example, in an application where device fit is expected to dynamically change over the usage of a device, a measured ERP response can also be used to judge if currently measured signals should be used. E.g. in PPG applications, measuring average heart rate can be done reliably under bad device wearing conditions and/or while a user is in motion, while detection of individual heart beats require proper device fit, among other conditions.
In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” or “including” does not omit the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word “a” or “an” preceding an element does not omit the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination.
Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.
This patent application claims the benefit of U.S. Application Serial No. 63/294,971 filed Dec. 30, 2021, the contents of which are herein incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 63294971 | Dec 2021 | US |
