Device personalizaton

Abstract

Presented herein are techniques for providing automated operational parameter (e.g., stimulation level) changes in a medical or hearing device. A trajectory for adjusting one or more operational parameters associated with a medical or hearing device is calculated based on information associated with the medical or hearing device and information associated with a recipient of the medical or hearing device. The one or more levels are automatically adjusted based on the trajectory until one or more target stimulation levels are reached.

Description

DEVICE PERSONAEIZATONBACKGROUNDField of the Invention[oooi] The present invention relates generally to programming of a device for a recipient.Related Art

[0002] Medical devices have provided a wide range of therapeutic benefits to recipients over recent decades. Medical devices can include internal or implantable components / devices, external or wearable components / devices, or combinations thereof (e.g., a device having an external component communicating with an implantable component). Medical devices, such as traditional hearing aids, partially or fully-implantable hearing prostheses (e.g., bone conduction devices, mechanical stimulators, cochlear implants, etc.), pacemakers, defibrillators, functional electrical stimulation devices, and other medical devices, have been successful in performing lifesaving and / or lifestyle enhancement functions and / or recipient monitoring for a number of years.

[0003] The types of medical devices and the ranges of functions performed thereby have increased over the years. For example, many medical devices, sometimes referred to as “implantable medical devices,” now often include one or more instruments, apparatus, sensors, processors, controllers or other functional mechanical or electrical components that are permanently or temporarily implanted in a recipient. These functional devices are typically used to diagnose, prevent, monitor, treat, or manage a disease / injury or symptom thereof, or to investigate, replace or modify the anatomy or a physiological process. Many of these functional devices utilize power and / or data received from external devices that are part of, or operate in conjunction with, implantable components.SUMMARY

[0004] In one aspect, a method is provided. The method comprises: calculating a trajectory for adjusting one or more stimulation levels associated with a hearing device based on information associated with the hearing device and information associated with a recipient of the hearing device; and automatically adjusting the one or more stimulation levels based on the trajectory until one or more target stimulation levels are reached.

[0005] In another aspect, a medical device is provided. The medical device comprises: a trajectory calculation module configured to calculate a trajectory for adjusting a parameterassociated with the medical device based on information associated with the medical device and information associated with a recipient of the medical device; and one or more processors for adjusting the parameter based on the trajectory until a target parameter is reached.

[0006] In yet another aspect, one or more non-transitory computer readable storage media comprising instructions that, when executed by a processor, cause the processor to: receive a trajectory for increasing levels of a parameter associated with a device over time, wherein the trajectory is calculated based on information associated with the device and information associated with a recipient of the device; and increase the levels of the parameter based on the trajectory until a target level of the parameter is reached.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Embodiments of the present invention are described herein in conjunction with the accompanying drawings, in which:

[0008] FIG. 1A is a schematic diagram illustrating a cochlear implant system with which aspects of the techniques presented herein can be implemented;

[0009] FIG. IB is a side view of a recipient wearing a sound processing unit of the cochlear implant system of FIG. 1A;[ooio] FIG. 1C is a schematic view of components of the cochlear implant system of FIG. 1 A;[ooii] FIG. ID is a block diagram of the cochlear implant system of FIG. 1A;

[0012] FIG. IE is a schematic diagram illustrating a computing device with which aspects of the techniques presented herein can be implemented;

[0013] FIG. 2 is a diagram illustrating a machine learning model that is used to determine a personalized acclimatization trajectory for a recipient of a hearing device, according to techniques presented herein;

[0014] FIG. 3 is a graph illustrating exemplary acclimatization trajectories with different parameters, according to techniques described herein;

[0015] FIG. 4A is a graph illustrating an initial trajectory, a trajectory modified to decrease the time to maximum, and a trajectory modified to increase the time to maximum, according to techniques described herein;

[0016] FIG. 4B is a graph illustrating an initial trajectory, a trajectory modified to increase the stimulation level maximum, and a trajectory modified to decrease the stimulation level maximum, according to techniques described herein;

[0017] FIG. 4C is a graph illustrating an initial trajectory, a modified trajectory with a positive offset applied to the current stimulation level and stimulation level maximum, and a modified trajectory with a negative offset applied to the current level and stimulation level maximum, according to techniques described herein;

[0018] FIG. 4D is a graph illustrating an initial traj ectory and a modified traj ectory with a pause on stimulation level increases and a subsequent resumption, according to techniques described herein;

[0019] FIG. 5 is a flowchart of a method for providing automated stimulation level changes, according to techniques described herein;

[0020] FIG. 6 is a schematic diagram illustrating a vestibular stimulator system with which aspects of the techniques presented herein can be implemented; and

[0021] FIG. 7 is a schematic diagram illustrating a retinal prosthesis system with which aspects of the techniques presented herein can be implemented.DETAILED DESCRIPTIONPresented herein are techniques for providing automated changes to programming of a device, such a medical device or hearing device, associated with (e.g., worn by or implanted in) a recipient / user. More specifically, aspects presented herein optimize changes in operational parameters of a device (e.g., stimulation parameters, such as dynamic range) over time in accordance with a “personalized acclimatization trajectory.” In certain examples, machine learning is used to calculate the personalized acclimatization trajectory based on relevant recipient and device factors (e.g., loudness tolerance, etiology, stimulation mode). The personalized acclimatization trajectory can be applied via automatic adjustments to parameters (e.g., stimulation levels over time), and the personalized acclimatization trajectory based on device usage (e.g., wear time), user input (e.g., Ecological Momentary Assessment), biometrics (e.g., impedances), and / or automatically detected events (e.g., startle reaction to loud noises). Feedback on wear time and acclimatization trajectory will be provided to recipients / users andprofessionals to reinforce the importance of device use for delivering optimal hearing outcomes.

[0022] In certain aspects, embodiments presented herein perform scaling of stimulation levels over time without user input to increase the dynamic range and maximize speech perception outcomes. The comfortability of the dynamic range increases is verified and the personalized acclimatization trajectory is adjusted as appropriate. As such, embodiments described herein can reduce the need for clinic visits for minor programming adjustments, thereby reducing recipient and clinic burden while automatically increasing the recipient’s dynamic range or other parameters without relying on user input. Freeing up clinic time can allow professionals to care for more recipients while maintaining excellence in care and delivering optimal recipient outcomes.

[0023] There are a number of different types of devices in / with which embodiments of the present invention can be implemented. Merely for ease of description, the techniques presented herein are primarily described with reference to a specific device in the form of a cochlear implant system. However, it is to be appreciated that the techniques presented herein can also be partially or fully implemented by any of a number of different types of devices, including consumer electronic device (e.g., mobile phones), wearable devices (e.g., smartwatches), hearing devices, implantable medical devices, wearable devices, etc. consumer electronic devices, wearable devices (e.g., smart watches, etc.), etc. As used herein, the term “hearing device” is to be broadly construed as any device that acts on an actual or potential auditory perception of an individual, including to improve perception of sound signals, to reduce perception of sound signals, etc. In particular, a hearing device can deliver sound signals to a recipient in any form, including in the form of acoustical stimulation, mechanical stimulation, electrical stimulation, etc., and / or can operate to suppress all or some sound signals. As such, a hearing device can be a device for use by a hearing -impaired person (e.g., hearing aids, middle ear auditory prostheses, bone conduction devices, direct acoustic stimulators, electro-acoustic hearing prostheses, auditory brainstem stimulators, bimodal hearing prostheses, bilateral hearing prostheses, dedicated tinnitus therapy devices, tinnitus therapy device systems, combinations or variations thereof, etc.), a device for use by a person with normal hearing (e.g., consumer devices that provide audio streaming, consumer headphones, earphones, and other listening devices), a hearing protection device, etc. In other examples, the techniques presented herein can be implemented by, or used in conjunction with, various implantable medical devices, such as vestibular devices (e.g., vestibular implants),visual devices (i.e., bionic eyes), sensors, pacemakers, drug delivery systems, defibrillators, functional electrical stimulation devices, catheters, seizure devices (e.g., devices for monitoring and / or treating epileptic events), sleep apnea devices, electroporation devices, etc.

[0024] FIGs. 1A-1D illustrates an example cochlear implant system 102 with which aspects of the techniques presented herein can be implemented. The cochlear implant system 102 comprises an external component 104 that is configured to be directly or indirectly attached to the body of the recipient, and an intemal / implantable component 112 that is configured to be implanted in or worn on the head of the recipient. In the examples of FIGs. 1A-1D, the implantable component 112 is sometimes referred to as a “cochlear implant.” FIG. 1A illustrates the cochlear implant 112 implanted in the head 154 of a recipient, while FIG. IB is a schematic drawing of the external component 104 worn on the head 154 of the recipient. FIG. 1C is another schematic view of the cochlear implant system 102, while FIG. ID illustrates further details of the cochlear implant system 102. For ease of description, FIGs. 1A-1D will generally be described together.

[0025] In the examples of FIGs. 1A-1D, the external component 104 comprises a sound processing unit 106, an external coil 108, and generally, a magnet fixed relative to the external coil 108. The cochlear implant 112 includes an implantable coil 114, an implant body 134, and an elongate stimulating assembly 116 configured to be implanted in the recipient’s cochlea. In one example, the sound processing unit 106 is an off-the-ear (OTE) sound processing unit, sometimes referred to herein as an OTE component, that is configured to send data and power to the implantable component 112. In general, an OTE sound processing unit is a component having a generally cylindrically shaped housing 111 and which is configured to be magnetically coupled to the recipient’s head 154 (e.g., includes an integrated external magnet 150 configured to be magnetically coupled to an intemal / implantable magnet 152 in the implantable component 112). The OTE sound processing unit 106 also includes an integrated external (headpiece) coil 108 (the external coil 108) that is configured to be inductively coupled to the implantable coil 114.

[0026] It is to be appreciated that the OTE sound processing unit 106 is merely illustrative of the external devices that could operate with implantable component 112. For example, in alternative examples, the external component 104 can comprise a behind-the-ear (BTE) sound processing unit configured to be attached to, and worn adjacent to, the recipient’s ear. In general, a BTE sound processing unit comprises a housing that is shaped to be worn on the outer ear of the recipient and is connected to the separate external coil assembly via a cable,where the external coil assembly is configured to be magnetically and inductively coupled to the implantable coil 114. It is also to be appreciated that alternative external components could be located in the recipient’s ear canal, worn on the body, etc.

[0027] Although the cochlear implant system 102 includes the sound processing unit 106 and the cochlear implant 112, as described below, the cochlear implant 112 can operate independently from the sound processing unit 106, for at least a period, to stimulate the recipient. For example, the cochlear implant 112 can operate in a first general mode, sometimes referred to as an “external hearing mode,” in which the sound processing unit 106 captures sound signals which are then used as the basis for delivering stimulation signals to the recipient. The cochlear implant 112 can also operate in a second general mode, sometimes referred as an “invisible hearing” mode, in which the sound processing unit 106 is unable to provide sound signals to the cochlear implant 112 (e.g., the sound processing unit 106 is not present, the sound processing unit 106 is powered-off, the sound processing unit 106 is malfunctioning, etc.). As such, in the invisible hearing mode, the cochlear implant 112 captures sound signals itself via implantable sound sensors and then uses those sound signals as the basis for delivering stimulation signals to the recipient. Further details regarding operation of the cochlear implant 112 in the external hearing mode are provided below, followed by details regarding operation of the cochlear implant 112 in the invisible hearing mode. It is to be appreciated that reference to the external hearing mode and the invisible hearing mode is merely illustrative and that the cochlear implant 112 could also operate in alternative modes.

[0028] In FIGs. 1A and 1C, the cochlear implant system 102 is shown with an external device 110, configured to implement aspects of the techniques presented. The external device 110, which is shown in greater detail in FIG, 1 E, is a computing device, such as a personal computer (e.g., laptop, desktop, tablet), fitting system, a mobile phone (e.g., smartphone), remote control unit, etc. The external device 110 and the cochlear implant system 102 (e.g., sound processing unit 106 or the cochlear implant 112) wirelessly communicate via a bi-directional communication link 126. The bi-directional communication link 126 can comprise, for example, a short-range communication, such as Bluetooth link, Bluetooth Low Energy (BLE) link, a proprietary link, etc.

[0029] Returning to the example of FIGs. 1A-1D, the sound processing unit 106 of the external component 104 also comprises one or more input devices configured to capture and / or receive input signals (e.g., sound or data signals) at the sound processing unit 106. The one or more input devices include, for example, one or more sound input devices 118 (e.g., one or moreexternal microphones, audio input ports, telecoils, etc.), one or more auxiliary input devices 128 (e.g., audio ports, such as a Direct Audio Input (DAI), data ports, such as a Universal Serial Bus (USB) port, cable port, etc.), and a short-range wireless transmitter / receiver (wireless transceiver) 120 (e.g., for communication with the external device 110), each located in, on or near the sound processing unit 106. However, it is to be appreciated that one or more input devices can include additional types of input devices and / or less input devices (e.g., the short- range wireless transceiver 120 and / or one or more auxiliary input devices 128 could be omitted).

[0030] The sound processing unit 106 also comprises the external coil 108, a charging coil, a closely-coupled radio frequency transmitter / receiver (RF transceiver) 122, at least one rechargeable battery 132, and an external sound processing module 124. The external sound processing module 124 can be configured to perform a number of operations which are represented in FIG. ID by an acclimatization trajectory module 131, a sound processor 133, and a modification detection module 135. Each of the acclimatization trajectory module 131, the sound processor 133, and the modification detection module 135 can be formed by one or more processors (e.g., one or more Digital Signal Processors (DSPs), one or more uC cores, etc.), firmware, software, etc. arranged to perform operations described herein. That is, the acclimatization trajectory module 131, the sound processor 133, and the modification detection module 135 can each be implemented as firmware elements, partially or fully implemented with digital logic gates in one or more application-specific integrated circuits (ASICs), partially or fully in software, etc. Although FIG. ID illustrates the acclimatization trajectory module 131, a sound processor 133, and a modification detection module 135 as being implemented / performed at the external sound processing module 124, it is to be appreciated that these elements (e.g., functional operations) could also or alternatively be implemented / performed as part of the implantable sound processing module 158, as part of the external device 110, etc.

[0031] Returning to the example of FIGs. 1A-1D, the implantable component 112 comprises an implant body (main module) 134, a lead region 136, and the intra-cochlear stimulating assembly 116, all configured to be implanted under the skin (tissue) 115 of the recipient. The implant body 134 generally comprises a hermetically-sealed housing 138 that includes, in certain examples, at least one power source 125 (e.g., one or more batteries, one or more capacitors, etc.) 125, in which RF interface circuitry 140 and a stimulator unit 142 are disposed. The implant body 134 also includes the intemal / implantable coil 114 that is generally externalto the housing 138, but which is connected to the RF interface circuitry 140 via a hermetic feedthrough (not shown in FIG. ID).

[0032] As noted, stimulating assembly 116 is configured to be at least partially implanted in the recipient’s cochlea. Stimulating assembly 116 includes a plurality of longitudinally spaced intra-cochlear electrical stimulating contacts (electrodes) 144 that collectively form a contact array (electrode array) 146 for delivery of electrical stimulation (current) to the recipient’s cochlea. Stimulating assembly 116 extends through an opening in the recipient’s cochlea (e.g., cochleostomy, the round window, etc.) and has a proximal end connected to stimulator unit 142 via lead region 136 and a hermetic feedthrough (not shown in FIG. ID). Lead region 136 includes a plurality of conductors (wires) that electrically couple the electrodes 144 to the stimulator unit 142. The implantable component 112 also includes an electrode outside of the cochlea, sometimes referred to as the extra-cochlear electrode (ECE) 139.

[0033] As noted, the cochlear implant system 102 includes the external coil 108 and the implantable coil 114. The external magnet 150 is fixed relative to the external coil 108 and the intemal / implantable magnet 152 is fixed relative to the implantable coil 114. The external magnet 150 and the intemal / implantable magnet 152 fixed relative to the external coil 108 and the intemal / implantable coil 114, respectively, facilitate the operational alignment of the external coil 108 with the implantable coil 114. This operational alignment of the coils enables the external component 104 to transmit data and power to the implantable component 112 via a closely-coupled wireless link 148 formed between the external coil 108 with the implantable coil 114. In certain examples, the closely-coupled wireless link 148 is a radio frequency (RF) link. However, various other types of energy transfer, such as infrared (IR), electromagnetic, capacitive and inductive transfer, can be used to transfer the power and / or data from an external component to an implantable component and, as such, FIG. ID illustrates only one example arrangement.

[0034] As noted above, sound processing unit 106 includes the external sound processing module 124. The external sound processing module 124 is configured to process the received input audio signals (received at one or more of the input devices, such as sound input devices 118 and / or auxiliary input devices 128), and convert the received input audio signals into output control signals for use in stimulating a first ear of a recipient or user (i.e., the external sound processing module 124 is configured to perform sound processing on input signals received at the sound processing unit 106). Stated differently, the one or more processors (e.g., processing element(s) implementing firmware, software, etc.) in the external sound processing module124 are configured to execute sound processing logic in memory to convert the received input audio signals into output control signals (stimulation signals) that represent electrical stimulation for delivery to the recipient.

[0035] As noted, FIG. ID illustrates an embodiment in which the external sound processing module 124 in the sound processing unit 106 generates the output control signals. In an alternative embodiment, the sound processing unit 106 can send less processed information (e.g., audio data) to the implantable component 112 and the sound processing operations (e.g., conversion of input sounds to output control signals 156) can be performed by a processor within the implantable component 112.

[0036] In FIG. ID, according to an example embodiment, output control signals (stimulation signals) are provided to the RF transceiver 122, which transcutaneously transfers the output control signals (e.g., in an encoded manner) to the implantable component 112 via external coil 108 and implantable coil 114. That is, the output control signals (stimulation signals) are received at the RF interface circuitry 140 via implantable coil 114 and provided to the stimulator unit 142. The stimulator unit 142 is configured to utilize the output control signals to generate electrical stimulation signals (e.g., current signals) for delivery to the recipient’s cochlea via one or more of the stimulating contacts (electrodes) 144. In this way, cochlear implant system 102 electrically stimulates the recipient’s auditory nerve cells, bypassing absent or defective hair cells that normally transduce acoustic vibrations into neural activity, in a manner that causes the recipient to perceive one or more components of the input audio signals (the received sound signals).

[0037] As detailed above, in the external hearing mode the cochlear implant 112 receives processed sound signals from the sound processing unit 106. However, in the invisible hearing mode, the cochlear implant 112 is configured to capture and process sound signals for use in electrically stimulating the recipient’s auditory nerve cells. In particular, as shown in FIG. ID, an example embodiment of the cochlear implant 112 can include a plurality of implantable sound sensors 165(1), 165(2) that collectively form a sensor array 160, and an implantable sound processing module 158. Similar to the external sound processing module 124, the implantable sound processing module 158 can comprise, for example, one or more processors and a memory device (memory) that includes sound processing logic. The memory device can comprise any one or more of: Non-Volatile Memory (NVM), Ferroelectric Random Access Memory (FRAM), read only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical,or other physical / tangible memory storage devices. The one or more processors are, for example, microprocessors or microcontrollers that execute instructions for the sound processing logic stored in memory device.

[0038] In the invisible hearing mode, the implantable sound sensors 165(1), 165(2) of the sensor array 160 are configured to detect / capture input sound signals 166 (e.g., acoustic sound signals, vibrations, etc.), which are provided to the implantable sound processing module 158. The implantable sound processing module 158 is configured to convert received input sound signals 166 (received at one or more of the implantable sound sensors 165(1), 165(2)) into output control signals 156 for use in stimulating the first ear of a recipient or user (i.e., the implantable sound processing module 158 is configured to perform sound processing operations). Stated differently, the one or more processors (e.g., processing element(s) implementing firmware, software, etc.) in implantable sound processing module 158 are configured to execute sound processing logic in memory to convert the received input sound signals 166 into output control signals 156 that are provided to the stimulator unit 142. The stimulator unit 142 is configured to utilize the output control signals 156 to generate electrical stimulation signals (e.g., current signals) for delivery to the recipient’s cochlea, thereby bypassing the absent or defective hair cells that normally transduce acoustic vibrations into neural activity.

[0039] It is to be appreciated that the above description of the so-called external hearing mode and the so-called invisible hearing mode are merely illustrative and that the cochlear implant system 102 could operate differently in different embodiments. For example, in one alternative implementation of the external hearing mode, the cochlear implant 112 could use signals captured by the sound input devices 118 and the implantable sound sensors 165(1), 165(2) of sensor array 160 in generating stimulation signals for delivery to the recipient.

[0040] According to the techniques of the present disclosure, external sound processing module 124 can also include an inertial measurement unit (IMU) 170. The inertial measurement unit 170 is configured to measure the inertia of the recipient's head, that is, motion of the recipient's head. As such, inertial measurement unit 170 comprises one or more sensors 175 each configured to sense one or more of rectilinear or rotatory motion in the same or different axes. Examples of sensors 175 that can be used as part of inertial measurement unit 170 include accelerometers, gyroscopes, inclinometers, compasses, and the like. Such sensors can be implemented in, for example, micro electromechanical systems (MEMS) or with other technology suitable for the particular application.

[0041] As also illustrated in FIG. ID, in certain examples, a second inertial measurement unit (IMU) 180 including one or more sensors 185 is incorporated into implantable sound processing module 158 of implant body 134. Second inertial measurement unit 180 can serve as an additional or alternative inertial measurement unit to inertial measurement unit 170 of external sound processing module 124. Like sensors 175, sensors 185 can each be configured to sense one or more of rectilinear or rotatory motion in the same or different axes. Examples of sensors 185 that can be used as part of inertial measurement unit 180 include accelerometers, gyroscopes, inclinometers, compasses, and the like. Such sensors can be implemented in, for example, micro electromechanical systems (MEMS) or with other technology suitable for the particular application. For hearing devices that include an implantable sound processing module, such as implantable sound processing module 158, that includes an IMU, such as IMU 180, the techniques presented herein can be implemented without an external processor.

[0042] FIG. IE is a block diagram illustrating one example arrangement for an external computing device 110 configured to perform one or more operations in accordance with certain embodiments presented herein. As shown in FIG. IE, in its most basic configuration, the external computing device 110 includes at least one processing unit 183 and a memory 184. The processing unit 183 includes one or more hardware or software processors (e.g., Central Processing Units) that can obtain and execute instructions. The processing unit 183 can communicate with and control the performance of other components of the external computing device 110. The memory 184 is one or more software or hardware-based computer-readable storage media operable to store information accessible by the processing unit 183. The memory 184 can store, among other things, instructions executable by the processing unit 183 to implement applications or cause performance of operations described herein, as well as other data. The memory 184 can be volatile memory (e.g., RAM), non-volatile memory (e.g., ROM), or combinations thereof. The memory 184 can include transitory memory or non-transitory memory. The memory 184 can also include one or more removable or non-removable storage devices. In examples, the memory 184 can include random access memory (RAM), read only memory (ROM), EEPROM (Electronically-Erasable Programmable Read-Only Memory), flash memory, optical disc storage, magnetic storage, solid state storage, or any other memory media usable to store information for later access. By way of example, and not limitation, the memory 184 can include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media or combinations thereof.In certain embodiments, the memory 184 comprises acclimatization trajectory logic 195 that, when executed, enables the processing unit 183 to perform aspects of the techniques presented.

[0043] In the illustrated example of FIG. IE, the external computing device 110 further includes a network adapter 186, one or more input devices 187, and one or more output devices 188. The external computing device 110 can include other components, such as a system bus, component interfaces, a graphics system, a power source (e.g., a battery), among other components. The network adapter 186 is a component of the external computing device 110 that provides network access (e.g., access to at least one network 189). The network adapter 186 can provide wired or wireless network access and can support one or more of a variety of communication technologies and protocols, such as ETHERNET, cellular, BLUETOOTH, near-field communication, and RF (Radiofrequency), among others. The network adapter 186 can include one or more antennas and associated components configured for wireless communication according to one or more wireless communication technologies and protocols. The one or more input devices 187 are devices over which the external computing device 110 receives input from a recipient. The one or more input devices 187 can include physically- actuatable user-interface elements (e.g., buttons, switches, or dials), a keypad, keyboard, mouse, touchscreen, and voice input devices, among other input devices that can accept user input. The one or more output devices 188 are devices by which the computing device 110 is able to provide output to a recipient. The output devices 188 can include a display 190 (e.g., a liquid crystal display (LCD)) and one or more speakers 191, among other output devices for presentation of visual or audible information to the recipient, a clinician, an audiologist, or other user.

[0044] It is to be appreciated that the arrangement for the external computing device 110 shown in FIG. IE is merely illustrative and that aspects of the techniques presented herein can be implemented at a number of different types of systems / devices including any combination of hardware, software, and / or firmware configured to perform the functions described herein. For example, the external computing device 110 can be a personal computer (e.g., a desktop or laptop computer), a hand-held device (e.g., a tablet computer), a mobile device (e.g., a smartphone), a surgical system, and / or any other electronic device having the capabilities to perform the associated operations described elsewhere herein.

[0045] Devices associated with (e.g., worn by or implanted in) a recipient / user can operate in accordance with different operational / control parameters which are initially set during a “fitting” session. Some example operational parameters of a cochlear implant include certainstimulation levels, such as the stimulation level in which stimulation signals are loud but comfortable (e.g., sometimes referred to as the Comfort (C) level), a so-called “M” level (e.g., upper limit of the dynamic stimulation range), a Threshold (T) level (e.g., a stimulation level in which stimulation signals are just audible), or other stimulation level. The difference between the threshold level and comfort level is sometimes referred as the “dynamic range” of the stimulation signals. An initial C level can be determined by sequentially increasing the stimulation current level for the recipient until the recipient reports a comfortably loud sound on a subjective loudness scale. The C level can be initially set to a lower level than the recipient can tolerate and incrementally increased overtime until a target C level is reached.

[0046] Increased wear time and wider dynamic ranges (i.e., differences between T and C levels) are correlated with better speech perception outcomes in both quiet and noise environments for recipients of devices. Whereas T levels for monopolar stimulation remain stable on average overtime, C levels can be increased on average approximately 30% (ranging 0-60%) over the first 6-12 months of device use. Increasing C levels too early and aggressively can cause loudness discomfort leading to device disuse or insufficient wear time. Failing to increase C levels from their initial setting can optimize wear time but will limit recipients from benefiting from increased dynamic ranges to maximize speech perception performance.

[0047] Current methods of scaling or increasing C levels rely on recipient effort in several ways. Devices are programmed with progressive maps (e.g., a default map Pl is a starter map, map P2 is intermediary map, and map P3 is largest dynamic range where a recipient should end up) . Master Volume, Bass and Treble (MVBT) controls allow recipients of hearing devices to manually adjust C levels by frequency by using a smartphone application. Recipients can manually adjust overall volume using controls on the sound processor. All these solutions require recipients / recipients to manually adjust their default setting in a systematic way to achieve the optimized result. However, many recipients never utilize any user controls and leave the settings at the default levels. In other methods currently being implemented, instead of relying on the recipient to adjust the levels, a recipient can receive adjustments at in-person clinic visits. At an in-person clinic visit, a clinician can adjust C levels in the default program through time-consuming and often biased psychometric testing. These current solutions have limited effectiveness and are a burden on both clinics and recipients.

[0048] Embodiments described herein provide for automated operational parameter adjustments, such as automated stimulation level changes, by using machine learning to calculate a personalized acclimatization trajectory based on recipient and device factors. Thepersonalized acclimatization trajectory can be implemented by applying automatic adjustments to operational parameters (e.g., stimulation levels) over time until a target parameter level is achieved. The acclimatization trajectory can be modified based on device usage, user input, biometrics, and / or automatically detected events..

[0049] Embodiments described herein perform scaling of C levels over time without user input to increase the dynamic range and maximize speech perception outcomes. The proposed system also has periodic checks (device usage, user input, biometrics, and / or automatically detected events) to verify comfortability of the dynamic range increases and adjust the trajectory as required. This solution reduces the need for clinic visits for minor programming adjustments, thereby reducing recipient and clinic burden, while automatically increasing the recipient’s dynamic range without relying on user input. Freeing up clinic time additionally allows professionals to potentially perform more cochlear implant implantations while maintaining excellence in care and delivering optimal recipient outcomes.

[0050] As noted above, embodiments described herein can be used to perform scaling of levels of other parameters in addition to C levels and T levels. For example, changes to the pulse width of the entire map and / or individual channels marked as out of compliance may be adjusted in isolation or in combination with T and C levels during the acclimatization period to provide an additional opportunity for scaling loudness. In addition, other parameters, such as maxima rate, as well as individual volume control set by the recipient may impact the acclimatization and may be adjusted according to an acclimatization trajectory.

[0051] FIG. 2 is a simplified diagram illustrating a machine learning model that is used to determine a personalized acclimatization trajectory for a recipient of a hearing device. As illustrated in FIG. 2, machine learning model 202 can receive recipient factors 204 and device factors 206 as inputs and can output information for determining a trajectory 208 for adjusting stimulation factors associated with a device. Machine learning model 202 can be located at an implantable portion of a medical device, an external portion of a medical device, or at an external device in communication with a medical device.

[0052] Recipient factors 204 can include one or more recipient factors or parameters that can affect an acclimatization trajectory for adjusting stimulation levels. The recipient factors 204 can include, for example, an etiology associated with a recipient of a hearing device. Individuals with otosclerosis can experience a 10-15% greater change in C levels over time compared to other etiologies, such as genetic, unknown, and Meniere’s disease. Similarly,recipients with meningitis can require increased stimulation over time compared to other etiologies. Therefore, an etiology associated with a recipient can affect the calculation of the acclimatization trajectory for the recipient.

[0053] The recipient factors 204 can additionally include information associated with the onset of hearing loss for a recipient. Individuals with prelingual hearing loss onset can experience an approximately 7% greater change in C levels over time compared to individuals with postlingual hearing loss.

[0054] The recipient factors 204 can additionally include a loudness tolerance associated with a recipient. Individual tolerance to loudness comfort can be assessed using such psychometric tests as the acceptable noise level test, loudness contour test, uncomfortable loudness level test, most comfortable loudness level test, or similar tests. These tests can be collected preoperatively in response to acoustic stimulation as a measure of individuals’ audiological and psychological tolerance to auditory loudness.

[0055] The recipient factors 204 can include stapedial reflex thresholds associated with a recipient. Stapedial reflex thresholds can be measured preoperatively via acoustic stimulation or postoperatively via electric stimulation. The stimulation level that elicits the stapedial muscle reflex response is a frequency-specific indicator of a recipient’s auditory system response to loud sounds and can be indicative of stimulation levels that can elicit loudness discomfort.

[0056] The recipient factors 204 can include a history of hearing aid use. Individuals with hearing aid experience prior to implantation of a hearing device, such as a cochlear implant, have a greater increase in C levels compared to those without routine preoperative hearing aid use. This is potentially due to auditory system of the recipients of hearing aids being habituated to sounds approaching their loudness comfort levels via amplification.

[0057] The recipient factors 204 can include whether the recipient has experienced sequential implantation. For sequential implantations, the acclimatization trajectory of the second implant can be based on the trajectory of the recipient’s first implant, as the peripheral auditory system would be analogous.

[0058] Other recipient factors 204 that can affect the acclimatization trajectory can include scalar location, sound coding strategy, preoperative speech scores in quiet and noise, age, duration of hearing loss, onset of hearing loss, sex, other demographic characteristics, and additional factors.

[0059] In addition to recipient factors 204, machine learning model 202 can calculate the information for determining the trajectory 208 based on device factors 206. Device factors 206 are associated with the medical device, such as a hearing device, whose levels are to be adjusted according to the calculated acclimatization trajectory. Device factors 206 can include information associated with an electrode array of the device. The electrode design (e.g., perimodiolar vs. lateral, with or without a positioner) and its final distance to the modiolus (which could be determined using computed tomography (CT) scans) can affect the minimum and maximum stimulation levels for a recipient of the device.

[0060] Device factors 206 can additionally include a simulation mode of the device. T and C levels for focused maps are, on average, higher and more variable across channels than those of monopolar maps. The low variability for monopolar maps can allow for the application of a single acclimatization trajectory across all channels, with lower final stimulation levels. In contrast, more focused maps (e.g., bipolar, tripolar, quadrupolar, focused multipolar, etc.) can require channel-specific acclimatization trajectories with overall higher final stimulation levels. In addition, T levels can increase over time with focused multipolar stimulation, whereas monopolar stimulation can result in stable T levels over time.

[0061] One or more of the recipient factors 204 and device factors 206 can be inputted to machine learning model 202 to determine a personalized acclimatization trajectory. Machine learning model 202 can calculate the personalized acclimatization trajectory for the recipient based on an existing recipient population dataset of hundreds, thousands, or more other recipients of hearing devices. For example, machine learning model 202 can determine a personalized acclimatization trajectory for a recipient based on recipient factors 204 and device factors 206 associated with the recipient and a device associated with the recipient as well as recipient factors, device factors, and acclimatization trajectories associated with other recipients of medical devices.

[0062] As illustrated in FIG. 2, machine learning model 202 may receive model updates 212 and may use the model updates 212 when determining the personalized acclimatization trajectory. Model updates 212 may be inputted to machine learning model 202, for example, when more normative data associated with the other recipients of hearing devices is received or when more classification outputs are defined. The model updates 212 may provide information that helps determine an acclimatization trajectory that is most appropriate for the recipient.

[0063] Machine learning model 202 can be a machine learning classification model, such as a deep neural network, to select a discrete trajectory from various pre-defined options. In addition or alternatively, a machine learning regression model, such as a multivariate regression, can be used to calculate a map trajectory based on the recipient factors 204 and device factors 206.

[0064] The output of the machine learning model 202 (classification or regression) determines the trajectory 208, which can be defined using at least five parameters: start and end times of the adaptation process (tstart and tend), the minimum and maximum stimulation level Lmax and Lmin , and stimulation level as a function of time ( / ( / )). The trajectory 208 can be channelspecific or applied to all stimulation channels.

[0065] FIG. 3 is a graph illustrating acclimatization traj ectories with different parameters . FIG. 3 illustrates three different acclimatization trajectories for modifying a C level over time. Trajectories 301, 302, and 303 are acclimatization trajectories of target C levels as a function of time . Traj ectory 304 is an acclimatization traj ectory illustrating a target T level as a function of time (Lc=f(t)). Trajectories 301, 302, and 303 have the same start point Lemin and the same start time tstart. Trajectories 301 and 302 have the same time to maximum (tend , but different stimulation level maxima. For example, the target C level Lcmax) associated with trajectory 301 is higher than the target C level associated with trajectory 302. Therefore, the shapes / slopes of trajectories 301 and 302 are different so that a higher C level is reached in trajectory 301 than in trajectory 302 over the same period of time. Trajectories 302 and 303 have the same stimulation level maximum, but different times to maximum. Trajectory 304 illustrates a constant increase in T level between a minimum T level Limin and a maximum T level Lrmax over a time period between tstart and tend.

[0066] As illustrated in FIG. 3, different trajectories can have different shapes and slopes based on the different minimum and maximum stimulation levels, start and end times of the adaption process, and type of stimulation level. The minimum and maximum stimulation levels and time to reach the maximum stimulation level can be determined based on the recipient factors and device factors described above. In addition, the acclimatization trajectories can be channelspecific (i.e., a different trajectory can be calculated for each channel) or applied to all stimulation channels. In some embodiments, a determination of whether an acclimatization trajectory is to be applied to all channels can be based on the recipient and / or device factors.

[0067] Based on the acclimatization trajectory parameters (stimulation level maximum and time to maximum), the stimulation levels (e.g., C levels, T levels, etc.) can be updated accordingly in discrete steps (e.g., X% increase every Y day(s)) until the stimulation maximum and time to maximum have been reached. The magnitude of each stepwise increase can be proportional to the stimulation level maximum and inversely proportional to the time to maximum and the frequency of step increases. The magnitude of each stepwise increase can be linear (e.g., for T levels) or logarithmic (e.g., for C levels to approximate the asymptotic stabilization of stimulation levels over time). Similarly, the frequency of stepwise increases can also vary over the acclimatization trajectory.

[0068] Throughout the course of device use and acclimatization, the trajectory can require modification to ensure any stimulation level increases do not exceed loudness discomfort. Modification to the acclimatization trajectory can involve decreasing or increasing the time to maximum and / or stimulation level maximum, applying an offset (positive or negative) to the stimulation level with or without modifying time to maximum and / or stimulation level maximum, or pausing and resuming stimulation level increases. Referring back to FIG. 2, machine learning model 202 may receive user feedback 210 from the recipient of the hearing device and may modify the trajectory 208 based on the user feedback.

[0069] The user feedback 210 can include information associated with device usage, user input, biometrics, and / or automatically detected events. Device usage can include, for example, wear time. Device usage is essential to acclimatizing to electrical stimulation. If the recipient is not wearing their device or if device usage is below a certain threshold (e.g., <10 hours per day), then the recipient may not have sufficient exposure to acclimate at the rate of the originally calculated trajectory. The recipient can receive a push notification inquiring as to why they are not wearing their devices long enough. If the recipient is not wearing their device due to loudness discomfort, programming can be adjusted by increasing the time to maximum, putting stimulation level increases on pause, slowing the rate of increase, decreasing the step size, and / or applying a negative offset to the current stimulation level. Device wear time exceeding the expectation of the trajectory can result in the converse potential modifications.

[0070] Device usage can also include overall sound pressure levels. To acclimatize the upper tolerable limits of stimulation, the recipient should be exposed to sounds that are near the limits (e.g., like exercising a muscle). If the recipient spends most of their time in low-level sound environments, then they may not have sufficient exposure to acclimate at the originallycalculated trajectory. In this case, the acclimatization trajectory time to maximum can be increased by putting stimulation level increases on pause, slowing the rate of increase, decreasing the step size, and / or applying a negative offset to current stimulation level.

[0071] User feedback 210 may include a user input. A user input can include an Ecological Momentary Assessment (EMA). User input regarding loudness comfort when wearing their device could be collected by performing an EMA. EMA information can be collected from recipients via push notifications displayed on a smart accessory, recorded by a button push, or recorded verbally. The results of the EMA can be linked to the sound pressure level (SPL) of the environment in which the response was elicited. If comfort is recorded in a relatively high SPL environment, then the recipient can be ready for another stimulation level increase. If discomfort is recorded in an environment with average SPL, then the trajectory can be delayed (e.g., stimulation level increases on pause, slowing the rate of increase, decreasing the step size, and / or applying a negative offset to current stimulation level).

[0072] The user input can additionally include volume adjustments. Manual adjustments to overall volume or master volume bass treble are an explicit indicator from the recipient about desired volume level. The volume adjustment can be linked to the SPL of the environment in which the adjustment was made. If the volume is increased in a relatively high SPL environment, then the recipient can be ready for another stimulation level increase. If the recipient is near the end of their acclimatization trajectory, then the stimulation level maximum can be increased. If recipients are at the end of their trajectories and decreasing volume in average to high SPL environments, then the trajectory maximums can be decreased.

[0073] User feedback 210 may additional include biometrics. Biometrics can include, for example, a change in impedance. Impedance metrics often decrease in the first months following implantation corresponding to increases in psychometrically measured stimulation levels. When decreased impedance is measured, then stimulation levels can be increased by a step, the size of the next step can be greater in magnitude, and / or the time to maximum can be reduced.

[0074] Biometrics can additionally include a change in electrically evoked stapedial reflex threshold. Electrically evoked stapedial reflex thresholds (eSRTs) are the electrical stimulation levels at which the stapedial muscle reflex is elicited. eSRTs are a reliable estimate of stimulation levels and avoid the potential biases of psychometric testing from the tester (i.e., clinician) or recipient. In response to an increase in frequency-specific eSRT, the trajectorymaximum can be increased, the time to maximum can be decreased, the magnitude of the next stepwise increase for the frequency-relevant electrodes can be increased. If the current stimulation level is equal to or greater than the eSRT, then the trajectory can be delayed (e.g., stimulation level increases on pause, slowing the rate of increase, decreasing the step size, and / or applying a negative offset to current stimulation level). If the recipient is near the end of the trajectory, then the stimulation level maximum can be decreased so as not to exceed the eSRT.

[0075] User feedback 210 may additionally include detected events. Detected events can include, for example, sound processor removal. If the sound processor is abruptly removed in a high SPL environment, particularly environments involving speech, then that can indicate that the recipient removed the hearing device due to loudness discomfort. If one or multiple of these events are detected, then the system can decrease the trajectory sound level maximum, increase the time to maximum, pause the next step increase, and / or apply a negative offset to the current stimulation level.

[0076] Detected events can additionally include a startle response. A sudden or jerking reaction to a loud sound in the environment can be indicative of loudness discomfort. This reaction can be detected by an inertial measurement unit motion sensor, electromyography sensor, electrooculography sensor (e.g., eye blink), and / or microphone (e.g., wincing sound). If one or more of these events are detected, then the trajectory sound level maximum can be decreased, the time to maximum can be increased, the next step increase can be paused, and / or a negative offset can be applied to the current stimulation level.

[0077] Modifications to the calculated trajectory based on user feedback 210 can cause an updated trajectory to “branch” from the original trajectory at the timepoint when the modification occurs. The updated trajectory supersedes the original trajectory and stimulation level increases will be based on the updated trajectory after the timepoint when the modification occurs. FIGs. 4A-4D illustrate examples of trajectories and trajectories that are modified based on user feedback 210 (e.g., device usage, user input, biometrics, and / or automatically detected events).

[0078] FIG. 4A shows an initial trajectory 401, a trajectory 402 modified to decrease the time to maximum (e.g., in response to a recipient exceeding device wear time, etc.), and a trajectory 403 modified to increase the time to maximum (e.g., in response to device usage being below a threshold, removal of the sound processor, detection of a startle response, etc.).

[0079] FIG. 4B shows the initial trajectory 401, a trajectory 404 modified to increase the stimulation level maximum (e.g., in response to a manual volume increase in a high SPL environment, etc.) and a trajectory 405 modified to decrease the stimulation level maximum (e.g., in response to a change in an electrically evoked stapedial reflex threshold, etc.).

[0080] FIG. 4C shows the initial trajectory 401, a modified trajectory 406 with a positive offset applied to the current stimulation level and stimulation level maximum (e.g., in response to a recipient exceeding device wear time, etc.), and a modified trajectory 407 with a negative offset applied to the current level and stimulation level maximum (e.g., in response to a recipient spending most fime in low-level sound environments, a discomfort recorded in an EMA, etc ).

[0081] FIG. 4D shows the initial trajectory 401 and a modified trajectory 408 with a pause on stimulation level increases and a subsequent resumption (e.g., in response to a change in an electrically evoked stapedial reflex threshold, detection of a sound processor removal, detection of a startle response, etc.).

[0082] Recipients can be able to view their progress on the acclimatization trajectory, such as through a datalogging feature on an application on a remote device (e.g., a smartphone, tablet, etc.). Through the display, a recipient can receive messages reinforcing the importance of full-time device usage. The messages can include information displayed numerically (e.g., average hours per day) or categorically (e.g., >10 hours = “acceptable”; <10 hours = “insufficient”). Recipients can be further motivated to meet wear time goals through gamification (e.g., in-app currency for maintaining a streak of acceptable wear time). Information can also be made viewable to a partner or caretaker to engage the recipient’ s social network and receive external encouragement for consistent device use, which leads to optimal speech perception outcomes.

[0083] Information on a recipient’s progress on their acclimatization trajectory can additionally be relayed to their hearing healthcare professional (i.e., audiologist) at a remote location. The professional can use a computing device to see and interact with the information in real time or retrospectively. The professional can be able to provide guidance and suggestions to the recipient or make recommendations about modifications to the trajectory.

[0084] FIG. 5 is a flowchart illustrating an exemplary method for providing automated stimulation level changes. At 510, a trajectory for adjusting one or more stimulation levels associated with a hearing device is calculated based on information associated with the hearingdevice and information associated with a recipient of the hearing device. For example, a machine learning model can calculate the trajectory or information used to determine the trajectory based on the information associated with hearing device and information associated with the recipient of the hearing device. The machine learning model can determine the trajectory based on an existing recipient population dataset.

[0085] At 520, the one or more stimulation levels can be automatically adjusted based on the trajectory until one or more target stimulation levels are reached. For example, the stimulation levels can be incrementally increased over time based on the calculated trajectory until a maximum stimulation level is reached. In some embodiments, the trajectory can be modified based on device usage, user input, biometrics, and / or automatically detected events.

[0086] As previously described, the technology disclosed herein can be applied in any of a variety of circumstances and with a variety of different devices. Example devices that can benefit from technology disclosed herein are described in more detail in FIGS. 6 and 7. The techniques of the present disclosure can be applied to other devices, such as neurostimulators, cardiac pacemakers, cardiac defibrillators, sleep apnea management stimulators, seizure therapy stimulators, tinnitus management stimulators, and vestibular stimulation devices, as well as other medical devices that deliver stimulation to tissue. Further, technology described herein can also be applied to consumer devices. These different systems and devices can benefit from the technology described herein.

[0087] FIG. 6 illustrates an example vestibular stimulator system 602, with which embodiments presented herein can be implemented. As shown, the vestibular stimulator system 602 comprises an implantable component (vestibular stimulator) 612 and an external device / component 604 (e.g., external processing device, battery charger, remote control, etc.). The external device 604 comprises a transceiver unit 660. As such, the external device 604 is configured to transfer data (and potentially power) to the vestibular stimulator 612,

[0088] The vestibular stimulator 612 comprises an implant body (main module) 634, a lead region 636, and a stimulating assembly 616, all configured to be implanted under the skin / tissue (tissue) 615 of the recipient. The implant body 634 generally comprises a hermetically-sealed housing 638 in which RF interface circuitry, one or more rechargeable batteries, one or more processors, and a stimulator unit are disposed. The implant body 134 also includes an intemal / implantable coil 614 that is generally external to the housing 638, but which is connected to the transceiver via a hermetic feedthrough (not shown).

[0089] The stimulating assembly 616 comprises a plurality of electrodes 644(l)-(3) disposed in a carrier member (e.g., a flexible silicone body). In this specific example, the stimulating assembly 616 comprises three (3) stimulation electrodes, referred to as stimulation electrodes 644(1), 644(2), and 644(3). The stimulation electrodes 644(1), 644(2), and 644(3) function as an electrical interface for delivery of electrical stimulation signals to the recipient’s vestibular system.

[0090] The stimulating assembly 616 is configured such that a surgeon can implant the stimulating assembly adjacent the recipient’s otolith organs via, for example, the recipient’s oval window. It is to be appreciated that this specific embodiment with three stimulation electrodes is merely illustrative and that the techniques presented herein can be used with stimulating assemblies having different numbers of stimulation electrodes, stimulating assemblies having different lengths, etc.

[0091] In operation, the vestibular stimulator 612, the external device 604, and / or another external device, can be configured to implement the techniques presented herein. That is, the vestibular stimulator 612, possibly in combination with the external device 604 and / or another external device, can include an evoked biological response analysis system, as described elsewhere herein.

[0092] FIG. 7 illustrates a retinal prosthesis system 701 that comprises an external device 710 (which can correspond to the wearable device) configured to communicate with an implantable retinal prosthesis 700 via signals 751. The retinal prosthesis 700 comprises an implanted processing module 725 and a retinal prosthesis sensor-stimulator 790 is positioned proximate the retina of a recipient. The external device 710 and the processing module 725 can communicate via coils 708, 714.

[0093] In an example, sensory inputs (e.g., photons entering the eye) are absorbed by a microelectronic array of the sensor-stimulator 790 that is hybridized to a glass piece 792 including, for example, an embedded array of microwires. The glass can have a curved surface that conforms to the inner radius of the retina. The sensor-stimulator 790 can include a microelectronic imaging device that can be made of thin silicon containing integrated circuitry that convert the incident photons to an electronic charge.

[0094] The processing module 725 includes an image processor 723 that is in signal communication with the sensor-stimulator 790 via, for example, a lead 788 which extends through surgical incision 789 formed in the eye wall. In other examples, processing module725 is in wireless communication with the sensor-stimulator 790. The image processor 723 processes the input into the sensor-stimulator 790, and provides control signals back to the sensor-stimulator 790 so the device can provide an output to the optic nerve. That said, in an alternate example, the processing is executed by a component proximate to, or integrated with, the sensor-stimulator 790. The electric charge resulting from the conversion of the incident photons is converted to a proportional amount of electronic current which is input to a nearby retinal cell layer. The cells fire and a signal is sent to the optic nerve, thus inducing a sight perception.

[0095] The processing module 725 can be implanted in the recipient and function by communicating with the external device 710, such as a behind-the-ear unit, a pair of eyeglasses, etc. The external device 710 can include an external light / image capture device (e.g., located in / on a behind-the-ear device or a pair of glasses, etc.), while, as noted above, in some examples, the sensor-stimulator 790 captures light / images, which sensor-stimulator is implanted in the recipient.

[0096] As should be appreciated, while particular uses of the technology have been illustrated and discussed above, the disclosed technology can be used with a variety of devices in accordance with many examples of the technology. The above discussion is not meant to suggest that the disclosed technology is only suitable for implementation within systems akin to that illustrated in the figures. In general, additional configurations can be used to practice the processes and systems herein and / or some aspects described can be excluded without departing from the processes and systems disclosed herein.

[0097] This disclosure described some aspects of the present technology with reference to the accompanying drawings, in which only some of the possible aspects were shown. Other aspects can, however, be embodied in many different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects were provided so that this disclosure was thorough and complete and fully conveyed the scope of the possible aspects to those skilled in the art.

[0098] As should be appreciated, the various aspects (e.g., portions, components, etc.) described with respect to the figures herein are not intended to limit the systems and processes to the particular aspects described. Accordingly, additional configurations can be used to practice the methods and systems herein and / or some aspects described can be excluded without departing from the methods and systems disclosed herein.

[0099] According to certain aspects, systems and non-transitory computer readable storage media are provided. The systems are configured with hardware configured to execute operations analogous to the methods of the present disclosure. The one or more non-transitory computer readable storage media comprise instructions that, when executed by one or more processors, cause the one or more processors to execute operations analogous to the methods of the present disclosure.[ooioo] Similarly, where steps of a process are disclosed, those steps are described for purposes of illustrating the present methods and systems and are not intended to limit the disclosure to a particular sequence of steps. For example, the steps can be performed in differing order, two or more steps can be performed concurrently, additional steps can be performed, and disclosed steps can be excluded without departing from the present disclosure. Further, the disclosed processes can be repeated.[ooioi] Although specific aspects were described herein, the scope of the technology is not limited to those specific aspects. One skilled in the art will recognize other aspects or improvements that are within the scope of the present technology. Therefore, the specific structure, acts, or media are disclosed only as illustrative aspects. The scope of the technology is defined by the following claims and any equivalents therein.

[0102] It is also to be appreciated that the embodiments presented herein are not mutually exclusive and that the various embodiments can be combined with another in any of a number of different manners.

Claims

CLAIMSWhat is claimed is:

1. A method comprising : calculating a trajectory for adjusting one or more stimulation levels associated with a hearing device based on information associated with the hearing device and information associated with a recipient of the hearing device; and automatically adjusting the one or more stimulation levels based on the trajectory until one or more target stimulation levels are reached.

2. The method of claim 1, wherein the one or more stimulation levels include threshold levels.

3. The method of claim 1, wherein the one or more stimulation levels include loudness comfort levels.

4. The method of claim 1, wherein calculating the trajectory includes calculating the trajectory using a machine learning model.

5. The method of claim 4, wherein calculating the trajectory using the machine learning model includes determining a personalized acclimatization trajectory for the recipient based on data associated with a plurality of other recipients of hearing devices.

6. The method of claim 1, 2, 3, 4, or 5, further comprising: detecting a modification event associated with the hearing device or the recipient of the hearing device; and modifying the trajectory based on detecting the modification event.

7. The method of claim 6, wherein the modification event is based on device usage, user input, biometrics, or an automatically detected event.

8. The method of claim 1, 2, 3, 4, or 5, wherein the trajectory is calculated based on a start time, an end time, a minimum level for the one or more stimulation levels, the one or more target stimulation levels, and the one or more stimulation levels as a function of time.

9. The method of claim 1, 2, 3, 4, or 5, wherein calculating the trajectory includes calculating a first trajectory for adjusting a first stimulation level associated with the hearing device and calculating a second trajectory for adjusting a second stimulation level associated with the hearing device.

10. The method of claim 1, 2, 3, 4, or 5, wherein the information associated with the hearing device includes data associated an electrode array or a stimulation mode associated with the hearing device.

11. The method of claim 1, 2, 3, 4, or 5, wherein the information associated with the recipient includes an etiology, an age of onset of hearing loss, a loudness tolerance, stapedial reflex thresholds, history of hearing aid use, or sequential implantation information associated with the recipient.

12. A medical device comprising: a trajectory calculation module configured to calculate a trajectory for adjusting a parameter associated with the medical device based on information associated with the medical device and information associated with a recipient of the medical device; and one or more processors for adjusting the parameter based on the trajectory until a target parameter is reached.

13. The medical device of claim 12, wherein the parameter is a stimulation level.

14. The medical device of claim 12 or 13, wherein the trajectory calculation module is a machine learning model.

15. The medical device of claim 14, wherein the machine learning model is a machine learning classification model.

16. The medical device of claim 14, wherein the machine learning model is a machine learning regression model.

17. The medical device of claim 12 or 13, wherein the information associated with the medical device includes information associated with an electrode array of the medical device or information associated with a stimulation mode of the medical device.

18. The medical device of claim 12 or 13, wherein the information associated with the recipient of the medical device includes an etiology, an age of onset of hearing loss, a loudness tolerance, stapedial reflex thresholds, history of hearing aid use, or sequential implantation information associated with the recipient.

19. The medical device of claim 12 or 13, wherein the medical device is a hearing device.

20. The medical device of claim 12 or 13, wherein the parameter is a threshold level associated with the medical device.

21. The medical device of claim 12 or 13, wherein the parameter is a loudness comfort level.

22. The medical device of claim 12 or 13, wherein the trajectory is a graph of the parameter as a function of time.

23. The medical device of claim 12 or 13, wherein the trajectory includes a start time, an end time to reach the target parameter, a minimum level for the parameter, and a level for the target parameter.

24. The medical device of claim 12 or 13, wherein the trajectory calculation module is configured to modify the trajectory based on a modification input.

25. The medical device of claim 24, wherein the modification input is based on information associated with usage of the medical device, input received from the recipient of the medical device, biometrics associated with medical device, and / or a detected event associated with the medical device or the recipient of the medical device.

26. The medical device of claim 24, wherein the trajectory calculation module is configured to modify the trajectory by increasing or decreasing a time to reach the target parameter.

27. The medical device of claim 24, wherein the trajectory calculation module is configured to modify the trajectory by increasing or decreasing a level associated with the target parameter.

28. The medical device of claim 24, wherein the trajectory calculation module is configured to modify the trajectory by applying an offset to a level of the parameter at a time when the trajectory is modified.

29. The medical device of claim 24, wherein the trajectory calculation module is configured to modify the trajectory by pausing an increase in a level of the parameter.

30. One or more non-transitory computer readable storage media comprising instructions that, when executed by a processor, cause the processor to: receive a trajectory for increasing levels of a parameter associated with a device over time, wherein the trajectory is calculated based on information associated with the device and information associated with a recipient of the device; and increase the levels of the parameter based on the trajectory until a target level of the parameter is reached.

31. The one or more non-transitory computer readable storage media of claim 30, wherein the device is a hearing device.

32. The one or more non-transitory computer readable storage media of claim 30, wherein the instructions further cause the processor to modify the trajectory based on one or more factors associated with the device or the recipient of the device.

33. The one or more non-transitory computer readable storage media of claim 32, wherein the one or more factors include information associated with usage of the device, an input received from the recipient of the device, biometrics associated with device, and / or a detected event associated with the device or the recipient of the device.

34. The one or more non-transitory computer readable storage media of claim 30, 31, 32, or 33, wherein the trajectory is calculated using a machine learning model.

35. The one or more non-transitory computer readable storage media of claim 34, wherein the trajectory is calculated based on information associated with other devices and information associated with other recipients of the other devices.

36. A system, comprising:a medical device; and a computing device in communication with the medical device, the computing device comprising one or more processors configured to: obtain information associated with the medical device; obtain information associated with a recipient of the medical device; and calculate a trajectory for adjusting one or more stimulation levels associated with the medical device based on the information associated with the medical device and the information associated with a recipient of the medical device.

37. The system of claim 36, wherein the one or more stimulation levels include threshold levels.

38. The system of claim 36, wherein the one or more stimulation levels include loudness comfort levels.

39. The system of claim 36, wherein calculating the trajectory includes calculating the trajectory using a machine learning model.

40. The system of claim 39, wherein calculating the trajectory using the machine learning model includes determining a personalized acclimatization trajectory for the recipient based on data associated with a plurality of other recipients of medical devices.

41. The system of claim 36, 37, 38, 39, or 40, wherein the trajectory is calculated based on a start time, an end time, a minimum level for the one or more stimulation levels, the one or more target stimulation levels, and the one or more stimulation levels as a function of time.

42. The system of claim 36, 37, 38, 39, or 40, wherein calculating the trajectory includes calculating a first trajectory for adjusting a first stimulation level associated with the medical device and calculating a second trajectory for adjusting a second stimulation level associated with the medical device.

43. The system of claim 36, 37, 38, 39, or 40, wherein the information associated with the medical device includes data associated an electrode array or a stimulation mode associated with the medical device.

44. The system of claim 36, 37, 38, 39, or 40, wherein the information associated with the recipient includes an etiology, an age of onset of hearing loss, a loudness tolerance, stapedial reflex thresholds, history of hearing aid use, or sequential implantation information associated with the recipient.

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